Abstract
Differential evolution (DE) is a population-based metaheuristic algorithm that has been proved powerful in solving a wide range of real-parameter optimization tasks. However, the selection of the mutation strategy and control parameters in DE is problem dependent, and inappropriate specification of them will lead to poor performance of the algorithm such as slow convergence and early stagnation in a local optimum. This paper proposes a new method termed as Joint Adaptation of Parameters in DE (JAPDE). The key idea lies in dynamically updating the selection probabilities for a complete set of pairs of parameter generating functions based on feedback information acquired during the search by DE. Further, for mutation strategy adaptation, the Rank-Based Adaptation (RAM) method is utilized to facilitate the learning of multiple probability distributions, each of which corresponds to an interval of fitness ranks of individuals in the population. The coupling of RAM with JAPDE results in the new RAM-JAPDE algorithm that enables simultaneous adaptation of the selection probabilities for pairs of control parameters and mutation strategies in DE. The merit of RAM-JAPDE has been evaluated on the benchmark test suit proposed in CEC2014 in comparison to many well-known DE algorithms. The results of experiments demonstrate that the proposed RAM-JAPDE algorithm outperforms or is competitive to the other related DE variants that perform mutation strategy and control parameter adaptation, respectively.
Highlights
Differential evolution (DE) is a population-based algorithm that belongs to the Evolutionary Algorithms family (Storn and Price 1997; Xiong et al 2015)
A different algorithm was proposed by Tanabe et al (Tanabe and Fukunaga 2013), stated as SHADE, which maintains memories of F and crossover rate (CR) values calculated as weighted Lehmer mean and weighted arithmetic mean of successful F and CR values from the last generation An improved version of SHADE was proposed in 2016 (Viktorin et al 2016), in which a multichaotic framework is used to select the parents that will be used during the mutation phase
This paper proposes a new parameter adaption method called Joint Adaptation of Parameters in DE (JAPDE), which jointly adapts the generation of the mutation factor and crossover rate during the running of DE
Summary
Differential evolution (DE) is a population-based algorithm that belongs to the Evolutionary Algorithms family (Storn and Price 1997; Xiong et al 2015). If the algorithm, with the purpose of avoiding stagnation into local optima, excessively explores the search space, it will encounter the problem of slow convergence speed For this reason, a proper trade-off between exploration and exploitation is very much needed in DE. Further JAPDE has been combined with the Rank-Based Mutation Adaptation (RAM) method, which was proposed in our recent study (Leon and Xiong 2018). The reason for this combination is that, the different mutation strategies will highly affect the exploration/exploitation behaviour of DE.
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