一类区间多目标粒子群优化算法

Particle swarm optimization algorithms for interval multi-objective optimization problems

针对区间参数多目标优化问题,提出一种基于模糊支配的多目标粒子群优化算法.首先,定义基于决策者悲观程度的模糊支配关系,用于比较解的优劣;然后,定义一种适于区间目标值的拥挤距离,以更新外部存储器并从中选择领导粒子;最后,对多个区间多目标测试函数进行仿真实验,实验结果验证了所提出算法的有效性.

Aiming at multi-objective optimization problems with interval parameters, a multi-objective particle swarm optimization algorithm based on fuzzy dominance is proposed. Firstly, the fuzzy Pareto dominance relation based on decision-makers’ pessimism degree is defined for comparison of solutions. Then, the crowding distance suitable for interval objectives is defined for updating the external repository and selecting the global particle leaders. Finally, trials are carried out on several interval multi-objective benchmark testing functions, and the results show the effectiveness of the proposed algorithms.