Abstract: Aiming at the problem that Multi-Objective Particle Swarm Optimization (MOPSO) was easy fell into partial optimal solution,a Territorial Behavior-based Multi-Objective Particle Swarm Optimization (TBMOPSO) was proposed.Through imitating the three subbehaviors of occupying territory,self-improvement and finding new territory in animal territorial behavior,the dynamic update of territory radius was presented to widen search scope of the population|the crossover operation and Gauss mutation were applied to increase  the population diversity and improve the search ability|the one-total probability and the expectation-variance probability were proposed to enhance the efficiency of particle search,which achieved the optimization of MOPSO.An improved TOPSIS was applied to assess and select the Pareto optimal solutions.The proposed algorithm was applied to the optimum design of plate-fin heat exchanger,and the resonable parameters were obtained.

Key words: territorial behavior, particle swarm optimization, plate-fin heat exchanger, technique for order preference by similarity to ideal solution

摘要： 针对多目标粒子群算法易于陷入局部最优解的问题,提出基于领地行为的多目标粒子群算法。通过模仿动物领地行为中的领地占领、自我完善和寻找新领地三种子行为,提出领地半径动态更新技术来扩大种群搜索范围,采用交叉操作和高斯变异增加种群多样性和增强搜索能力,提出定总概率和期差概率提高粒子寻优的有效性,实现对多目标粒子群算法的改进。同时采用改进的逼近理想解法群体多属性决策方法,实现对Pareto最优解的多属性模糊评价与优选。将算法应用于板翅式换热器多目标综合设计,可以快速、准确地获得合理的结构参数。

关键词: 领地行为, 粒子群算法, 板翅式换热器, 逼近理想解法