关键词: 粒子群优化算法, Circle映射, 正弦余弦因子, 随机搜索策略, 正弦余弦算法, 鲸鱼优化算法

Abstract: Aiming at the problems of slow convergence speed, low accuracy and easy to fall into local optimization in the process of search and optimization of particle swarm algorithms, an improved particle swarm algorithm introducing Circle mapping and sine cosine factor is proposed. The population initialization operation introduced into Circle mapping can obtain a more uniform and diverse initial population, which is conducive to improving the convergence speed and accuracy of the algorithm. The strategy of nonlinearly decreasing inertia weight and introducing the sine cosine factor in the sine cosine algorithm is adopted, which better balances the global exploration ability and local development ability of the algorithm. Inspired by the whale optimization algorithm, a random search strategy with elimination system is proposed and adopted, which enhances the ability of the algorithm to jump out of local optimization and global exploration. The algorithm is simulated on 16 benchmark functions, and compared and analyzed with four particle swarm correlation algorithms and other four swarm intelligent optimization algorithms, which verifies that the proposed improved algorithm has stronger convergence performance and stability.

Key words: particle swarm optimization, Circle map, sine cosine factor, random search strategy, sine cosine algorithm, whale optimization algorithm