PerferredLearningBased Differential Evolution Algorithm

Abstract

Abstract: Traditional differential evolution (DE) algorithm only abstracts information of random and the best individual. Randomness may result in a low searching efficiency, and even cannot find the best solution. A perferredlearningbased DE algorithm is proposed to solve this tough problem. The algorithm selectively uses the wellbehaved individual’s information and overcomes the blindness in the evolving process to enhance the searching ability. After different kind benchmark functions are investigated, the results reveal that the number of iterations can be clearly reduced and the calculation efficiency can be improved.

Key words: differential evolution (DE) algorithm, perferred learning (PL), mutation strategy, function optimization

CLC Number:

TP 181

LIU Hao，DING Jinliang，YANG Cui’e，CHAI Tianyou. PerferredLearningBased Differential Evolution Algorithm[J]. Journal of Shanghai Jiaotong University, 2017, 51(6): 704-708.

References

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