Abstract
In recent years, Differential Evolution (DE) has shown excellent performance in solving optimization problems over continuous space and has been widely used in many fields of science and engineering. How to avoid the local optimal solution and how to improve the convergence performance of DE are hotpot problems for many researchers. In this paper, an improved differential evolution algorithm based on dual-strategy (DSIDE) is proposed. The DSIDE algorithm has two strategies. (1) An enhanced mutation strategy based on “DE/rand/1,” which takes into account the influence of reference individuals on mutation and has strong global exploration and convergence ability. (2) A novel adaptive strategy for scaling factor and crossover probability based on fitness value has a positive impact on population diversity. The DSIDE algorithm is verified with other seven state-of-the-art DE variants under 30 benchmark functions. Furthermore, Wilcoxon sign rank-sum test, Friedman test, and Kruskal–Wallis test are utilized to analyze the results. The experiment results show that the proposed DSIDE algorithm can significantly improve the global optimization performance.
Highlights
Differential Evolution (DE) is an emerging optimization technique proposed by Storn and Price [1] in 1995, which was initially used to solve Chebyshev polynomials
Similar to other intelligent evolutionary algorithms, DE is a stochastic parallel optimization algorithm based on swarm intelligence, which guides optimization search by imitating heuristic swarm intelligence generated by cooperation and competition among individuals in the population
It is difficult to verify that evolutionary algorithms are superior to other algorithms due to their limited knowledge
Summary
Differential Evolution (DE) is an emerging optimization technique proposed by Storn and Price [1] in 1995, which was initially used to solve Chebyshev polynomials. Das et al [18] proposed an improved algorithm based on “DE/current-to-best/1” strategy, which made full use of the optimal individual information in the neighborhood to guide the mutation operation. Zhang and Sanderson [19] proposed an adaptive differential evolution algorithm (JADE), which adopted “DE/current-to-pbest/1” mutation model, used suboptimal solutions to improve population diversity, and employed Cauchy and Normal distribution to generate F and CR. Wei et al [29] proposed the RPMDE algorithm, designed the “DE/M. pbest-best/1” mutation strategy, used the optimal individual group information to generate new solutions, and adopted the random perturbation method to avoid falling into the local optimal.
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