新的MOPSO及其在大型复杂系统可靠性优化中的应用

可靠性优化问题是大型复杂系统设计的一个关键问题。针对大型复杂系统多个指标(可靠度、造价和冗余数)同时进行最优分配的结果多样性不好的问题,提出了一种基于杂草克隆的多目标粒子群算法—IWMOP-SO(invasive weed multi-objective particle swarm optimization)的多指标分配方法。该分配方法通过引入杂草克隆机制来改善Pareto最优解的收敛性和多样性。通过对大型复杂系统多个指标进行分配,其分配效果与NSGA-Ⅱ相比,得到的Pareto非劣解集多样性和均匀性好,分布范围更广,更利于设计者进行决策,是一种更有效的复杂系统多指标分配方法。

Novel MOPSO and its application in reliability optimization of large complex systems

Reliability optimization problem is a key problem of designing the large complex system.As there existed a diversity problem in the multi-indexes(such as reliability,cost and redundancy) allocation optimization,this paper proposed a novel multi-indexes allocation algorithm-IWMOPSO based on the multi-objective optimization.The algorithm introduced invasive weed mechanism in order to better the convergence and the diversity of the Pareto front.It utilized the algorithm to allocate the multi index of the large complex systems.In order to indicate its effectiveness and efficiency,the achieved Pareto front was compared with the result achieved by NSGA-Ⅱ.It turns out that the new algorithm gets a better diversity and convergence,and it is more convenient for designers to make a decision,therefore,it is a more effective multi-objective allocation algorithm of large complex systems.