【摘要】 参数不确定优化问题是实践中经常遇到的复杂优化问题,现有方法多针对单目标函数的情况.本文利用微粒群优化算法解决含区间参数多目标优化问题,提出一种基于概率支配的多目标微粒群优化算法.该算法通过定义概率支配关系,比较所得解的优劣;基于σ区间值,选择微粒的全局极值点,并给出新的微粒个体极值点及外部储备集的更新策略.与传统多目标微粒群优化算法比较,仿真结果表明本文所提算法的有效性.更多还原

【Abstract】 Optimization problems with uncertainties are a kind of familiar and complicated optimization problems,and most of the existing methods only deal with the case of a single-objective function.To solve multi-objective optimization problems with interval parameters by particle swarm optimization,a multi-objective particle swarm optimization algorithm based on probable dominance is proposed in this paper.In the algorithm,the probable dominance and the a intervals are presented to compare the quality of solutions and select the globally optimal particles,respectively.A novel strategy for updating locally optimal particles and exterior archive are put forward.The feasibility of the proposed algorithm is validated by simulation results.更多还原