Abstract
The differential evolution (DE) algorithm is an efficient random search algorithm based on swarm intelligence for solving optimization problems. It has the advantages of easy implementation, fast convergence, strong optimization ability and good robustness. However, the performance of DE is very sensitive to the design of different operators and the setting of control parameters. To solve these key problems, this paper proposes an improved self-adaptive differential evolution algorithm with a shuffled frog-leaping strategy (SFSADE). It innovatively incorporates the idea of the shuffled frog-leaping algorithm into DE, and at the same time, it cleverly introduces a new strategy of classification mutation, and also designs a new adaptive adjustment mechanism for control parameters. In addition, we have carried out a large number of simulation experiments on the 25 benchmark functions of CEC 2005 and two nonparametric statistical tests to comprehensively evaluate the performance of SFSADE. Finally, the results of simulation experiments and nonparametric statistical tests show that SFSADE is very effective in improving DE, and significantly improves the overall diversity of the population in the process of dynamic evolution. Compared with other advanced DE variants, its global search speed and optimization performance also has strong competitiveness.
Highlights
Differential evolution (DE) was jointly proposed by (Storn and Price 1997) to solve Chebyshev polynomials
The out-of-bound detection operator is after the mutation operator, which will ensure that the value of each dimension in every variant individual Vi,t is within the boundary constraints Xmin, Xmax of the constrained numerical optimization problems (CNOPs) [see Eq (1)]
In order to improve the performance of DE algorithm in solving numerical optimization problems, this paper proposes an improved adaptive differential evolution algorithm SFSADE based on a shuffled frog-leaping strategy
Summary
Differential evolution (DE) was jointly proposed by (Storn and Price 1997) to solve Chebyshev polynomials. In order to increase the diversity of the population and improve the optimization performance and speed of the algorithm, this is a very effective attempt in which the entire population is decomposed into multiple subpopulations in DE These subpopulations are continuously decomposed and merged in the evolution process, and different mutation strategies and control parameter adaptive adjustment mechanisms are used (MA et al 2009). To further improve the solution performance of the DE algorithm, accelerate the convergence speed, prevent falling into local optimization and improve the stability, on the basis of SAMDE (Zhu et al 2020) and p-ADE (Bi and Xiao 2012), we propose an improved self-adaptive differential evolution algorithm with a shuffled frog-leaping strategy, called SFSADE.
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