An adaptive improved gray wolf optimization algorithm with dynamic constraint handling for mechanism-constrained optimization problems

Abstract. To address constrained optimization problems in mechanical design, this study proposes an enhanced gray wolf optimization (GWO) algorithm. First, a novel individual memory optimization strategy is developed to expand the population's exploration scope and mitigate the risk of individuals pursuing misguided search trajectories. Second, a position update strategy incorporating differential variation is proposed to balance the local and global search capabilities of individual populations. Lastly, a discrete crossover strategy is proposed to promote information diversity across individual dimensions within the population. By integrating these three improvement strategies with the GWO, a novel improved gray wolf optimization (IGWO) algorithm is developed, which not only preserves robust global and local search capabilities but also demonstrates accelerated convergence performance. To validate the effectiveness, feasibility, and generalizability of the proposed algorithms, three representative mechanical design optimization cases and the Z3 parallel mechanism scale parameter optimization case were employed. Empirical findings reveal that the IGWO algorithm effectively resolves the targeted optimization problem, demonstrating superior performance relative to other benchmark algorithms in comparative analyses.