基于邻域混沌PSO算法的目标分配优化方法

Targets Assignment Approach Based on Neighborhood Chaos Searching Particle Swarm Optimization

针对基本粒子群算法在求解火力打击体系目标分配问题时易陷入局部极值、计算精度差的局限性,提出了一种基于混沌粒子群算法(Chaos Particle Swarm Optimization,CPSO)的目标分配优化方法。在综合考虑整体毁伤效能、打击匹配度和风险概率的基础上,分析了目标分配问题的数学模型,设计了相应的粒子编码方法、更新策略和有效性修订方法,提出一种在种群最优粒子邻域内进行混沌搜索的改进策略。仿真结果表明,所提CPSO算法的性能明显优于基本粒子群算法和变异粒子群算法。

To conquer the limitation of basic particle swarm optimization （PSO） ,which has low convergence precision and easily runs in- to local extremum when solving targets assignment problems, an algorithm based on Chaos PSO （CPSO） is introduced. By taking global strike efficiency, strike match degree and risk probability into account,the mathematic model of Wxgets assignment problems are analyzed, the encoding method,updating strategy and validity correcting are designed,whereas a chaos optimization search method nearby optimum particle is introduced. Lastly ,the simulation results indicate the performance of proposed CPSO, which is obviously superior to basic PSO and mutation PSO.