一种改进的多目标粒子群优化算法及其应用

针对多目标粒子群优化算法在求解约束优化问题时存在难以兼顾收敛性能和求解质量这一问题,提出一种基于免疫网络的改进多目标粒子群优化算法.该算法通过免疫网络互通种群最优信息达到粒子群算法与人工免疫网络算法的协同搜索,同时给出了速度迁移策略、自适应方差变异策略和基于聚类的免疫网络策略.最后将所提出的方法应用于求解电弧炉供电优化模型,达到了减少电量消耗、缩短冶炼时间、延长炉衬使用寿命的目的,同时表明了该算法的有效性.

An improved multi-objective particle swarm optimization algorithm and its application

Considering that the multi-objective particle swarm optimization(MOPSO)algorithm can not give simultaneously attention to convergence performance and solutions quality when it deals with constrained optimization problems,an improved MOPSO algorithm based on immune network(IN-MOPSO) is proposed.In IN-MOPSO,the information of populations exchange through immune network in IN-MOPSO in order to achieve cooperative search of both MOPSO and artificial immune network(AIN) for solution space.Meanwhile,an improved migration method of particle velocity,an improved adaptive variance mutation method and clustering immune network are proposed in order to enhance the function of MOPSO and AIN.The global convergence properties and convergence rate of the improved algorithm are analyzed and described.Finally,the algorithm is applied to optimize the steelmaking process in practice,which reduces the electric energy consumption,shortens smelting time and improves lifetime of the furnace lining.The result shows the effectiveness of the algorithm.