基于最优交叉的广泛学习粒子群优化

Comprehensive Learning Particle Swarm Optimization Based on Optimal Crossover

粒子群优化算法实现简单、便于操作,近年来已被广泛应用于资源分配等大规模复杂问题,但算法收敛速度慢、求解精度低等问题也制约着其进一步应用。针对以上问题,引入遗传算法的染色体交叉特性,结合广泛学习粒子群优化算法,提出一种基于最优交叉的广泛学习粒子群优化算法。通过全局最优粒子位置与个体历史最优位置执行最优交叉操作得到更优个体,加快算法收敛速度,提高对问题的求解精度。基准测试函数实验结果表明,该算法相较原算法具有更快的收敛速度和优化精度,同时Friedman检验和Wilcoxon符号秩检验结果表明,基于最优交叉的广泛学习粒子群优化算法具备较好的稳定性,优化精度和收敛速度有了较大提升。

Particle swarm optimization(PSO)algorithm has been widely used in large-scale complex problems such as resource allocation in recent years because of its simple implementation and easy operation.However,the slow convergence speed and low solution accuracy of the algorithm also restrict its further application.In order to solve the above problems,this paper introduces the chromosome crossing characteris⁃tics of genetic algorithm,and combines with comprehensive learning particle swarm optimization,proposes a comprehensive learning particle swarm optimization based on optimal crossing.It can improve the convergence speed of the algorithm and the accuracy of solving the problem by performing the optimal crossover operation between the global optimal particle position and the historical optimal position of the individual to obtain a better individual.The experimental results of benchmark function show that the proposed algorithm has faster convergence speed and optimization accuracy than the original algorithm,and the results of Friedman test and Wilcoxon signed-rank test show that the proposed algorithm has better advantages than other comparison algorithms.