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Adaptive Differential Evolution Algorithm Based on Restart Mechanism and Direction Information
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DOI：10.1109/ACCESS.2019.2953776.png)
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Abstract
En 中文
Differential evolution is a competent algorithm for solving single objective real-parameter optimization problems. In order to enhance the performance of adaptive DE algorithms based on successful parameters, in this paper, a new DE algorithm, called adaptive differential evolution restart and direction, abbreviated ADERD, is proposed for solving global numerical optimization problems over continuous space. In the proposed algorithm, a novel mutation strategy based on the feasible descent direction is introduced. Only individuals of the population top ranked with smaller errors adopt this novel strategy to mutate. Additionally, the variable coefficient is used in the restart mechanism first to avoid stagnation and/or jump out of the local optima. Modified mechanism of crossover probability sorting is also introduced. In order to better understand the effectiveness of our proposed strategies, those are integrated into two representative adaptive DE variants, i.e. JADE_rcr and JADE_sort. Experimental results demonstrate that the our proposed strategies are capable of enhancing the performance of JADE_rcr and JADE_sort. Improved JADE_sort is denoted as ADERD_sort. Experiments have been conducted on 30 functions presented in CEC 2017 competition. Moreover, compared with recent adaptive DE algorithms, ADERD_sort obtains better, or at least comparable, results in terms of the quality of final solutions.
Keywords:
Differential evolution
restart mechanism
novel mutation strategy
variable coefficient
descent direction
crossover probability sorting
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