Performance of Differential Evolution and Particle Swarm Methods on Some Relatively Harder Multimodal Benchmark Functions

The IUP Journal of Computational Mathematics, Vol. III, No. 1, pp. 7-18, March 2010

Posted: 28 Mar 2010

Date Written: March 24, 2010

Abstract

Particle swarm optimization and Differential Evolution (DE) optimization methods of global optimization are two of the very versatile methods of minimization/maximization of multimodal non-convex continuous nonlinear functions that find their application in many fields of engineering, natural sciences and social sciences. In most cases they perform better than their rival methods, such as the genetic algorithms, simulated annealing, tabu search and clustering algorithms. However, these methods are not a panacea for all problems of non-convex optimization. The present paper tests their performance on certain harder multimodal functions and finds that in a majority of cases they falter. There is a need, therefore, to improve these methods.

Keywords: Global optimization, Non-convex functions, Particle swarm, Differential evolution, Hard benchmark functions

Suggested Citation

Mishra, Sudhanshu K., Performance of Differential Evolution and Particle Swarm Methods on Some Relatively Harder Multimodal Benchmark Functions (March 24, 2010). The IUP Journal of Computational Mathematics, Vol. III, No. 1, pp. 7-18, March 2010, Available at SSRN: https://ssrn.com/abstract=1577592

Sudhanshu K. Mishra (Contact Author)

North-Eastern Hill University (NEHU) ( email )

NEHU Campus
Shillong, 793022
India
03642550102 (Phone)

HOME PAGE: http://www.nehu-economics.info

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