Abstract
Aiming at the problems of the basic sparrow search algorithm (SSA) in terms of slow convergence speed and the ease of falling into the local optimum, the chaotic mapping strategy, adaptive weighting strategy and t-distribution mutation strategy are introduced to develop a novel adaptive sparrow search algorithm, namely the CWTSSA in this paper. In the proposed CWTSSA, the chaotic mapping strategy is employed to initialize the population in order to enhance the population diversity. The adaptive weighting strategy is applied to balance the capabilities of local mining and global exploration, and improve the convergence speed. An adaptive t-distribution mutation operator is designed, which uses the iteration number t as the degree of freedom parameter of the t-distribution to improve the characteristic of global exploration and local exploration abilities, so as to avoid falling into the local optimum. In order to prove the effectiveness of the CWTSSA, 15 standard test functions and other improved SSAs, differential evolution (DE), particle swarm optimization (PSO), gray wolf optimization (GWO) are selected here. The compared experiment results indicate that the proposed CWTSSA can obtain higher convergence accuracy, faster convergence speed, better diversity and exploration abilities. It provides a new optimization algorithm for solving complex optimization problems.
Highlights
College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China; Research Center of Big Data and Information Management, Civil Aviation Management Institute of China, School of Computer Science, China West Normal University, Nanchong 637002, China
In order to further prove the optimization performance of the CWTSSA, the gray wolf optimization (GWO), particle swarm optimization (PSO), and sparrow search algorithm based on Cauchy distribution and reverse learning (CASSA) are selected for comparative analysis in three different dimensions
It can be seen that the convergence accuracy of the CWTSSA has been improved, and the standard deviation is kept at a small value
Summary
In order to solve some complex optimization problems, many bionic swarm intelligence optimization algorithms have emerged, such as the ant colony algorithm (ACO), particle swarm optimization (PSO), artificial bee colony algorithm (ABC), genetic algorithm (GA), sparrow search algorithm (SSA), grey wolf optimization (GWO), differential evolution (DE), and so on [1–5]. Wang et al [40] proposed a dynamic adaptive SSA algorithm based on Bernoulli chaotic mapping, the SSA algorithm of weighting, Cauchy mutation and reverse learning to improve the efficiency of microgrid clusters. In order to solve these existing problems, a novel adaptive SSA algorithm based on the chaotic mapping strategy, adaptive weighting strategy and t-distribution mutation strategy is proposed in this paper. A novel CWTSSA based on the strategies of chaotic mapping, adaptive weighting and t-distribution mutation is developed to effectively solve the complex optimization problem. Comprehensive experiments are designed and executed to comprehensively prove the effectiveness of the CWTSSA by 15 standard test functions
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