采用概率选择的自适应多目标粒子群算法

针对多目标粒子群算法进行了收敛性和分布性分析，提出了一种应用概率分配的自适应调整惯性因子的粒子群优化算法。该算法通过粒子非劣排序的支配等级，设定个体的适应度数值，为增强最优解集的分散性，采用拥挤距离对适应度进行惩罚，进而根据概率选择比较获取相应的最优个体;同时算法根据粒子个体所处位置以及相应的迭代次数，对惯性因子进行了自适应调整，增强了算法的收敛性。最后通过测试函数对改进算法进行了效果验证，表明了算法的有效性。

Adaptive multi-objective particle warm algorithm using probability assignment

After analyzing convergence and diversity of the multi-objective Particle Swarm Optimization （PSO）, an improved multi-objective PSO, which is introduced the method of probability assignment, is proposed. The arithmetic calculates the fitness of the particles basis of the dominative rank using the Pareto sorting. In order to increase the diversity of the optimal solution, a penalty function including the crowding distance is added to the fitness. Then the best particle is selected according to the probability comparing. Also the inertia coefficients are adjusted adaptively based on the information of the particles and the iterative number performances. So the convergence rate of the arithmetic can be accelerated. At last the validity of the arithmetic is validated via the testing function.