基于双极偏好控制的多目标粒子群优化算法

考虑双极偏好信息对粒子群的控制作用，提出一种使用双极偏好——正偏好和负偏好引导粒子群向 Pareto 前沿偏好区域进化的方法．根据TOPSIS 决策法思想，将外部种群粒子与正负偏好点的相对贴近度排序作为 外部种群管理和全局最优解更新策略；根据贴近度值确定解集的分布度；选取6 种不同类型的多目标测试函数进行 算法模拟，从世代距离、空间测度和超体积测度3 个指标与基于单极偏好的多目标粒子算法进行性能比较．结果显 示，基于双极偏好控制的多目标粒子群算法的收敛性和综合性能更优秀．

Multi-objective Particle Swarm Optimization Based on Bipolar Preferences Control

Considering the control of bipolar preferences to the particle swarm,a method with the bipolar preferences,positive and negative preferences,is presented to lead the particle to move toward the true Pareto front.The similarities of the non-dominated solutions to bipolar preferences are computed according to TOPSIS(technique for order preference by similarity to ideal solution)decision method,then the similarities are sorted in descending order and the non-dominated solutions ale maintained in the out-archives according to the order.The spread of the solutions are also determined by the similarity.Six different test functions are chosen in the simulation experiment.and this method is compared with the unipolar preferences based MOPSO in generation distance,spacing metric and hyper-volume metric,the final comparison result show the proposed method is better in aspect of convergence and combination property.