Publication Type : Conference Proceedings
Publisher : Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Source : Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, Springer, p.470–477 (2009)
Url : http://link.springer.com/chapter/10.1007/978-3-642-10646-0_57
Campus : Coimbatore
School : School of Engineering
Center : Amrita Innovation & Research
Department : Computer Science
Verified : Yes
Year : 2009
Abstract : In this paper we present an empirical , comparative performance, analysis of fourteen variants of Differential Evolution (DE) and Multiple Trial Vectors Differential Evolution algorithms to solve unconstrained global optimization problems. The aim is (1) to compare Multiple Trial Vectors DE, which allows each parent vector in the population to generate more than one trial vector, against the classical DE and (2) to identify the competitive variants which perform reasonably well on problems with different features. The DE and Multiple Trial Vectors DE variants are benchmarked on 6 test functions grouped by features – unimodal separable, unimodal nonseparable, multimodal separable and multimodal non-separable. The analysis identifies the competitive variants and shows that Multiple Trial Vectors DE compares well with the classical DE.
Cite this Research Publication : Dr. Jeyakumar G. and Dr. Shunmuga Velayutham C., “A comparative performance analysis of multiple trial vectors differential evolution and classical differential evolution variants”, Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. Springer, pp. 470–477, 2009