A Multimixed Strategy Improved Sparrow Search Algorithm and Its Application in TSP

Abstract

Aiming at the shortcomings of the sparrow search algorithm (SSA), such as falling into local optimum and slow convergence speed, an improved sparrow search algorithm based on multimixed strategy (MISSA) is proposed in this paper. In the initial stage, the iterative chaotic mapping is used to initialize the population in order to improve the diversity of population. In the foraging stage, the golden sine algorithm and nonlinear convergence factor strategy are introduced to optimize the discoverer-follower model, which make search process more comprehensive and extensive for the discoverer. The elite opposition-based learning strategy is used to update the optimal solution and the population obtained in each iteration to improve the self-learning ability of the algorithm. To verify the rationality of the multimixed strategy selection and efficiency of the proposed algorithm, MISSA is compared with three derived single-strategy improved algorithms, other improved SSAs, and five typical swarm intelligence algorithms using ten basic benchmark functions and CEC 2014 function. The optimization results, diversity analysis, and Wilcoxon rank-sum test results certify that the proposed MISSA has better optimization accuracy, convergence speed, and robustness than other compared methods. Moreover, the practicability and feasibility of MISSA are verified by solving the traveling salesman problem (TSP).