一种基于多样性信息和收敛度的多目标粒子群优化算法

为了提高多目标粒子群算法优化解的多样性和收敛性，提出了一种基于多样性信息和收敛度的多目标粒子群优化算法（Multiobjective Particle Swarm Optimization based on the Diversity Information and Convergence Degree，dicdMOPSO）.首先，利用非支配解多样性信息评估知识库中最优解的分布状态，设计出一种全局最优解选择机制，平衡了种群的进化过程，提高了非支配解的多样性和收敛性；其次，基于种群多样性信息设计出一种飞行参数调整机制，增强了粒子的全局探索能力和局部开发能力，获得了多样性和收敛性较好的种群.最后，将dicdMOPSO应用于标准测试函数测试，实验结果表明，dicdMOPSO与其他多目标算法相比不仅获得了多样性较高的可行解，而且能够较快的收敛到Pareto前沿.

A Multiobjective Particle Swarm Optimization Algorithm Based on the Diversity Information and Convergence Degree

To improve the diversity and convergence of optimal solutions in multiobjective particle swarm optimization (MOPSO) algorithm,a multiobjective particle swarm optimization algorithm,based on the diversity information and convergence degree,named dicdMOPSO,is developed in this paper.Firstly,a global optimal solution selection mechanism,based on the distribution of optimal solutions in the knowledge base with the diversity information of non-dominated solutions,is introduced to balance the evolutionary process of population to improve the diversity and convergence of non-dominated solutions.Then,to enhance global exploration and local exploitation abilities of particles,a flight parameter adjustment mechanism is proposed to obtain the particles with better diversity and convergence by using the population diversity information.Finally,the experiment results demonstrate that,compared with other multiobjective algorithms,this proposed dicdMOPSO algorithm can not only obtain the optimal solutions with better diversity,but also be faster to catch the Pareto front.