Abstract: To efficiently solve the reliability optimization problem of Multi-State System(MSS) with heterogeneous redundancy and make up for the lack of premature convergence of Particle Swarm Optimization (PSO),the algorithm was improved from two aspects: force mode and population topology.The single force mode in the particle swarm optimization algorithm was improved,and the front and rear search stages were set.Corresponding to the two search stages,the balanced repulsion force mode and the double-layer gravity (individual and global optimal solution gravity and intermediate fitness particle gravity) mode were constructed respectively,and the Stage-search Particle Swarm Optimization (SPSO) algorithm was proposed;the Survival Evolution (SE) topology was constructed by using the survival system of "choosing friends" and survival of the fittest.The topology was integrated into the stage-search particle swarm optimization algorithm in the parallel way of structural evolution and algorithm evolution,and the SPSO-SE algorithm was proposed to further improve the optimization performance of the algorithm;The benchmark function was used to test and compare the proposed algorithm with the variant algorithms of PSO,and the results showed that the proposed algorithm had better optimization ability.The proposed algorithm was used to optimize the Reliability Redundancy Allocation Problem (RAP) of multi-state systems with series parallel and bridge structures,and the system structure with lower cost and higher reliability was obtained。

Key words: heterogeneous redundancy, multi-state system, particle swarm optimization, force mode, survival and evolution, Benchmark function, reliability redundancy allocation problem

摘要： 为高效解决含有异质冗余的多态系统(MSS)可靠性优化问题,并弥补微粒群优化(PSO)算法易早熟收敛的不足,从作用力方式和种群拓扑结构两方面对算法进行改进。改进PSO算法中单一的作用力方式,设置前后两个搜索阶段,对应两个搜索阶段分别构造平衡引斥力方式和双层引力(个体和全局最优解引力、中间适应度微粒引力)方式,提出阶段性搜索微粒群(SPSO)算法;利用生物个体“择友而交”和优胜劣汰的生存体系构建生存进化(SE)拓扑结构,以结构演化和算法进化并行方式将该拓扑结构融入SPSO算法,提出生存进化阶段性搜索微粒群(SPSO-SE)算法,进一步提升算法的优化性能;利用Benchmark函数对所提算法与PSO的改进算法进行测试对比,结果表明,所提SPSO-SE算法具有更好的寻优能力。采用SPSO-SE算法对串-并联和桥式结构的多态系统的可靠性冗余分配问题进行优化,得到的系统结构费用更低、可靠度更高。

关键词: 异质冗余, 多态系统, 微粒群优化算法, 作用力方式, 生存进化, Benchmark函数, 可靠性冗余分配问题优化