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

摘要： 针对粒子群算法在搜索寻优过程中存在收敛速度慢、精度低和易陷入局部最优等问题，提出了引入Circle映射和正弦余弦因子的改进粒子群算法。采用引入Circle映射的种群初始化操作，能获得更加均匀和多样的初始种群，有利于提高算法的收敛速度和精度。采用非线性递减的惯性权重并引入正弦余弦算法中的正弦余弦因子的策略，更好地平衡了算法全局探索能力和局部开发能力。受鲸鱼优化算法启发，提出并采用带有淘汰制的随机搜索策略，增强了算法跳出局部最优和全局探索的能力。在16个基准测试函数上对该算法进行了仿真实验，并与4种粒子群相关算法及其他4种群智能优化算法进行了比较和分析，验证了所提出的改进算法具有更强的收敛性能和稳定性。

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