MCDMSR: multicriteria decision making selection/replacement based on agility strategy for real optimization problems
作者： HongGuang Zhang , Rui Wang , HuaJian Liu , Han Luo , Yuanan Liu
DOI: 10.1007/S10489-019-01414-7
关键词:
摘要: Selection/replacements are an indispensable part in evolutionary algorithms (EAs), which generally based on a single evaluation criterion. However, selections nature multi criteria, such as multi-aspect survival criteria of wolves (like running and attacking abilities). To realize more real the fittest EAs, multicriteria decision making selection/replacement (MCDMSR) is proposed. In fact, there little research about using to improve models EAs. VIKOR (a method) management science used MCDMSR, populations synthetically analyzed radar chart form. By VIKOR, MCDMSR able respond population-state change time. characterized by this agility, agility derived from ability VIKOR. Moreover, principle analysis discussions given explain feasibility applications selection/replacements. We provided simple genetic algorithm, particle swarm optimization, artificial fish shuffled frog leaping comparing with tournament selection, fine-grained all-individual-guider replacement, CD/RW, constrained-visual-region group part-individual-guider replacement for 36 benchmarks (i.e., 5 unimodal 15 multimodal problems CEC 2013 test suite, 16 P-Peak problems). The effectiveness, efficiency, diversity results were acceptable.
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Robert B. Heckendorn, Soraya Rana, Darrell Whitley, The Island Model Genetic Algorithm: On Separability, Population Size and Convergence computer and information technology. ,vol. 7, pp. 33- 47 ,(2015)
Stanislaw Krenich, Andrzej Osyczka, Some methods for multicriteria design optimization using evolutionary algorithms Journal of Theoretical and Applied Mechanics. ,vol. 42, pp. 565- 584 ,(2004)
John H. Holland, Adaptation in natural and artificial systems ,(1992)
Jim E. Smith, Frantisek Vavak, Replacement strategies in Steady State Genetic Algorithms : Dynamic environments computer and information technology. ,vol. 7, pp. 49- 59 ,(1999)
Larry J. Eshelman, The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination Foundations of Genetic Algorithms. ,vol. 1, pp. 265- 283 ,(1991) , 10.1016/B978-0-08-050684-5.50020-3
John J. Grefenstette, Genetic algorithms for changing environments parallel problem solving from nature. pp. 139- 146 ,(1992)
Trung Thanh Nguyen, Shengxiang Yang, Juergen Branke, Xin Yao, Evolutionary Dynamic Optimization: Methodologies Springer, Berlin, Heidelberg. pp. 39- 64 ,(2013) , 10.1007/978-3-642-38416-5_2
Vladimir Filipović, Fine-grained Tournament Selection Operator in Genetic Algorithms Computing and Informatics \/ Computers and Artificial Intelligence. ,vol. 22, pp. 143- 161 ,(2003)
Andrew J. Chipperfield, James F. Whidborne, Peter J. Fleming, Evolutionary Algorithms and Simulated Annealing for MCDM Springer, Boston, MA. pp. 501- 532 ,(1999) , 10.1007/978-1-4615-5025-9_16
Xin-She Yang, Suash Deb, Cuckoo Search via Lévy flights nature and biologically inspired computing. pp. 210- 214 ,(2009) , 10.1109/NABIC.2009.5393690