基于随机黑洞和逐步淘汰策略的多目标粒子群优化算法

提出一种基于随机黑洞粒子群算法(RBH-PSO) 和逐步淘汰策略的多目标粒子群优化(MRBHPSO-SE) 算法. 利用RBH-PSO 全局优化能力强和收敛速度快的优点逼近Pareto 最优解; 为了避免拥挤距离排序策略的缺陷, 提出逐步淘汰策略, 并将其应用到下一代粒子的选择策略中. 同时, 动态选择领导粒子, 运用动态惯性权重系数和变异操作 来增强种群全局寻优能力, 以及避免早熟收敛. 利用具有不同特点的测试函数进行验证, 结果表明, 与同类算法相比, 该算法具有较高的精度并兼顾优化解的多样性.

Multi-objective Particle Swarm Optimization Algorithm Based on Random Black Hole Mechanism and Step-by-step Elimination Strategy

A multi-objective particle swarm optimization algorithm based on the random black hole particle swarm optimization(RBH-PSO) and step-by-step elimination strategy is proposed. The Pareto optimal solutions are approached by its advantage of speeding up the convergence and improving the performance of global optimizer greatly. To avoid the disadvantage of crowding distance sorting technique, the step-by-step elimination(SE) strategy is proposed, which is used to select the particles from one iteration to another. In addition, dynamic selection of leader particle for each particle, adaptive inertia weight and a special mutation operation are incorporated to enhance the global exploratory capability and avoid premature convergence. The performance of the proposed algorithm is tested on a set of well-known benchmark functions and compared with several representative multi-objective optimization algorithms. Simulation results show that the MRBHPSO-SE algorithm can converge to the global optimal with high accuracy while keeping the good diversity of the Pareto solutions.