A Hybrid Differential Evolution Algorithm with Opposition-based Learning
Differential evolution (DE) is a popular optimization technique, however it also tends to suffer from premature convergence. One possible way to fix this problem is adaptively to choose the right mutation strategy and control parameter setting for distinct problems. Recently, a new concept, opposition-based learning, was introduced to computational intelligent, which was experimentally proven to be effective and robust. Therefore, a new approach is proposed to combine these two means in attempt to enhance the ability of DE. In the proposed approach, one solution produced by different mutation strategies and parameter setting is used to generate the corresponding opposite one, and then these two solutions are simultaneously evaluated to make the better one as the offspring. The experiments are conducted on 13 well-known benchmark functions, and the experimental results compared with other several state-of-the-art DE variants show that the proposed approach is effective and robust.
Differential evolution adaptive approach opposition-based learning search space convergence speed
Jianghua Li
School of Information Engineering Jiangxi University of Science & Technology Ganzhou, China
国际会议
南昌
英文
85-89
2012-08-26（万方平台首次上网日期，不代表论文的发表时间）