To address the limitations of the Crow Search Algorithm regarding its susceptibility to local optima and slow convergence speed when handling high-dimensional complex problems, this study proposes six enhanced variants, encompassing ACSA-1 through ACSA-3 and ACSA-LEVY-1 through ACSA-LEVY-3, which integrate adaptive mechanisms, Lévy flight strategies, and a unified guidance strategy. These variants employ linear and piecewise functions to dynamically adjust flight length fl and awareness probability AP, incorporate Lévy flight perturbations, and utilize a probabilistic global-best guidance mechanism to synergistically balance global exploration and local exploitation. The performance of the proposed algorithms is rigorously evaluated across CEC benchmark functions, the CEC2019 test suite comprising Storn’s Chebyshev Polynomial Fitting and Inverse Hilbert Matrix problems, three classical engineering design problems, and a complex damper allocation problem based on nonlinear finite element time-history analysis. Experimental results demonstrate that the improved variants significantly outperform the original CSA and other well-established metaheuristic algorithms in terms of solution quality and robustness. Specifically, for the ill-conditioned Inverse Hilbert Matrix problem, the mean error is reduced from 511.46 to 161.30, representing a reduction of approximately 68.5%; in the pressure vessel design problem, the cost is lowered by 5.5%. Notably, in the large-scale seismic damper allocation problem, the proposed method achieves superior structural response indices in only 237s, standing in stark contrast to the 22,561s required by the Genetic Algorithm. The Wilcoxon signed-rank test with Bonferroni correction yielding a p-value less than 0.008333 confirms the statistical significance of these improvements. These findings highlight that the proposed enhancements effectively resolve stagnation issues without increasing asymptotic computational complexity, demonstrating strong potential for real-world engineering applications.
Lan et al. (Fri,) studied this question.