交叉反向学习和同粒社会学习的粒子群优化算法

针对社会学习粒子群优化（SLPSO）算法存在的优化效率低、收敛速度慢等问题，提出了一种改进的SLPSO算法，即基于交叉反向学习和同粒社会学习的PSO算法（CPPSO）。首先，将最优解随机纵向交叉与一般反向学习以及随机反向学习构建交叉反向学习；然后，以此交叉反向学习策略更新种群中的最优粒子位置，增强探索能力，并克服SLPSO中最优粒子无更新导致效率低下的缺点；最后，对于非最优粒子，与SLPSO采用基于维的社会学习不同，均采用新型基于粒子的社会学习机制，在提高全局搜索能力同时，更提高开采能力和搜索效率。在一组不同维基准函数上优化的实验结果表明，CPPSO的优化性能、搜索效率和普适性大幅度领先于SLPSO和其他先进的PSO改进算法，如交叉搜索PSO （CSPSO）算法、自我调节的PSO （SRPSO）算法、异构综合学习的PSO （HCLPSO）算法和反向学习和局部学习能力的PSO （RLPSO）算法。

Particle swarm optimization algorithm with cross opposition learning and particle-based social learning

In order to solve the problems of the Social Learning Particle Swarm Optimization (SLPSO) algorithm, such as slow convergence speed and low search efficiency, a Cross opposition learning and Particle-based social learning Particle Swarm Optimization (CPPSO) algorithm was proposed. Firstly, a cross opposition learning mechanism was formulated based on combining general opposition learning, random opposition learning and vertical random cross on the optimal solution. Secondly, the cross opposition learning was adopted for the optimal particle to improve the population diversity, exploration ability and avoid the disadvantage of SLPSO's slow convergence and low search efficiency. Finally, a novel social learning mechanism was adopted for the non-optimal particles in the particle swarm, and the new social learning method used particle-based approach, instead of the dimension-based one of SLPSO, not only improved the exploration capacity, but also improved exploitation and the optimization efficiency. The simulation results on a set of benchmark functions with different dimensions show that the optimization performance, search efficiency and generalizability of the CPPSO algorithm are much better than those of the SLPSO and the advanced PSO algorithms such as Crisscross Search PSO (CSPSO), Self-Regulating PSO (SRPSO), Heterogeneous Comprehensive Learning PSO (HCLPSO) and Reverse learning and Local learning PSO (RLPSO).