多种群并行协作的粒子群算法

针对高维复杂优化问题在求解时容易产生维数灾难导致算法极易陷入局部最优的问题，提出一种能够综合考虑高维复杂优化问题的特性，动态调整进化策略的多种群并行协作的粒子群算法。该算法在分析高维复杂问题求解过程中的粒子特点的基础上，建立融合环形拓扑、全连接形拓扑和冯诺依曼拓扑结构的粒子群算法的多种群并行协作的网络模型。该模型结合3种拓扑结构的粒子群算法在解决高维复杂优化问题时的优点，设计一种基于多群落粒子广播-反馈的动态进化策略及其进化算法，实现高维复杂优化环境中拓扑的动态适应，使算法在求解高维单峰函数和多峰函数时均具有较强的搜索能力。仿真结果表明，该算法在求解高维复杂优化问题的寻优精度和收敛速度方面均有良好的性能。

Particle Swarm Optimization Algorithm Based on Multigroup Parallel Cooperation

Aiming at the problem that high-dimensional complex optimization problems are prone to dimension disaster, which makes the algorithm easily fall into local optimization, a particle swarm optimization algorithm based on multigroup parallel cooperation is proposed, which can comprehensively consider the characteristics of high-dimensional complex optimization problems and dynamically adjust the evolution strategy. Based on the analysis of the characteristics of particles in the process of solving high-dimensional complex problems, the network model of multigroup parallel cooperation of particle swarm optimization algorithm (PSO) which integrates ring topology, fully connected topology and von Neumann topology is established. The model combines the advantages of three kinds of topology particle swarm optimization algorithm in solving high-dimensional complex optimization problems, designs a multigroup particle broadcast feedback dynamic evolution strategy, and designs an evolutionary algorithm to realize the dynamic adaptation of topology in high-dimensional complex optimization environment, so that the algorithm has strong search ability in solving high-dimensional unimodal function and multi-modal function. The simulation results show that the algorithm has good performance in the optimization accuracy and convergence speed of solving high-dimensional complex optimization problems.