Abstract: PSO algorithm with robust solution searching methodologies is proposed，a new mechanism for expected fitness evaluations is introduced，and a strategy for assigning expected fitness only to the best solutions of particles and their neighbors is proposed，in order to reduce the computational requirements for additional sampling points in the process of expected fitness assignments.Moreover，the neighborhood methodology is redefined in accordance with the goal of the proposed algorithm.Two case studies are reported to validate and demonstrate the feasibilities and advantages of using the proposed algorithm in finding the robust solutions.

摘要： 提出了一种搜索鲁棒优化解的粒子群算法。为解决期望适值函数计算需要大量新采样点而导致的计算效率过低问题，提出了一种期望适值赋值的新机制。该机制只对每一代粒子中的个体最优解和整体最优解分配期望适值。此外，为便于算法搜索鲁棒优化解，重新定义了粒子的邻域关系。最后，通过两个实例计算证明了新算法求解电磁场逆问题鲁棒优化解的可行性和优点。