Multiobjective Optimization Research Papers - Academia.edu (original) (raw)

2025

Mon but aujourd'hui est de démontrer des résultats dans l'optimisation des fonctions de deux variables entières qui correspondent aux résultats fondamentaux de l'analyse convexe des variables réelles, à savoir qu'un minimum local d'une fonction convexe est global ; que la fonction marginale d'une fonction convexe est convexe ; et que deux ensembles convexes disjoints peuvent être séparés par un hyperplan. À cette fin j'introduirai une nouvelle classe de fonctions de deux variables discrètes que j'appellerai fonctions fortement convexes.

2025, Przeglad Elektrotechniczny

In the past few years Genetic Algorithms (GAs) have proved to be a very powerful and reliable optimisation tool. They have also been successfully applied in the design optimisation of many electromagnetic devices. Therefore, in this paper the optimal design performed on the single phase permanent magnet brushless DC motor (SPBLDCM) is done by using a Genetic Algorithm. The objective function of the optimisation search is selected to be the efficiency of the motor and the design process is defined as a maximisation problem. Based on the values of some specific motor parameters, a comparative analysis of the improved motor and the initial motor design is performed. As an addition to the comparative analysis, a Finite Element Method modelling and analysis of both models is also performed. Streszczenie. W ostatnich kilku latach algorytmy genetyczne staly się jednym z poważniejszych narzędzi w rozwiązywaniu problemów optymalizacji. Dotyczy to również optymalizacji licznych urządzeń elektromagnetycznych. W tym artykule pokazano zastosowanie algorytmu genetycznego w optymalizacji silnika jednofazowego z magnesem trwały w wykonaniu bezszczotkowym. Funkcją celu w procesie optymalnego projektowania jest efektywność silnika jako wartość maksymalna. Biorąc pod uwagę kilka specyficznych parametrów silnika przeprowadzono analizę porównawczą modeli optymalnego i początkowego, a także dla obu modeli przeprowadzono analizę pracy silnika metodą elementów skończonych. (Nowy projekt jednofazowego silnika bezszczotkowego prądu stałego o maksymalnej wydajności)

2025, IEEE Transactions on Magnetics

In this paper, a hybrid technique for global based on the genetic algorithm and a determinktic method is presented. A potential advantage of the hybrid method compared to the genetic algorithm is that global o p t h b t i ( ~1 can be performed more efficiently. An intrinsic pmb1em of the hybrid tmKques is rehted to the moment of stopping the stochastic routin; to launch the one. This is investigated using some natural criteria for the commutation between the two methods. The results show that it is possible to gain in efficiency and in accuracy but the criterion is usually problem dependent. Finally, to show the solution of a real problem, the hybrid algoritlann is coupled to a 2D cade based on the boundary element a conneetor of 145 kV GIs.

2025, IEEE Transactions on Magnetics

This work presents a multiobjective genetic algorithm with a novel feature, the real biased crossover operator. This operator takes into account the function values of the two parents, defining a nonuniform probability for the new individuals' locations that biases them toward the best parents' locations. The procedure leads to better estimates of the Pareto set. The proposed algorithm is applied to the optimization of a Yagi-Uda antenna in a wide frequency range with several simultaneous performance specifications, providing antenna geometries with good performance, compared to those presented in the available literature.

2025, IEEE Transactions on Magnetics

The TEAM benchmark problem 22 is an important optimization problem in electromagnetic design, which can be formulated as a constrained mono-objective problem or a multiobjective one with two objectives. In this paper, we propose a multiobjective version with three objectives, whose third objective is related to the quench constraint and the better use of the superconducting material. The formulation proposed yields results that provide new alternatives to the designer. We solved the formulation proposed using the multiobjective clonal selection algorithm. After that, we selected a particular solution using a simple decision making procedure.

2025, PLOS ONE

Optimal operation of water resources in multiple and multipurpose reservoirs is very complicated. This is because of the number of dams, each dam's location (Series and parallel), conflict in objectives and the stochastic nature of the inflow of water in the system. In this paper, performance optimization of the system of Karun and Dez reservoir dams have been studied and investigated with the purposes of hydroelectric energy generation and providing water demand in 6 dams. On the Karun River, 5 dams have been built in the series arrangements, and the Dez dam has been built parallel to those 5 dams. One of the main achievements in this research is the implementation of the structure of production of hydroelectric energy as a function of matrix in MATLAB software. The results show that the role of objective function structure for generating hydroelectric energy in weighting method algorithm is more important than water supply. Nonetheless by implementing εconstraint method algorithm, we can both increase hydroelectric power generation and supply around 85% of agricultural and industrial demands.

2025

Ficha catalográfica: Biblioteca Profº Mário Werneck, Escola de Engenharia da UFMG "O assunto mais importante do mundo pode ser simplificado até ao ponto em que todos possam apreciá-lo e compreendê-lo. Isso é -ou deveria ser -a mais... more

2025

Em primeiro lugar agradeço a Deus, quem guiou os meus passos desde a minha infância, quando ainda nem pensava em me formar em engenharia elétrica, e muito menos, em defender um título de mestre numa universidade tão conceituada quanto a UFMG. Agradeço sinceramente pelas inúmeras portas que foram abertas, e também pelas oportunidades que certamente virão. Agradeço ao meu pai João Batista, quem sempre me proporcionou muitas alegrias, além de me mostrar as grandes virtudes da paciência e mansidão, e à minha mãe Maria Clarisberte, quem há muito vem me ensinando a lutar pelos sonhos tão almejados. Estas duas vidas são os principais responsáveis pelo meu caráter, e sei que mesmo diante de alguns desentendimentos, eles sempre torceram por mim. Sou grato também aos meus familiares pelo apoio e carinho, principalmente à minha tia Airam e às minhas irmãs Paula e Arielly, as quais sempre me ajudaram e animaram. Agradeço especialmente aos meus tios Jaci e Solange, os quais nunca me desampararam e sempre se mostraram grandes amigos. Agradeço ao meu orientador Jaime A. Ramírez, quem direcionou meus primeiros passos no campo da otimização evolucionária. Além de ter se mostrado um amigo, representa um dos maiores responsáveis pela concretização deste trabalho. Sou grato ainda aos grandes professores que ajudaram na minha formação, principalmente Oriane Magela,

2025

Multiobjective evolutionary algorithms based on decomposition (MOEA/Ds) represent a class of widely employed problem solvers for multicriteria optimization problems. In this work we investigate the adaptation of these methods for incorporating preference information prior to the optimization, so that the search process can be biased towards a Pareto-optimal region that better satisfies the aspirations of a decision-making entity. The incorporation of the Preference-based Adaptive Region-of-interest (PAR) framework into the MOEA/D requires only the modification of the reference points used within the scalarization function, which in principle allows a straightforward use in more sophisticated versions of the base algorithm. Experimental results using the UF benchmark set suggest gains in diversity within the region of interest, without significant losses in convergence. Recent works have called this a posteriori approach into question, pointing out that in many cases one is not interested in actually mapping out the full Pareto front in detail, which can lead to wasteful optimization approaches that spend most of their computational budgets refining solutions that are clearly of no interest to the end user. 1 Throughout this text we use the term "MOEA/Ds" to refer to the class of algorithms based on decomposition for multiobjective optimization, i.e., the original MOEA/D and its many variants found in the literature.

2025

Logistic delivery through road contributes substantial carbon emission (CE). In business, timely goods delivery i.e. customer satisfaction, is important. With these facts, a sustainable multi-objective 3D delivery problem with customer satisfaction (SMO3DDPwCS) in a hilly region (HR) is developed to minimize total CE and customer dissatisfaction (CDS) simultaneously. Here, one supplier's vehicle starts from the depot with goods equal to retailers' demands, distributes among the retailers as per their orders within their preferred times, and comes back. The retailers' shops and depot are connected through multiple hilly tracks, which have up and down slopes and are susceptible to landslide. The cautious driving through these tracks produces extra CE and CDS. The SMO3DDPwCS is solved by a modified MOTLBO (mMOTLBO) algorithm. This algorithm incorporates self-learning concepts after both the teaching and learning phases, introduces innovative upgrading strategies, and employs a group-based learning approach. Some statistical tests are performed using mMOTLBO on the standard TSPLIB instances. The efficiency of mMOTLBO is established against NSGA-II and MOEA/D. Multiple solutions in Pareto front are ranked using TOPSIS. Some managerial decisions are drawn. The optimum routing plan for SMO3DDPwCS in a hilly region is presented and gives better results (31% total CE and 8% total CDS) than the single path formulation. mMOTLBO showed superiority over other algorithms in most cases concerning the Pareto front for the objectives. On the benchmark instances, mMOTLBO demonstrated its superiority by outperforming NSGA-II and MOEA/D, showing improvements of 0.11 in IGD and 4.12 in GD.

2025, Jurnal Sistem dan Manajemen Industri

The turning process involves the linear removal of material from the workpiece and requires a relatively high amount of energy. The high energy consumption of the machining process increases carbon emissions, which affects the environment. Moreover, production costs will rise as the cost of energy rises. Energy savings during the machining process are crucial for achieving sustainable manufacturing. In order to determine and optimize the cutting parameters, this study creates a multi-pass turning processes optimization model. It considers cutting speeds, feed rates, and depth of cut. In this study, the model uses multi-objective optimization by incorporating three objective functions: processing time, energy consumption and production costs. OptQuest completed the proposed model in Oracle Crystal Ball software, then normalized and weighted the sum. Ordering preferences, the Multi-Objective Optimization based on Ratio Analysis (MOORA) approach is utilized. It ranks items based on their higher priority values. This paper provides a numerical example to demonstrate the application of an optimization model. Based on the preference order ranking results, the optimal values for three objective functions are as follows: total processing time of 4.953 min, the total energy consumption of 5.434 MJ, and total production cost of 395.21$.

2025, Applied Mathematics and Computation

It is assumed that in an n-firm single-product oligopoly without product differentiation the firms face an uncertain price function, which is considered random by the firms. At each time period each firm simultaneously maximizes its expected profit and minimizes the variance of the profit since it wants to receive as high as possible profit with the least possible uncertainty. It is assumed that the best response of each firm is obtained by the weighting method. We show the existence of a unique equilibrium, and investigate the local stability of the equilibrium. Es asumido que en un oligopolio de n-firmas "single-product" sin diferenciación producto firmas con función de precio variable, son consideradas randon por las firmas.

2025, Scientific Reports

Improving thermal comfort often impacts daylight, creating trade-offs that remain underexplored, particularly in tropical dwellings. Overheating metrics-essential for assessing thermal conditions in warm regions-are entirely absent from daylight performance analysis. Response Surface Methodology (RSM) and desirability functions were employed to optimize the thermal and daylight performance of a typical low-rise tropical housing typology. Specifically, this approach simultaneously optimized Indoor Overheating Hours (IOH) and Useful Daylight Illuminance (UDI) metrics through an Overall Desirability (D). Each response required only 138 simulation runs (~ 30 h: 276 runs) to determine optimal values for passive strategies: window-to-wall ratio (WWR) and roof overhang depth across four orientations (eight factors). Initial screening based on 2 8-2 V fractional factorial design, identified four key factors using stepwise and Lasso regression, narrowed down to three: roof overhang depth on the south and west, WWR on the west, and WWR on the south. Then, RSM optimization yielded an optimal solution (west/south roof overhang: 3.78 m, west WWR: 3.76%, south WWR: 29.3%) with a D of 0.625 (IOH: 8.33%, UDI: 79.67%). Finally, robustness analysis with 1,000 bootstrap replications provided 95% confidence intervals for the optimal values. This study balances thermal comfort and daylight with few experiments using a computationally-efficient multiobjective approach.

2025, Applied Microbiology and Biotechnology

A multiobjective optimization was performed to maximize native protein concentration and shelf life of ASD, using artificial neural network (ANN) and genetic algorithm (GA). Optimum pH, storage temperature, concentration of protein, and protein stabilizers (Glycerol, NaCl) were determined satisfying the twin objective: maximum relative area of the dimer peak (native state) after 48 h of storage, and maximum shelf life. The relative area of the dimer peak, obtained from size exclusion chromatography performed as per the central composite design (CCD), and shelf life (obtained as turbidity change) served as training targets for the ANN. The ANN was used to establish mathematical relationship between the inputs and targets (from CCD). GA was then used to optimize the above determinants of aggregation, maximizing the twin objectives of the network. An almost fourfold increase in shelf life (~196 h) was observed at the GA-predicted optimum (protein concentration: 6.49 mg/ml, storage temperature: 20.8 °C, Glycerol: 10.02%, NaCl: 51.65 mM and pH: 8.2). Since no aggregation was observed at the optimum till 48 h, all the protein was found at the dimer position with maximum relative area (64.49). Predictions of the finally adapted network also reveal that storage temperature and solvent glycerol concentration plays key role in deciding the degree of ASD aggregation. This multiobjective optimization strategy was also successfully applied in minimizing the batch culture period and determining optimum combination of medium components required for most economical production of actinomycin D.

2025, Ferdowsi University of Mashhad

Particle swarm optimization (PSO) is a widely recognized bio-inspired algorithm for systematically exploring solution spaces and iteratively identifying optimal points. Through updating local and global best solutions, PSO effectively explores the search process, enabling the discovery of the most advantageous outcomes. This study proposes a novel Smith chartbased particle swarm optimization to solve convex and nonconvex multiobjective engineering problems by representing complex plane values in *Corresponding author

2025, Annals of Nuclear Energy

We have developed a system to design optimized boiling water reactor fuel reloads. This system is based on the Tabu Search technique along with the heuristic rules of Control Cell Core and Low Leakage. These heuristic rules are a common practice in fuel management to maximize fuel assembly utilization and minimize core vessel damage, respectively. The system uses the 3-D simulator code CM-PRESTO and it has as objective function to maximize the cycle length while satisfying the operational thermal limits and cold shutdown constraints. In the system tabu search ideas such as random dynamic tabu tenure, and frequency-based memory are used. To test this system an optimized boiling water reactor cycle was designed and compared against an actual operating cycle. Numerical experiments show an improved energy cycle compared with the loading patterns generated by engineer expertise and genetic algorithms.

2025, Annals of Nuclear Energy

An optimization system to get control rod patterns (CRP) has been generated. This system is based on the tabu search technique (TS) and the control cell core heuristic rules. The system uses the 3-D simulator code CM-PRESTO and it has as objective function to get a specific axial power profile while satisfying the operational and safety thermal limits. The CRP design system is tested on a fixed fuel loading pattern (LP) to yield a feasible CRP that removes the thermal margin and satisfies the power constraints. Its performance in facilitating a power operation for two different axial power profiles is also demonstrated. Our CRP system is combined with a previous LP optimization system also based on the TS to solve the combined LP-CRP optimization problem. Effectiveness of the combined system is shown, by analyzing an actual BWR operating cycle. The results presented clearly indicate the successful implementation of the combined LP-CRP system and it demonstrates its optimization features.

2025

There are many applications in aeronautics where there exist strong couplings between disciplines. One practical example is within the context of Unmanned Aerial Vehicle (UAV) automation where there exists strong coupling between operation constraints, aerodynamics, vehicle dynamics, mission and path planning. UAV path planning can be done either online or offline. The current state of path planning optimisation online UAVs with high performance computation is not at the same level as its ground-based offline optimizer's counterpart, this is mainly due to the volume, power and weight limitations on the UAV; some small UAVs do not have the computational power needed for some optimisation and path planning task. In this paper, we describe an optimisation method which can be applied to Multi-disciplinary Design Optimisation problems and UAV path planning problems. Hardware-based design optimisation techniques are used. The power and physical limitations of UAV, which may not be a problem in PC-based solutions, can be approached by utilizing a Field Programmable Gate Array (FPGA) as an algorithm accelerator. The inevitable latency produced by the iterative process of an Evolutionary Algorithm (EA) is concealed by exploiting the parallelism component within the dataflow paradigm of the EA on an FPGA architecture. Results compare software PC-based solutions and the hardware-based solutions for benchmark mathematical problems as well as a simple real world engineering problem. Results also indicate the practicality of the method which can be used for more complex single and multi-objective coupled problems in aeronautical applications.

2025, Revista de Geografia

Licença Creative Commons Atribuição 4.0 Internacional. CC BY -permite que outros distribuam, remixem, adaptem e criem a partir do seu trabalho, mesmo para fins comerciais, desde que lhe atribuam o devido crédito pela criação original.

2025, 2011 Fourth International Conference on Modeling, Simulation and Applied Optimization

Rotational inverted pendulum (RIP) is widely used as a benchmark system in evaluating various control strategies, as it is an ideal test bed that represents under-actuated plants. Although a proportional-integralderivative (PID) controller is popular and relatively simple in structure, its use in balancing the RIP is usually not recommended due to the difficulties in finding the suitable PID controller parameter values for the RIP system, which has two unstable open-loop poles. Therefore, this paper presents an investigation on a PID tuning problem for stabilizing the RIP using a variant of multi-objective Genetic Algorithm (GA), called the Global Criterion Genetic Algorithm (GCGA). Simulation work has shown that GCGA is capable in tuning the PID controller gains to balance the RIP. Compared to the standard single objective GA, the solutions found in GCGA have better control performances. The optimization results have then been applied to the real RIP in order to validate the results in the real-time environment.

2025, Computers & Structures

This paper describes an analytical sensitivity analysis and optimization implementation for cable-stayed bridge design. The finite element software is based on the Vax/VMS version of the Modulef code [I MODULEF Reference Guide. INRIA (1992).] and was adapted to an IBM-PC compatible. The main focus of this research concerns the analytical sensitivity analysis developed on this platform. The cable-stayed bridge optimization is posed as a multiobjective optimization with goals of minimum cost of material, stresses and displacements. Cable anchor positions on the main girder and pylon and cross-sectional sizes of the structural members are dealt with as design variables. By using the maximum entropy formalism it is shown that a Pareto solution may be found indirectly by the unconstrained optimization of a scalar function. The validity and effectiveness of the proposed technique is examined by means of a three-span steel cable-stayed bridge.

2025, DOAJ (DOAJ: Directory of Open Access Journals)

, with a numerical model of optimization, using Minitab software.

2025

Stamping/ deep drawing is one of the essential metals forming industries that produce parts such as car bodies, aircraft parts, electronic components, and household appliances. However, defects such as wrinkles sometimes occur in the produced part because of the lack of properly blank support during the forming process and the metal sheet's inability to follow the die's contours. This is solved by controlling the sheet's restraining force and material flow. The larger and more complex the product becomes, the more controlled the flow of the metal is needed to produce the shape of the final product. But sometimes controlling the blank-holder force alone is not enough. In these cases, draw beads are integrated into the blank holder or the die to add more restraining force, especially for complex products such as automotive or aerospace parts. The draw bead parameters such as bead groove radius, bead radius, bead-groove clearance, and the friction coefficient play an important role in affecting the draw dead performance, so these will be discussed in this paper.

2025, Annals of Nuclear Energy

A method to improve the optimization performance of a genetic algorithm (GA) for multiobjective optimization problems is proposed. It is based on niche induction among nondominated solutions that is ful®lled by the control on their reproduction potential by using a sharing function. It is applied to an equilibrium cycle fuel reloading pattern for a Self-Fuel-Providing Reactor, and it provides better results compared to ones obtained with an adaptation of a conventional method.

2025, Studies in Computational Intelligence (SCI, volume 1184)

Preface
Optimization lies at the heart of engineering, driving the design of systems that are not only efficient but also innovative and robust. By identifying optimal solutions within given constraints, engineers can enhance the performance of systems in ways that were previously unattainable. This book is a comprehensive guide to engineering optimization, covering fundamental principles, advanced methods, and cutting-edge applications that span multiple domains within engineering.
In recent years, advancements in optimization have introduced an array of tools, from classical approaches to metaheuristics, capable of tackling highly complex problems across fields as diverse as manufacturing, renewable energy, and thermal systems. By exploring the applications of these techniques in real-world case studies, this book equips readers with both the theoretical and practical skills needed to approach modern engineering challenges.
Chapter 1 provides a thorough foundation in engineering optimization theory, beginning with the key elements of optimization, such as objective functions, decision variables, constraints, and the concept of the feasible region. Readers are introduced to optimality conditions that guide the selection of solutions, followed by a comprehensive overview of classical and modern optimization techniques, including heuristic and metaheuristic methods. This chapter also covers sensitivity analysis, which is crucial for understanding the impact of variations in input parameters on outcomes, and concludes with an introduction to multi-objective optimization.
Following this foundational overview, each subsequent chapter applies a specific optimization algorithm to a practical engineering scenario, allowing readers to see these concepts in action. For instance, Chapter 2 explores the optimization of machining performance using the Harris Hawk Optimization algorithm, showcasing its ability to handle multi-objective optimization challenges.
In Chapter 3, the Whale Optimization Algorithm is applied to enhance natural convection in a triangular chamber, demonstrating how evolutionary algorithms can manage complex thermal dynamics. These case studies illustrate how modern algorithms can be customized and fine-tuned to address specific needs, from performance improvements to energy efficiency.
Chapter 4 focuses on the optimization of parabolic trough collectors in solar energy systems, utilizing the Grey Wolf Optimization algorithm. This case highlights the application of bio-inspired algorithms in renewable energy, showcasing their effectiveness in maximizing energy capture while balancing design constraints.
Chapter 5 introduces the Sunflower Optimization algorithm to optimize the design of honeycomb heat sinks, which are essential components in thermal management. This chapter emphasizes the importance of optimizing heat transfer and efficiency in cooling systems, providing valuable insights for applications in electronics and power systems.
Chapter 6 examines the optimization of a small-sized solar PV-T (photovoltaic-thermal) water collector, using the Imperialistic Competitive Algorithm. This chapter highlights the application of optimization techniques to hybrid energy systems, where multi-objective optimization can significantly enhance both power generation and thermal efficiency.
Chapter 7 addresses the multi-objective optimization of a multi-channel cold plate under intermittent pulsating flow using the Jaya Algorithm. This case study explores the optimization of thermal management systems for applications requiring precise temperature control, such as electronics cooling and HVAC systems.
In Chapter 8, the Thermal Exchange Optimization Algorithm is applied to the optimization of a rectangular microchannel heat sink. This chapter demonstrates how optimized microchannel designs can improve heat dissipation in compact devices, highlighting applications in high-density electronics and microfluidics.
Chapter 9 presents a study on the optimization of a solar-driven generation plant using the Grasshopper Optimization Algorithm. This chapter focuses on maximizing the efficiency of solar-based power systems, balancing constraints related to environmental factors and energy storage.
Chapter 10 explores the optimization of cutting parameters and tool geometry using the Cuckoo Search Algorithm. By improving machining efficiency and tool performance, this chapter provides practical insights into manufacturing optimization and cost-effective machining processes.
In addition to showcasing advanced algorithms, this book addresses comparative optimization methods. Each case study includes a comparison between the primary algorithm and alternative approaches, such as the Epsilon constraint method, offering insights into the relative advantages and limitations of each approach. These comparisons provide readers with valuable guidance for selecting the most appropriate algorithm based on the unique requirements of their own projects.
Chapter 11 provides a forward-looking perspective on the future of engineering optimization, discussing emerging trends and the challenges posed by increasingly complex optimization tasks. The chapter explores the latest developments in metaheuristic optimization, addressing topics such as constrained optimization, multi-objective optimization, and the integration of advanced algorithms in engineering contexts. This chapter serves as a valuable resource for those interested in the ongoing evolution of optimization techniques and their expanding role in engineering.
To assist readers with practical implementation, the appendices include comprehensive MATLAB and GAMS models corresponding to each chapter. These resources offer a solid foundation for experimenting with the algorithms and concepts discussed, and they enable readers to apply these models to their own projects, enhancing the book's practical utility.
This book is intended for engineers, researchers, and advanced students seeking to deepen their understanding of optimization in engineering. By bridging theory and application, it provides a holistic view of optimization, demonstrating both the fundamental principles that underpin the field and the advanced methods that drive innovation in today’s engineering landscape. Whether your focus is on designing efficient energy systems, improving manufacturing processes, or optimizing thermal management solutions, this book offers valuable insights and tools to help you achieve your goals.
In a world where engineering solutions must increasingly balance performance, cost, and sustainability, optimization remains a crucial element in achieving success. I hope that this book serves as a guide and inspiration for engineers and researchers, empowering them to leverage optimization as a powerful tool for tackling the complex challenges of modern engineering.
Prof. Lagouge K. Tartibu
Professor of Mechanical engineering
Department of Mechanical and Industrial Engineering Technology
University of Johannesburg
South Africa

2025, IEEE Latin America Transactions

Multiobjective Metaheuristics (MOMH) permit to conceive a complete novel approach to induce classifiers. In the Rule Learning problem, the use of MOMH permit that the properties of the rules can be expressed in different objectives, and then the algorithm finds these rules in an unique run by exploring Pareto dominance concepts. This work describes a Multiobjective Particle Swarm Optimization (MOPSO) algorithm that handles with numerical and discrete attributes. The algorithm is evaluated by using the area under ROC curve and the approximation sets produced by the algorithm are also analyzed following Multiobjective methodology.

2025, Fermentation

Numerous fruits are produced in Ecuador, of which about 40% are never eaten. In addition, fresh goat cheeses are in high demand. However, goat cheese generates goat milk whey with high contamination loads, and, therefore, it must be adequately treated before being discharged into ecosystems. This research aims to use a mixture of tree tomato, common strawberry juices, and goat’s milk whey, to be statically fermented by milk and water kefir grains (WKG) for 48 h. For this, a dual mixture design of L-optimal response surface methodology was carried out to find the conditions that maximized all the responses evaluated (lactic-acid bacteria and yeasts concentrations and the overall acceptability assessed on a 7-point scale). Experiments were carried out in San Gabriel, Ecuador. Temperatures during the day and night were 20.2 ± 0.3 °C and 18.7 ± 0.3 °C, respectively. Three conditions were selected, where the highest response values were reached. Complementary experiments demonstrated the...

2025, IEEE Journal on Selected Areas in Communications

A modified multicarrier (MC) direct-sequence codedivision multiple-access (DS-CDMA) system has been proposed for use over slow multipath fading channels with frequency selectivity in the reverse link transmission of a cellular network. Instead of transmitting data substreams uniformly through subchannels, data substreams hop over subchannels with the hopping patterns adaptively adjusted to the channel fading characteristics. The problem of determining the optimal hopping pattern is formulated as a multiobjective optimization problem, for which an efficient algorithm, based on the water-filling (WF) principle, is designed to solve the problem practically. Simulation results show that the performance in terms of the average bit-error probability (BEP) (over all users) is better than that of single carrier RAKE receiver systems, conventional MC CDMA systems applying moderate error protection, or diversity systems with different combining techniques.

2025, Machines

The optimization of independent automotive suspension systems, which is one of the main pillars of the vehicle performance and comfort, is currently going through a revolutionary change due to the development of artificial intelligence and quantum computing. This paper aims to review the multi-objective optimization of suspension parameters including camber, caster, and toe to discuss the complex design issues that arise from geometric and dynamic considerations. Some of the most common computational methodologies, which are Genetic Algorithms, Particle Swarm Optimization, and Gradient Descent, are discussed in this paper along with the new quantum computing techniques such as Gate-Based quantum computing and Quantum Annealing (QA). In addition, this review incorporates information from the practice of automotive manufacturers who have incorporated the use of artificial intelligence and quantum computing in their suspension systems. However, there are still some issues remaining, such as the computational cost, real-time flexibility, and the applicability of theoretical concepts to actual engineering structures. Some potential future research directions are introduced in this paper, such as hybrid optimization approaches, quantum techniques, and adaptive materials, which are considered as potential directions for future development. This systematic review presents a conceptual framework for researchers and engineers to follow, stressing the importance of interdisciplinarity in the development of intelligent suspension systems with performance objectives that are capable of adjusting to various road conditions. The findings of this work underscore the growing importance of complex computational techniques in modern automotive industry and highlight their potential to shape future developments based on emerging trends and industry practices.

2025, Socio-Economic Planning Sciences

In this paper we describe a multiobjective optimization model of "Smart Growth" applied to land development in Montgomery Country, Maryland. The term "Smart Growth" is generally meant to describe those land development strategies which do not result in urban sprawl, however the term is somewhat open to interpretation. The multiobjective aspects arise when considering the conflicting interests of the various stakeholders involved: the government planner, the environmentalist, the conservationist, and the land developer. We present a formulation, which employs linear and convex quadratic objective functions for the stakeholders that are subject to polyhedral and binary constraints. As such, the resulting optimization problems are convex, quadratic mixed integer programs which are known to be NP-complete (Mansini and Speranza, 1999). We report numerical results with this model and present these results using a geographic information system (GIS).

2025

The design of offshore wind farms is computationally challenging, requiring the simultaneous optimisation of many conflicting objectives. Solving this problem is of paramount importance if society is to meet ambitious net zero goals. The problem is solved by identifying an optimal arrangement of individual turbines such that all objectives are optimised. However, a single solution does not exist due to the inherent conflict between objectives, and a set of solutions must be identified. As well as the challenge in generating optimal solution sets, there exists a decision support task if the solutions are to be effectively presented to a decision maker. This study focuses on six key objectives: wind farm efficiency, annual energy production, electric cable length, number of wind turbines, levelised cost of energy, and total area. Two evolutionary algorithms, NSGA-II and NSGA-III, were employed to explore the solution space efficiently. Performance evaluation was performed using spacing, generational distance, and hypervolume metrics. The aforementioned algorithms and metrics were applied to three wind farm layouts: a discrete layout and two continuous layouts. The NSGA-III algorithm was shown to perform better than its predecessor (NSGA-II). The difference was small, albeit significant. Previous works (e.g., Rodrigues et al. (2016); Mytilinou and Kolios ( )) in which many-objective optimisations were discussed provided little insight into the visualisation and interpretation of the results. While the mentioned work used parallel coordinate plots, this work provides a deeper insight by presenting the results via Principal Component Analysis (PCA) and Multi Dimensional Scaling (MDS) plots. The best solution, containing 6188 wind farm layouts, was found by the NSGA-III algorithm on a continuous wind farm layout with repair mechanism. From the best solution, the wind farms containing 27, 102 and 160 wind turbines were selected and compared with the real wind farms located around the UK. It was demonstrated that the optimiser could identify better wind farm layouts concerning annual energy production, efficiency, and LCOE than the real wind farm layouts of Rhyl Flats and Greater Gabbard.

2025, GEOPHYSICS

We have developed a new algorithm for the inversion of magnetotelluric (MT) data. The developed algorithm is built to be fast, versatile, and accurate. A fast inversion algorithm has to include a fast forward-modeling routine. To achieve that, a hybrid approach consisting of finite-difference (FD) and finite-element (FE) methods is used to benefit from the speed of the FD method and the flexibility to add topographic features of the FE method. To reduce the number of cells, and thus reducing the size of the system to be solved in the forward and pseudoforward solutions, different meshes for various groups of frequencies are used. Then, these are mapped onto the inversion mesh by a mesh-decoupling technique to further accelerate the inversion. To build a versatile inversion algorithm, the capability of using different data types is implemented. In addition to the impedance tensor and the magnetic transfer function, the algorithm also computes the phase tensor and phase vector, which ...

2025, Structural and Multidisciplinary Optimization

Multiobjective optimization is one of the key challenges in engineering design process. Since the answer to such problem is not unique, a set of evenly distributed solutions is particularly important for a designer. The Directed Search Domain (DSD) method is a numerical optimization approach that has proven to be efficient enough to tackle such optimization problems. In this paper, we propose two modifications to the DSD approach which make the solution algorithm simpler for program implementation. These modifications are related to the control of the search domain and reformulation of the appropriate single objective optimization problem. As a result, the computational efficiency of the method is increased due to the lower number of objective function evaluations. The capabilities of the new approach are demonstrated on a set of test cases.

2025, Reliability Engineering & System Safety

When attempting to optimize the design of engineered systems, the analyst is frequently faced with the demand of achieving several targets (e.g. low costs, high revenues, high reliability, low accident risks), some of which may very well be in con¯ict. At the same time, several requirements (e.g. maximum allowable weight, volume etc.) should also be satis®ed. This kind of problem is usually tackled by focusing the optimization on a single objective which may be a weighed combination of some of the targets of the design problem and imposing some constraints to satisfy the other targets and requirements. This approach, however, introduces a strong arbitrariness in the de®nition of the weights and constraints levels and a criticizable homogenization of physically different targets, usually all translated in monetary terms. The purpose of this paper is to present an approach to optimization in which every target is considered as a separate objective to be optimized. For an ef®cient search through the solution space we use a multiobjective genetic algorithm which allows us to identify a set of Pareto optimal solutions providing the decision maker with the complete spectrum of optimal solutions with respect to the various targets. Based on this information, the decision maker can select the best compromise among these objectives, without a priori introducing arbitrary weights.

2025

A representação do comportamento das substâncias com o uso de equações de estado pode apresentar dificuldades na região próxima ao ponto crítico. As equações de estado podem apresentar desvios significativos nessa região quando se compara os valores calculados via equação de estado com os valores das variáveis de estado obtidos experimentalmente. Com o objetivo de minimizar os desvios na região próxima ao ponto crítico, no caso do eteno, foi feito um ajuste dos parâmetros da equação com o uso de métodos numéricos para se obter uma melhor representação do diagrama de Mollier via equação de estado. O método numérico para o ajuste dos coeficientes da equação -Peng-Robinson modificada por Melhem -foi o método dos mínimos quadrados com ajuste dos parâmetros da equação de modo a aproximar os valores calculados com os valores experimentais. O objetivo desse estudo é obter uma equação que represente comportamento do eteno na região próxima ao ponto crítico sem alterar a equação propriamente dita, ou seja, ajustando apenas os seus coeficientes (PIBIC/UFRGS)

2025, JAWRA Journal of the American Water Resources Association

Forest roads are associated with accelerated erosion and can be a major source of sediment delivery to streams, which can degrade aquatic habitat. Controlling road-related erosion therefore remains an important issue for forest stewardship. Managers are faced with the task to develop efficient road management strategies to achieve conflicting environmental and economic goals. This manuscript uses mathematical programming techniques to identify the efficient frontier between sediment reduction and treatment costs. Information on the nature of the tradeoffs between conflicting objectives can give the decision maker more insight into the problem, and help in reaching a suitable compromise solution. This approach avoids difficulties associated with a priori establishment of targets for sediment reduction, preferences between competing objectives, and mechanisms to scale noncommensurate objectives. Computational results demonstrate the utility of this multiobjective optimization approach, which should facilitate tradeoff analysis and ideally promote efficient erosion control on forest roads.

2025, arXiv (Cornell University)

This work provides the exact expression of the probability distribution of the hypervolume improvement (HVI) for bi-objective generalization of Bayesian optimization. Here, instead of a single-objective improvement, we consider the improvement of the hypervolume indicator concerning the current best approximation of the Pareto front. Gaussian process regression models are trained independently on both objective functions, resulting in a bi-variate separated Gaussian distribution serving as a predictive model for the vector-valued objective function. Some commonly HVI-based acquisition functions (probability of improvement and upper confidence bound) are also leveraged with the help of the exact distribution of HVI. In addition, we show the superior numerical accuracy and efficiency of the exact distribution compared to the commonly used approximation by Monte-Carlo sampling. Finally, we benchmark distribution-leveraged acquisition functions on the widely applied ZDT problem set, demonstrating a significant advantage of using the exact distribution of HVI in multi-objective Bayesian optimization.

2025

The resolution of the environmental/economic dispatch (EED) problem using the different methods which are proposed in literature consumes an important computing time. Thus, the present paper deals with a technique based on two steps to solve the EED problem of electric energy power in real-time for forecast load curve. The first step uses the NSGAII approach (Non-dominated Sorting Genetic Algorithm) to solve the multi-objective problem MOP for different levels of load by treating the two cases, problem without line constraints and with line constraints. To verify effectiveness of this approach, NSGAII is compared with other algorithms which are used in the literature. Such as, weighted sum method (WSM), NPGA (Niched Pareto Genetic Algorithm), NSGA and SPEA (Strength Pareto Evolutionary Algorithm). To exploit the results in real time for forecast load curve, second step uses a radial basis function neural network (RBFN) with 3 layers, input layer formed by the level of global load, hidden layer and output layer formed by the generations of the various machines. The validity and effectiveness of this technique are verified by an example of a load curve of a didactic electric network IEEE 30-bus system with 6-generating units.

2025

Agriculture serves as the backbone of the Philippine economy, with its ability to sustain a growing global population reliant on effective resource management. To optimize resources such as fertilizers, pesticides, and area, researchers have employed Javadi Et. Al.'s Non-Dominated Sorting Genetic Algorithm-II-Grid-Based Crowding Distance Algorithm (NSGA-II-Gr). Resource allocation and optimization often involve multi-objective decision-making, requiring careful trade-offs among competing parameters. However, upon simulating in higher dimensions, it converges prematurely to suboptimal solutions. After several iterations, the population tends to be dominated by the 'best' solution, leading to premature convergence. This reduces the diversity of candidate solutions and increases the risk of converging to a local optimum, ultimately limiting the exploration of solutions. To address this issue, the researchers introduced a separate spreading mechanism where the mutation intensity decreases over generations. Initially, the mutation strength is high to ensure significant diversity while later in the process, it weakens to promote convergence. This enhancement successfully allowed the algorithm to first explore the solutions before converging. By effectively preventing premature convergence, the modified algorithm gains the ability to explore a broader range of potential solutions. This advancement is particularly valuable for optimizing crop yield and identifying the most effective combinations and trade-offs in agricultural resource management.

2025, Electronic Notes in Discrete Mathematics

Many crossover operators have been proposed and adapted to different combinatorial optimization problems. In particular, many permutation based crossovers are well designed for the traveling salesman problem (TSP) which is among the moststudied combinatorial optimization problems. However, there is no evidence that one crossover operator is superior to another operator. This is specially true for multiobjective optimization. The performance of any genetic algorithm generally varies according to the crossover and mutation operators used. We propose to include mutiple crossover and mutation operators with a dynamic selection scheme Electronic Notes in Discrete Mathematics 36 (2010) 939-946

2025, ArXiv

It is shown that the use of an external archive, purely for storage purposes, can bring substantial benefits in multi-objective optimization. A new scheme for archive management for the above purpose is described. The new scheme is combined with the NSGA-II algorithm for solving two multi-objective optimization problems, and it is demonstrated that this combination gives significantly improved sets of Pareto-optimal solutions. The additional computational effort because of the external archive is found to be insignificant when the objective functions are expensive to evaluate.

2025, Proceedings of the 10th annual conference companion on Genetic and evolutionary computation

A stent device is a permanent metallic implant currently used to prop open arteries blocked with atherosclerotic plaques. Many classes of stents are available and mainly differ by their design. Our purpose in this paper is to determine some optimal stent parameters to ensure a conforming blood flow through the stented artery. Coupling computational fluid dynamics with stochastic optimization based method is used to obtain the optimal parameters of a simplified stent. Our results point out that the obtained related stents overcome or at least reduce the risk of the late restenosis in stented segments.

2025, Applied Mathematical Sciences

This paper is coming with a view to extend and improve the multiobjective optimization of a stent in a fluid structure context studied in the previous works. The stent is assumed to be elastic and is modeled by Euler-Bernouilli equation. To obtain an optimal stent shape, we combine a fluid structure interaction computational method with a -multiobjective evolutionary algorithm.

2025, mdpi books

This is the reprint book of a special issue with the same title in the journal Mathematics. It contains 11 papers on 232 pages. The hardcover book can be ordered via the website of the publisher.

2025, 45th AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit

The performances achievable with conventional engine-cycles may be reaching their limits. Preliminary results show that two-combustor engines can achieve larger flight envelopes and higher thrusts, but at the expenses of higher fuel-consumption rates when compared with those for comparable conventional engines. A simple transient-behaviour prediction methodology is adopted and integrated with TURBOMATCH. This approach was validated and extended to predict the performances of the baseline (i.e. conventional) and two-combustor engines. The latter has been compared with that of the baseline engine, whose performance is identical to that of an F100-PW229 engine. The present analysis has been undertaken using data from the published literature.

2025, European Journal of Operational Research

In this article, we consider the problem of planning inspections and other tasks within a software development (SD) project with respect to the objectives quality (no. of defects), project duration, and costs. Based on a discrete-event simulation model of SD processes comprising the phases coding, inspection, test, and rework, we present a simplified formulation of the problem as a multiobjective optimization problem. For solving the problem (i.e. finding an approximation of the efficient set) we develop a multiobjective evolutionary algorithm. Details of the algorithm are discussed as well as results of its application to sample problems.

2025, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

For network-on-chip (NoC) designs, optimizing buffers is an essential task since buffers are a major source of cost and power consumption. This paper proposes flow regulation and has defined a regulation spectrum as a means for system-on-chip architects to control delay and backlog bounds. The regulation is performed per flow for its peak rate and burstiness. However, many flows may have conflicting regulation requirements due to interferences with each other. Based on the regulation spectrum, this paper optimizes the regulation parameters aiming for buffer optimization. Three timing-constrained buffer optimization problems are formulated, namely, buffer size minimization, buffer variance minimization, and multiobjective optimization, which has both buffer size and variance as minimization objectives. Minimizing buffer variance is also important because it affects the modularity of routers and network interfaces. A realistic case study exhibits 62.8% reduction of total buffers, 84.3% reduction of total latency, and 94.4% reduction on the sum of variances of buffers. Likewise, the experimental results demonstrate similar improvements in the case of synthetic traffic patterns. The optimization algorithm has low run-time complexity, enabling quick exploration of large design spaces. This paper concludes that optimal flow regulation can be a highly valuable instrument for buffer optimization in NoC designs.

2025, Symmetry

The formation of patterns is one of the main stages in logical data analysis. Fuzzy approaches to pattern generation in logical analysis of data allow the pattern to cover not only objects of the target class, but also a certain proportion of objects of the opposite class. In this case, pattern search is an optimization problem with the maximum coverage of the target class as an objective function, and some allowed coverage of the opposite class as a constraint. We propose a more flexible and symmetric optimization model which does not impose a strict restriction on the pattern coverage of the opposite class observations. Instead, our model converts such a restriction (purity restriction) into an additional criterion. Both, coverage of the target class and the opposite class are two objective functions of the optimization problem. The search for a balance of these criteria is the essence of the proposed optimization method. We propose a modified evolutionary algorithm based on the N...