Particle swarm optimization (PSO) has been widely used in engineering optimization because of its simple structure and easy implementation. However, standard PSO and most of its variants mainly learn from the personal best position and the global best position. Thus, they often fail to preserve and reuse population-level knowledge generated during the search process. This problem becomes more evident when the search state changes or the swarm falls into stagnation, at which point useful search information may be ignored or forgotten. To address this issue, this paper proposes a state-adaptive knowledge recall PSO algorithm, termed SKRPSO. It includes three cooperative components. First, a state-aware adaptive aggregation mechanism adjusts the elite knowledge-pool size according to population dispersion and builds a rank-weighted knowledge vector for stable population-level guidance. Second, a stagnation-driven knowledge recall mechanism stores historical knowledge associated with global improvements in a bounded memory buffer and recalls recently successful knowledge with a time-decay preference when stagnation is detected. Third, a knowledge-fusion position update strategy uses current aggregated knowledge during normal search and recalled knowledge under stagnation, balancing local exploitation and stagnation escape. Experiments on the CEC2017 benchmark suite show that, based on 30 independent runs, SKRPSO achieves the best mean error on 22 of 29 functions and the best overall Friedman average rank of 1.431 among all compared algorithms. Engineering design results further indicate stable performance.
Zhang et al. (2026) studied this question.