直接位置更新策略的试变异粒子群优化算法及其在可靠性优化中的应用

Direct Position Updating-based Trying-mutation Particle Swarm Optimization Algorithm and its Application on Reliability Optimization

为了提升粒子群优化(Particle swarm optimization,PSO)算法的全局寻优能力,增强PSO算法在处理复杂的、高维的、多模态优化问题的寻优性能,提升PSO算法在可靠性优化应用中的优化效果,提出了一种基于直接位置更新策略的试变异(Direct position updating-based trying-mutation PSO,DTPSO)算法。设计了直接位置更新策略和试变异策略,有效维持了种群的多样性,维持了探索和开发的平衡,提升了获得全局最优解的概率。通过9种复杂测试函数的验证和比较,证明了DTSPO算法设计的合理性,以及算法拥有的优异的全局寻优能力。对可靠性冗余分配和可靠度分配问题进行了优化,并和其它先进的改进算法进行比较,结果证明了DTPSO算法的稳定性和寻优性能。

In order to improve the global optimization ability of particle swarm optimization(PSO),enhance the performance of PSO in dealing with those complex,high-dimensional,multimodal optimization problems,and furthermore,promote the optimization effect in reliability optimization applications,a direct position updating-based trying-mutation PSO(DTPSO)is proposed in this paper.In this algorithm,the direct position updating strategy and trying mutation strategy are designed,which can effectively maintain the diversity of population,balance the exploitation and exploration,and increase the probability of obtaining the global optimal solution.After being verified and compared by 9 complex test functions,the rationality of DTPSO algorithm design and the excellent global optimization performances are proved.In the reliability optimization,the reliability redundancy allocation and reliability allocation are optimized by different advanced improved algorithms,and the comparison results prove the stability and optimization performance of DTPSO.