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Adaptive backtracking search optimization algorithm with pattern search for numerical optimization 被引量:6
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作者 Shu Wang Xinyu Da +1 位作者 Mudong Li Tong Han 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2016年第2期395-406,共12页
The backtracking search optimization algorithm(BSA) is one of the most recently proposed population-based evolutionary algorithms for global optimization. Due to its memory ability and simple structure, BSA has powe... The backtracking search optimization algorithm(BSA) is one of the most recently proposed population-based evolutionary algorithms for global optimization. Due to its memory ability and simple structure, BSA has powerful capability to find global optimal solutions. However, the algorithm is still insufficient in balancing the exploration and the exploitation. Therefore, an improved adaptive backtracking search optimization algorithm combined with modified Hooke-Jeeves pattern search is proposed for numerical global optimization. It has two main parts: the BSA is used for the exploration phase and the modified pattern search method completes the exploitation phase. In particular, a simple but effective strategy of adapting one of BSA's important control parameters is introduced. The proposed algorithm is compared with standard BSA, three state-of-the-art evolutionary algorithms and three superior algorithms in IEEE Congress on Evolutionary Computation 2014(IEEE CEC2014) over six widely-used benchmarks and 22 real-parameter single objective numerical optimization benchmarks in IEEE CEC2014. The results of experiment and statistical analysis demonstrate the effectiveness and efficiency of the proposed algorithm. 展开更多
关键词 evolutionary algorithm backtracking search optimization algorithm(BSA) Hooke-Jeeves pattern search parameter adaption numerical optimization
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