Hybrid random opposition-based learning and Gaussian mutation of chaotic squirrel search algorithm

To address the problems such as easy to fall into local optimum and premature convergence of Squirrel Search Algorithm (SSA), a hybrid Random opposition-based learning and Gaussian mutation of Chaotic Squirrel Search Algorithm (RGCSSA) was proposed. The chaotic initial population was generated by the Tent chaotic mapping initialization strategy to enhance the uniformity of the initial population distribution and achieve a more efficient search of the solution space. Then, a nonlinear decreasing predator probability strategy was used to balance the global search and local exploitation capabilities of SSA. The positional greedy selection strategy was utilized to increase the convergence speed of the algorithm by continuously retaining the dominant individuals in the population during the iterative process. The random opposition-based learning and Gaussian variation strategies were introduced to increase the population diversity and improve the ability of the algorithm to jump out of the local optimum. The optimization performance of the proposed algorithm was verified by simulation experiments and Wilcoxon's signed rank test on 10 different benchmark functions. The results showed that the RGCSSA algorithm had greatly improved in terms of solution accuracy and convergence speed as well as stability. © 2023 CIMS. All rights reserved.