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

Stringent emission regulations combined with customer demands for improved fuel economy and performance have forced the automotive industry to consider more advanced powertrain configurations than standard port-fuel injected gasoline engines. Modern state-of-the-art powertrain systems may combine several power sources (internal combustion engines, electric motors, fuel cells, etc.) and various exhaust aftertreatment devices (catalytic converters, lean NOx traps, particulate filters, etc.) in addition to conventional engine subsystems such as turbochargers and exhaust gas recirculation. The determination of the way in which these systems need to be operated to meet driver's torque demand, performance and fuel economy expectations while satisfying federal emission regulations is a complex and a multiobjective optimal control problem. This paper reviews some of the approaches to this problem in the context of two case studies

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- Applied Mathematics, Multiobjective Optimization, Automotive Industry, Fuel Cell

Intermittent operation of water networks is prevalent in many developing countries. It is a practical method to continue operation of water distribution networks (WDNs) during unexpected water shortages. Implementation of intermittent water supply compels consumers to withstand periods of interrupted water supply. Intermittent operation increases operating and maintenance costs attributable to the damage of pipes and valves caused by water pressure fluctuations. This paper considers consumers' welfare and system depreciation simultaneously in a multiobjective optimization model for intermittent water supply in WDNs. The objectives of the optimization model are the improvement of water supply resiliency and the maximization of the mechanical reliability of WDNs. The optimization model is solved by means of the honey-bee mating optimization (HBMO) algorithm linked to WDN hydraulic simulator software. The model's performance is tested with several shortage scenarios in two different WDNs. The calculated results show the optimization model's capacity to determine optimal values of water supply resiliency, and demonstrate that consumers' welfare may conflict with the objective of mechanical reliability, giving rise to an optimization possibility tradeoff frontier.

- by Ahmed Khalafallah
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- Civil Engineering, Multiobjective Optimization, Building, Minimum Travel Cost

This paper presents a methodology for solving the Periodic Vehicle Routing Problem (PVRP). Customer locations each with a certain daily demand are known, as well as the capacity of vehicles. In this work, the problem is solved by means of a two-phase heuristic. The first phase is the itinerary assignment to visit each customer, and in the second phase the routing is done for each day of the itinerary. The itinerary assignment is made randomly and the routing is solved using the Savings Heuristic. The problem is treated as a bi-objective problem that minimizes the total distance travelled in the time horizon, minimizing also the maximum number of routes in any day. Three instances were solved identifying the non-dominated solutions and presenting them in a graph of the Pareto front. Computational results show that the proposed approach can yield good quality solutions within reasonable running times.

Robust design optimization is a modeling methodology, combined with a suite of computational tools, which is aimed to solve problems where some kind of uncertainty occurs in the data or in the model. This paper explores robust optimization complexity in the multiobjective case, describing a new approach by means of Polynomial Chaos expansions (PCE). The aim of this paper is to demonstrate that the use of PCE may help and speed up the optimization process if compared to standard approaches such as Monte Carlo and Latin Hypercube sampling.

- by Alberto Lovison and +1
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- Monte Carlo, Multiobjective Optimization, Robust optimization, Latin hypercube

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.

Abstract-Technology foresight deals with the necessity of anticipating the future to better adapt to new situations regarding innovations that directly affect business world. One widely spread methodology in technology foresight is Godet's Scenario Method, which includes a module (MICMAC) performing the so-called structural analysis. The goal of the structural analysis is to identify the most important variables in a system. To this end, it makes use of an influence matrix that describes the relations between the variables. This information is usually given by experts based on their own knowledge and experience. However, some of the information of the influence matrix may contain errors due to the subjective nature of the criteria and opinions of the experts. Here we propose a new analysis that follows a multi-objective approach and allows to measure the sensibility of the model versus possible errors at the input. The well-known NSGA-II algorithm has been used as a solver. The...

- by Rafael Nogueras
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- Business, Computer Science, Artificial Intelligence, Data Mining
- by Ajay Ray
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- Chemical Engineering, Multiobjective Optimization, Genetic Algorithm, Optimization

In this paper, we propose the use of preference-based evolutionary multi-objective optimization techniques (P-EMO) to address various software modelling challenges. P-EMO allows the incorporation of decision maker (i.e., designer) preferences (e.g., quality, correctness, etc.) in multi-objective optimization techniques by restricting the Pareto front to a region of interest easing the decision making task. We discuss the different challenges and potential benefits of P-EMO in software modelling. We report experiments on the use of P-EMO on a well-known modeling problem where very promising results are obtained.

This paper deals with the bi-objective multi-dimensional knapsack problem. We propose the adaptation of the core concept that is effectively used in single-objective multi-dimensional knapsack problems. The main idea of the core concept is based on the “divide and conquer” principle. Namely, instead of solving one problem with n variables we solve several sub-problems with a fraction of n variables (core variables). The quality of the obtained solution can be adjusted according to the size of the core and there is always a trade off between the solution time and the quality of solution. In the specific study we define the core problem for the multi-objective multi-dimensional knapsack problem. After defining the core we solve the bi-objective integer programming that comprises only the core variables using the Multicriteria Branch and Bound algorithm that can generate the complete Pareto set in small and medium size multi-objective integer programming problems. A small example is used to illustrate the method while computational and economy issues are also discussed. Computational experiments are also presented using available or appropriately modified benchmarks in order to examine the quality of Pareto set approximation with respect to the solution time. Extensions to the general multi-objective case as well as to the computation of the exact solution are also mentioned.

Research on the integration of renewable distributed generators (RDGs) in radial distribution systems (RDS) is increased to satisfy the growing load demand, reducing power losses, enhancing voltage profile, and voltage stability index (VSI) of distribution network. This paper presents the application of a new algorithm called 'coyote optimization algorithm (COA)' to obtain the optimal location and size of RDGs in RDS at different power factors. The objectives are minimization of power losses, enhancement of voltage stability index, and reduction total operation cost. A detailed performance analysis is implemented on IEEE 33 bus and IEEE 69 bus to demonstrate the effectiveness of the proposed algorithm. The results are found to be in a very good agreement.

This paper introduces an interactive approach to support multi-criteria decision analysis of project portfolios. In high-scale strategic decision domains, scientific studies suggest that the Decision Maker (DM) can find help by using many-objective optimisation methods, which are supposed to provide values in the decision variables that generate highquality solutions. Even so, DMs usually wish to explore the possibility of reaching some levels of benefits in some objectives. Consequently, they should repeatedly run the optimisation method. However, this approach cannot perform well-in an interactive way-for large instances under the presence of many objective functions. We present a mathematical model that is based on compromise programming and fuzzy outranking to aid DMs in analysing multi-criteria project portfolios on the fly. This approach allows relaxing the problem of rapidly optimising portfolios while preserving the beneficial properties of the DM's preferences expressed by outranking relations. Our model supports the decision analysis on two instance benchmarks: for the first one, a better compromise solution was generated 84% of the runs; for the second one, this ranged from 93% to 97%. Our model was also applied to a real-world problem involving social projects, obtaining satisfactory results.

- by Jerzy Balicki
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- Multiobjective Optimization
- by Mark S Daskin
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- Marketing, Applied Mathematics, Multiobjective Optimization, Supply Chain

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Global warming caused by human activities presents serious global risks. Individuals, governments, and industries need to be more energy efficient and contribute to the mitigation of global warming by reducing their greenhouse gas (GHG) emissions. In previous research, GHG emission reduction has been identified as one important criterion in improving the sustainability of urban infrastructure and urban water systems.Within the water industry, opportunities exist for reducing GHG emissions by improving pumping efficiency via the use of variable-speed pumps (VSPs). Previously, VSPs have been used in the optimization of the operation of existing water distribution systems (WDSs). However, in WDS design optimization problems, fixed-speed pumps (FSPs) are commonly used. In this study, a pump power estimation method, developed using a false position method based optimization approach, is proposed to incorporate VSPs in the conceptual design or planning of water transmission systems (WTSs), using optimization. This pump power estimation method is implemented within the solution evaluation process via a multiobjective genetic algorithm approach. A case study is used to demonstrate the application of the pump power estimation method in estimating pump power and associated energy consumption of VSPs and FSPs in WTS optimization. In addition, comparisons are made between variable-speed pumping and fixed-speed pumping in multiobjective WTS optimization accounting for total cost and GHG emissions. The results show that the use of variable-speed pumping leads to significant savings in both total cost and GHG emissions from WTSs for the case study considered.

- by Saif Ur Rehman Khan and +1
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- Evolutionary Computation, Multiobjective Optimization, Multiobjective Evolutionary Optimization

This paper presents a two-stage genetic algorithm (2S-GA) for multi-objective Job Shop scheduling problems. The 2S-GA is proposed with three criteria: Minimize makespan, Minimize total weighted earliness, and Minimize total weighted tardiness. The proposed algorithm is composed of two Stages: Stage 1 applies parallel GA to find the best solution of each individual objective function with migration among populations. In Stage 2 the populations are combined. The evolution process of Stage 2 is based on Steady-State GA using the weighted aggregating objective function. The algorithm developed can be used with one or two objectives without modification. The genetic algorithm is designed and implemented with the GALIB object library. The random keys representation is applied to the problem. The schedules are constructed using a permutation with m-repetitions of job numbers. Performance of the proposed algorithm is tested on published benchmark instances and compared with results from other published approaches for both the single objective and multi-objective cases. The experimental results show that 2S-GA is effective and efficient to solve job shop scheduling problem in term of solution quality.

- by Hirohisa Aki
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- Engineering, Economics, Multiobjective Optimization, Wear
- by George Mavrotas
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- Integer Programming, Multiobjective Optimization, Gams
- by Drago Ban
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- Engineering, Evolutionary Computation, Finite element method, Differential Evolution

A Simulation Based Design Optimization approach is proposed for the design of RIM driven propellers operating in an accelerating duct. The tool relies on RANS analyses of parametrically described ge-ometries driven by an automatic, multi-objective optimization loop for the design of propellers with improved performances simultaneously in terms of both propulsive efficiency and cavitation inception. Pareto convergence is achieved by using a mix of fully resolved RANS analyses and surrogate models aimed at significantly improving the computational efficiency of the procedure. The effectiveness of the design approach is verified by comparing the devised RIM propellers with the performance of a reference ducted propeller at the same functioning point while design trends and guidelines are extracted from the analysis of the amount of data collected during the optimization process.

Design of seismic-resistant civil structural systems necessitates a balanced minimization of two general conflicting objective functions: the short-term construction investment and the long-term seismic risk. Many of the existing seismic design optimization procedures use single objectives of either the traditional minimum material usage (weight or cost) or the recent minimum expected life-cycle cost, while imposing constraints from relevant code specifications as well as additional seismic performance requirements. The resulting single optimized structural design may not always perform satisfactorily in terms of other important but competing merit objectives; the designer's individual risk-taking preference is not explicitly integrated into the design process. This paper presents a practical and general framework for design optimization of code-compliant seismic-resistant structures. Multiple objective functions, which reflect material usage, initial construction expenses, degr...

Non-recurrent events, such as lane blockage incidents or demand surge due to traffic diversion or rerouting, can increase the congestion on signalized arterial streets, resulting in long queues, queue spillbacks, and significant vehicle delays. This paper compares two methods for the selection of signal timing plans for activation during these events. First, the paper introduces a multi-objective optimization model to determine the signal timing plans considering the performance on the impacted arterial intersection approaches as well as the whole intersection performance measures during the non-recurrent events. The multi-objective optimization problem is solved via a simulation-based optimization utilizing the Non-Dominated Sorting Genetic Algorithm (NSGA-III) algorithm to find a set of Pareto optimal fronts. The Pareto optimal fronts allow trade-offs among various objectives of the optimization. Microscopic simulation models are developed for use as part of the optimization and calibrated using high-resolution controller data to better replicate real-world conditions. The performance of the resulting signal plans is compared to the performance selected using a second approach previously developed by the authors that use machine learning to emulate signal timing expert's decisions during non-recurrent events. The evaluation results show that, although both approaches can improve the performance during non-recurrent congestion, the special signal timing plans obtained from the optimization method can produce better results in improving system performance during non-recurrent events in terms of travel time, intersection delay, throughputs, and queue lengths.

- by Mosammat Tahnin Tariq and +1
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- Machine Learning, Multiobjective Optimization, Traffic Operations, Traffic Congestion
- by Yusuke Nojima
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- Evolutionary Computation, Multiobjective Optimization, Scalability