DocumentCode :
2779713
Title :
Strategy Adaptative Memetic Crowding differential evolution for multimodal optimization
Author :
Liang, J.J. ; Ma, S.T. ; Qu, B.Y. ; Niu, B.
Author_Institution :
Sch. of Electr. Eng., Zhengzhou Univ., Zhengzhou, China
fYear :
2012
fDate :
10-15 June 2012
Firstpage :
1
Lastpage :
7
Abstract :
Differential evolution (DE) is undoubtedly one of the most powerful stochastic searching optimization algorithms. However, solving a specific problem using DE crucially depends on appropriately choosing of trial vector generation strategies and their associated control parameters. At the same time, multimodal optimization refers to locating not only one optimum but a set of optimal solutions. Niching is a useful technique to solve multi-modal optimization problems. Discovering multiple niches is the key capability of niching algorithms. In this paper, we propose a Strategy Adaptive Memetci Crowding DE (SAMCDE), which incorporate Crowding DE (CDE) with strategies and control parameter self-adaptation technique as well as fine search technique to handle multi-modal optimization problems. The algorithm is tested on 10 benchmark multi-modal functions and compared with the original CDE as well as several popular multimodal optimization algorithms in literature. As shown by the experimental results, the proposed algorithm is able to generate superior performance on the tested functions.
Keywords :
genetic algorithms; vectors; DE; control parameter self-adaptation technique; multimodal optimization; niching technique; stochastic searching optimization algorithm; strategy adaptative memetic crowding differential evolution; trial vector generation strategy; Accuracy; Benchmark testing; Educational institutions; Memetics; Optimization; Search problems; Vectors; differential evolution; multimodal optimization; niching; strategy adaptive;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation (CEC), 2012 IEEE Congress on
Conference_Location :
Brisbane, QLD
Print_ISBN :
978-1-4673-1510-4
Electronic_ISBN :
978-1-4673-1508-1
Type :
conf
DOI :
10.1109/CEC.2012.6252917
Filename :
6252917
Link To Document :
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