An Improved Crayfish Optimization Algorithm with Multi-Strategy Collaboration

Abstract

The crayfish optimization algorithm (COA) is competitive in solving continuous optimization problems, but its performance deteriorates in high-dimensional and multimodal environments because random initialization may provide uneven population coverage, exploration relies excessively on the current best solution, and exploitation lacks effective multi-source information interaction. To address these limitations, this study proposes a multi-strategy improved crayfish optimization algorithm (ICOA). First, an enhanced Kent chaotic map with random perturbation is used to initialize the population, thereby improving ergodicity and spatial diversity. Second, an adaptive grid-based multi-cave strategy incorporating Monte Carlo sampling dynamically constructs an elite cave set and assigns target caves according to load-aware probability distributions. This probabilistic mechanism broadens global exploration and reduces premature population aggregation. Third, a multi-source crossover operator combining adaptive frequency rank encoding with chaotic self-attention reconstructs interactions among individuals while preserving the physical scale and boundary feasibility of candidate solutions. The contribution of each strategy is examined through ablation experiments. ICOA is further compared with COA, PSO, GWO, MVO, and BOA on six challenging benchmark functions over 30 independent runs, and statistical significance is evaluated using the Wilcoxon rank-sum test. The proposed method is also applied to tension/compression spring and pressure vessel design problems. Experimental results show that ICOA consistently improves solution accuracy, convergence behavior, robustness, and the ability to escape local optima, while achieving the best optimization results in both constrained engineering applications. These findings demonstrate that the collaborative strategies provide an effective balance between global exploration and local exploitation for complex continuous optimization tasks.