Jacqueline Moore
ABSTRACT
Evolutionary algorithms (EAs) are search procedures based on natural selection [2]. They have been successfully applied to a wide variety of optimization problems [4]. Particle Swarm Optimization (PSO) [1,7] is a new type of evolutionary paradigm that has been successfully used to solve a number of single objective optimization problems (SOPs). However, to date, no one has applied PSO in an effort to solve multiobjective optimization problems (MOPs). The purpose of our research is to demonstrate how PSO can be modified to solve MOPs. In addition to showing how this can be done, we demonstrate its effectiveness on two MOPs.
- Angeline, P. "Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences", In 7th International Conference on Evolutionary Programming, San Diego, California, Springer, 1998, pp. 601--610. Google Scholar
Digital Library
- Back, T. Evolutionary Algorithms in Theory and Practice, Oxford University Press, New York, 1996, pp. 7--11. Google ScholarDigital Library
- Coello Coello, C. "A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques", In Knowledge and Information Systems, August 1999, pp. 269--308.Google Scholar
- Goldberg, D. Genetic Algorithms in Search, Optimization & Machine Learning<, Addison-Wesley, Massachusetts, 1989, pp. 106--120. Google ScholarDigital Library
- Kennedy, J. "The Behavior of Particles", In 7th International Conference on Evolutionary Programming, San Diego, California, Springer, 1998, pp. 582--589. Google ScholarDigital Library
- Kennedy, J. "The Particle Swarm, Social Adaptation of Knowledge", In Proceedings of the 1997 International Conference on Evolutionary Computation, IEEE, NJ, pp. 303--308.Google Scholar
- Kennedy, J., and Eberhart, R. "Particle Swarm Optimization", In Proceedings of the 1995 IEEE International Conference on Neural Networks, IEEE, NJ, pp. 1942--1948.Google Scholar
- Lis, J. and Eiben, A. "A MultiSexual Genetic Algorithm for Multiobjective Optimization", In Proceedings of the 1997 International Conference on Evolutionary Computation, Indianapolis, Indiana, 1997, pp. 59--64.Google Scholar
- Wolf, W. "Hardware-Software Co-Design of Embedded Systems", In Proceedings of the IEEE, Vol. 82, No. 7, July 1994, pp. 967--989.Google ScholarCross Ref
- Yu, P. Multiple-Criteria Decision Making, Plenum Press, New York, 1985, pp. 7--10.Google ScholarCross Ref
Recommendations
An parallel particle swarm optimization approach for multiobjective optimization problems
GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computationThis paper proposes a parallel particle swarm optimization (PPSO) to solve the multiobjective optimization problems (MOP). PPSO makes the use of the parallel characteristic of the PSO algorithm to deal with the multiple objectives issue of the MOP. PPSO ...
Euclidean Particle Swarm Optimization
ICINIS '09: Proceedings of the 2009 Second International Conference on Intelligent Networks and Intelligent SystemsParticle swarm optimization (PSO) is a swarm intelligence algorithm, has been successfully applied to many engineering optimization problems and shown its high search speed in these applications. However, as the dimension and the number of local optima ...
Dynamic multiple swarms in multiobjective particle swarm optimization
A multiple-swarm multiobjective particle swarm optimization (PSO) algorithm, named dynamic multiple swarms in multiobjective PSO, is proposed in which the number of swarms is adaptively adjusted throughout the search process via the proposed dynamic ...
Comments