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An Improved Squirrel Search Algorithm for Global Function Optimization

School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
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Algorithms 2019, 12(4), 80; https://doi.org/10.3390/a12040080
Received: 10 March 2019 / Revised: 11 April 2019 / Accepted: 12 April 2019 / Published: 17 April 2019
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Abstract

An improved squirrel search algorithm (ISSA) is proposed in this paper. The proposed algorithm contains two searching methods, one is the jumping search method, and the other is the progressive search method. The practical method used in the evolutionary process is selected automatically through the linear regression selection strategy, which enhances the robustness of squirrel search algorithm (SSA). For the jumping search method, the ‘escape’ operation develops the search space sufficiently and the ‘death’ operation further explores the developed space, which balances the development and exploration ability of SSA. Concerning the progressive search method, the mutation operation fully preserves the current evolutionary information and pays more attention to maintain the population diversity. Twenty-one benchmark functions are selected to test the performance of ISSA. The experimental results show that the proposed algorithm can improve the convergence accuracy, accelerate the convergence speed as well as maintain the population diversity. The statistical test proves that ISSA has significant advantages compared with SSA. Furthermore, compared with five other intelligence evolutionary algorithms, the experimental results and statistical tests also show that ISSA has obvious advantages on convergence accuracy, convergence speed and robustness. View Full-Text
Keywords: the squirrel search algorithm; the jumping search method; the progressive search method; linear regression selection strategy the squirrel search algorithm; the jumping search method; the progressive search method; linear regression selection strategy
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Wang, Y.; Du, T. An Improved Squirrel Search Algorithm for Global Function Optimization. Algorithms 2019, 12, 80.

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