An improvement of opposition-based differential evolution with archive solutions

Differential evolution (DE) is a simple yet efficient evolutionary algorithm. Because of its simplicity, effectiveness and robustness, DE has gradually become more popular and applied in various fields. In addition, a lot of works have been done to improve the search ability of DE. Among them, oppos...

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Bibliographic Details
Main Authors: Kushida, Jun-ichi, Hara, Akira, Takahama, Tetsuyuki
Format: Conference Proceeding
Language:English
Subjects:
Approximation
Archives
Benchmark testing
Benchmarking
Conferences
Differential evolution
Estimates
Evolution
Evolutionary algorithm
Evolutionary computation
Heuristic algorithms
Learning
Mathematical analysis
Opposition-based learning
Optimization
Robustness
Sociology
Statistics
Vectors
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Summary:Differential evolution (DE) is a simple yet efficient evolutionary algorithm. Because of its simplicity, effectiveness and robustness, DE has gradually become more popular and applied in various fields. In addition, a lot of works have been done to improve the search ability of DE. Among them, opposition-based DE (ODE), which is incorporated opposition-based learning (OBL), has shown better performance compared to classical DE. The main idea behind OBL is the simultaneous consideration of an estimate and its corresponding opposite estimate in order to achieve a better approximation for the current candidate solution. In this paper, we improve OBL by using archive solutions and propose an improved version of the ODE. Experimental verifications are conducted on well-known benchmark functions and the performance of the proposed method is evaluated by comparing with classical DE and generalized ODE.
ISSN:2325-0682
2325-0690
DOI:10.1109/ICAMechS.2014.6911590