期刊
APPLIED MATHEMATICS AND COMPUTATION
卷 217, 期 12, 页码 5822-5829出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.amc.2010.12.064
关键词
Shuffled complex evolution algorithm; Genetic algorithm; Differential evolution; Unconstrained optimization; Evolutionary algorithms; Benchmark functions
资金
- National Council of Scientific and Technologic Development of Brazil - CNPq [568221/2008-7, 474408/2008-6, 302786/2008-2-PQ, 303963/2009-3/PQ, 478158/2009-3, 475689/2010-0]
- Fundacao Araucaria [14/2008-416/09-15149]
Numerous optimization methods have been proposed for the solution of the unconstrained optimization problems, such as mathematical programming methods, stochastic global optimization approaches, and metaheuristics. In this paper, a metaheuristic algorithm called Modified Shuffled Complex Evolution (MSCE) is proposed, where an adaptation of the Downhill Simplex search strategy combined with the differential evolution method is proposed. The efficiency of the new method is analyzed in terms of the mean performance and computational time, in comparison with the genetic algorithm using floating-point representation (GAF) and the classical shuffled complex evolution (SCE-UA) algorithm using six benchmark optimization functions. Simulation results and the comparisons with SCE-UA and GAF indicate that the MSCE improves the search performance on the five benchmark functions of six tested functions. (C) 2010 Elsevier Inc. All rights reserved.
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