DocumentCode :
3074849
Title :
Ensemble crowding differential evolution with neighborhood mutation for multimodal optimization
Author :
Hui, S. Y. Ron ; Suganthan, P.
Author_Institution :
Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore
fYear :
2013
fDate :
16-19 April 2013
Firstpage :
135
Lastpage :
142
Abstract :
Many optimization problems possess multiple global solutions or comparably fit local solutions. These multimodal optimization problems require the identification of not just one global optimal, but also multiple compatible solutions. Differential Evolution (DE) has been demonstrated to be highly effective for solving single-objective unimodal problems, but its loss of diversity over the course of evolution prevents it from locating multiple compatible solutions. Our proposed method combines the diversity maintenance of niching and neighborhood mutation techniques with the versatility of ensemble parameters for DE to enhance the exploitation of individual peaks on difficult multi-modal problems. Greedy local mutation strategy and crossover are shown to have improved the performance of the neighborhood crowding DE (NCDE) in our experiment with 14 common multimodal benchmark functions.
Keywords :
evolutionary computation; greedy algorithms; NCDE; crossover; diversity maintenance; ensemble crowding differential evolution; greedy local mutation strategy; multimodal benchmark function; multimodal optimization; multimodal problem; neighborhood crowding DE; neighborhood mutation technique; niching technique; single-objective unimodal problem; Convergence; Evolution (biology); Maintenance engineering; Optimization; Sociology; Statistics; Vectors; Differential Evolution (DE); Multimodal optimization; ensemble parameters; neighborhood mutation; niching;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Differential Evolution (SDE), 2013 IEEE Symposium on
Conference_Location :
Singapore
Type :
conf
DOI :
10.1109/SDE.2013.6601453
Filename :
6601453
Link To Document :
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