Indexed by:会议论文
Date of Publication:2007-07-30
Included Journals:EI、CPCI-S、Scopus
Volume:2
Page Number:470-+
Key Words:global optimization; real-coded; evolutionary algorithm; differential evolution; low dimensional simplex evolution
Abstract:In this paper, anew real-coded evolutionary algorithm-low dimensional simplex evolution (LDSE) for global optimization is proposed. It is a hybridization of two well known heuristics, the differential evolution (DE) and the Nelder-Mead method. LDSE takes the idea of DE to randomly select parents from the population and perform some operations with them to generate new individuals. Instead of using the evolutionary operators of DE such as mutation and cross-over, we introduce operators based on the simplex method, which makes the algorithm more systematic and parameter free. The proposed algorithm is very easy to implement, and its efficiency has been studied on an extensive testbed of 50 test problems from [I]. Numerical results show that the new algorithm outperforms DE in terms of number of function evaluations (nfe) and percentage of success (ps).
Professor
Supervisor of Doctorate Candidates
Supervisor of Master's Candidates
Gender:Male
Alma Mater:吉林大学
Degree:Doctoral Degree
School/Department:数学科学学院
Discipline:Computational Mathematics. Financial Mathematics and Actuarial Science
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