Handling Dynamic Multiobjective Problems with Particle Swarm Optimization (original) (raw)
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On performance metrics and particle swarm methods for dynamic multiobjective optimization problems
2007 IEEE Congress on Evolutionary Computation, 2007
This paper describes two performance measures for measuring an EMO (Evolutionary Multiobjective Optimization) algorithm's ability to track a time-varying Paretofront in a dynamic environment. These measures are evaluated using a dynamic multiobjective test function and a dynamic multiobjective PSO, maximinP SOD, which is capable of handling dynamic multiobjecytive optimization problems. maximinP SOD is an extension from a previously proposed multiobjective PSO, maximinP SO. Our results suggest that these performance measures can be used to provide useful information about how well a dynamic EMO algorithm performs in tracking a time-varying Pareto-front. The results also show that maximinP SOD can be made self-adaptive, tracking effectively the dynamically changing Pareto-front.
Particle swarm optimization method in multiobjective problems
Proceedings of the 2002 ACM symposium on Applied computing - SAC '02, 2002
This paper constitutes a rst study of the Particle Swarm Optimization (PSO) method in Multiobjective Optimization (MO) problems. The ability of PSO to detect Pareto Optimal points and capture the shape of the Pareto Front is studied through experiments on well{known non{trivial test functions. The Weighted Aggregation technique with xed or adaptive weights is considered. Furthermore, critical aspects of the VEGA approach for Multiobjective Optimization using Genetic Algorithms are adapted to the PSO framework in order to develop a multi{swarm PSO that can cope e ectively with MO problems. Conclusions are derived and ideas for further research are proposed.
Dynamic Particle Swarm Optimization to Solve Multi-objective Optimization Problem
Procedia Technology, 2012
Multi-objective optimization problem is reaching better understanding of the properties and techniques of evolutionary algorithms. This paper presents the Dynamic Particle Swarm Optimization algorithm for solving multiobjective PSO in terms of swarm size, topology and search space. In this paper swarm size criteria for dynamic PSO is considered. Experiment conducted for standard benchmark functions of multi-objective optimization problem, which shows the better performance rather the basic PSO.
A novel Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization (DPb-MOPSO) algorithm
2020
Distributed architecture-based Particle Swarm Optimization is very useful for static optimization and not yet explored to solve complex dynamic multi-objective optimization problems. This study proposes a novel Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization (DPb-MOPSO) algorithm with two optimization levels. In the first level, all solutions are optimized in the same search space and the second level is based on a distributed architecture using the Pareto ranking operator for dynamic multi-swarm subdivision. The proposed approach adopts a dynamic handling strategy using a set of detectors to keep track of change in the objective function that is impacted by the problem’s time-varying parameters at each level. To ensure timely adaptation during the optimization process, a dynamic response strategy is considered for the reevaluation of all non-improved solutions, while the worst particles are replaced with a newly generated one. The convergence anddiversity perfor...
DPb-MOPSO: A Novel Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization Algorithm
Particle swarm optimization system based on the distributed architecture over multiple sub-swarms has shown its efficiency for static optimization and has not been studied to solve dynamic multi-objective problems (DMOPs). When solving DMOP, tracking the best solutions over time and ensuring good exploitation and exploration are the main challenging tasks. This study proposes a novel Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization (DPb-MOPSO) algorithm including two parallel optimization levels. At the first level, all solutions are managed in a single search space. When a dynamic change is successfully detected in the objective values, the Pareto ranking operator is used to enable a multiple sub-swarm’ subdivisions and processing which drives the second level of enhanced exploitation. A dynamic handling strategy based on random detectors is used to track the changes of the objective function due to time-varying parameters. A response strategy consisting in re-ev...
Solving dynamic multi-objective problems with vector evaluated particle swarm optimisation
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 2008
Many optimisation problems are multi-objective and change dynamically. Many methods use a weighted average approach to the multiple objectives. This paper introduces the usage of the vector evaluated particle swarm optimiser (VEPSO) to solve dynamic multi-objective optimisation problems. Every objective is solved by one swarm and the swarms share knowledge amongst each other about the objective that it is solving. Not much work has been done on using this approach in dynamic environments. This paper discusses this approach as well as the effect of the population size and the response methods to a detected change on the performance of the algorithm. The results showed that more non-dominated solutions, as well as more uniformly distributed solutions, are found when all swarms are re-intialised when a change is detected, instead of only the swarm(s) optimising the specific objective function(s) that has changed. Furthermore, an increase in population size results in a higher number of non-dominated solutions found, but can lead to solutions that are less uniformly distributed.
PhD Thesis, 2012
Most optimisation problems in everyday life are not static in nature, have multiple objectives and at least two of the objectives are in conflict with one another. However, most research focusses on either static multi-objective optimisation (MOO) or dynamic singleobjective optimisation (DSOO). Furthermore, most research on dynamic multi-objective optimisation (DMOO) focusses on evolutionary algorithms (EAs) and only a few particle swarm optimisation (PSO) algorithms exist. This thesis proposes a multi-swarm PSO algorithm, dynamic Vector Evaluated Particle Swarm Optimisation (DVEPSO), to solve dynamic multi-objective optimisation problems (DMOOPs). In order to determine whether an algorithm solves DMOO efficiently, functions are required that resembles real world DMOOPs, called benchmark functions, as well as functions that quantify the performance of the algorithm, called performance measures. However, one major problem in the field of DMOO is a lack of standard benchmark functions and performance measures. To address this problem, an overview is provided from the current literature and shortcomings of current DMOO benchmark functions and performance measures are discussed. In addition, new DMOOPs are introduced to address the identified shortcomings of current benchmark functions. Guides guide the optimisation process of DVEPSO. Therefore, various guide update approaches are investigated. Furthermore, a sensitivity analysis of DVEPSO is conducted to determine the influence of various parameters on the performance of DVEPSO. The investigated parameters include approaches to manage boundary constraint violations, approaches to share knowledge between the sub-swarms and responses to changes in the environment that are applied to either the particles of the sub-swarms or the non-dominated solutions stored in the archive. From these experiments the best DVEPSO configuration is determined and compared against four state-of-the-art DMOO algorithms.
An Enhanced Particle Swarm Optimization Algorithm for Multiobjective Problems
International Journal of Computer and Information Engineering, 2018
Multiobjective Particle Swarm Optimization (MOPSO) has shown an effective performance for solving test functions and real-world optimization problems. However, this method has a premature convergence problem, which may lead to lack of diversity. In order to improve its performance, this paper presents a hybrid approach which embedded the MOPSO into the island model and integrated a local search technique, Variable Neighborhood Search, to enhance the diversity into the swarm. Experiments on two series of test functions have shown the effectiveness of the proposed approach. A comparison with other evolutionary algorithms shows that the proposed approach presented a good performance in solving multiobjective optimization problems.
Particle Swarm Optimization for Constrained and Multiobjective Problems: A Brief Review
ipedr.net
Particle swarm optimization (PSO) is an optimization method that belongs to the swarm intelligence family. It was initially introduced as continuous problem optimization tool. It has evolved to being applied to more complex multiobjective and constrained problem. This paper presents a systematic literature review of PSO for constrained and multiobjective optimization problems.
Dynamic Multi-Swarm Particle Swarm Optimization for Multi-objective optimization problems
2012 IEEE Congress on Evolutionary Computation, 2012
Particle swarm optimization (PSO) has been initially proposed as an optimization technique for static environments; however, many real problems are dynamic, meaning that the environment and the characteristics of the global optimum can change over time. Thanks to its stochastic and population based nature, PSO can avoid being trapped in local optima and find the global optimum. However, this is never guaranteed and as the complexity of the problem rises, it becomes more probable that the PSO algorithm gets trapped into a local optimum due to premature convergence. In dynamic environments the optimization task is even more difficult, since after an environment change the earlier global optimum might become just a local optimum, and if the swarm is converged to that optimum, it is likely that new real optimum will not be found. For the same reason, local optima cannot be just discarded, because they can be later transformed into global optima. In this paper, we propose novel techniques, which successfully address these problems and exhibit a significant performance over multi-modal and non-stationary environments. In order to address the premature convergence problem and improve the rate of PSO's convergence to global optimum, Fractional Global Best Formation (FGBF) technique is developed. FGBF basically collects all the best dimensional components and fractionally creates an artificial Global Best particle (aGB) that has the potential to be a better "guide" than the PSO's native gbest particle. In this way the potential diversity that is present among the dimensions of swarm particles can be efficiently used within the aGB particle. To establish follow-up of (current) local optima, we then introduce a novel multi-swarm algorithm, which enables each swarm to converge to a different optimum and use FGBF technique distinctively. We investigated the proposed techniques over the Moving Peaks Benchmark (MPB), which is a publicly available test bench for testing optimization algorithms in a multi-modal dynamic environment. An extensive set of experiments show that FGBF technique with multi-swarms exhibits an impressive speed gain and tracks the global maximum peak with the minimum error so far achieved with respect to the other competitive PSO-based methods.