Abstract: Aiming at the problems such as slow convergence speed and easy to fall into local optimum when the original crayfish optimization algorithm is solving optimization problems, a crayfish optimization algorithm with multi-strategy collaborative optimization is proposed. By constructing a three-dimensional collaborative optimization framework that enhances population diversity, continuously regulates behavior, and improves local escape capability, the performance of the algorithm is fundamentally enhanced from the essence of the optimization mechanism. Firstly, multi-scale Chebyshev chaotic mapping and direction-aware mechanisms are introduced to collaboratively enhance population diversity, thereby achieving a dynamic balance between global exploration and local development. Secondly, an adaptive temperature regulation and smooth transition mechanism is proposed. Through a dynamic temperature regulation strategy based on population state, the behavioral mutation problem caused by traditional hard threshold switching is effectively alleviated, and the iterative continuity is enhanced. Finally, the Corchy mutation perturbation is applied to the optimal individual of the population and combined with the greedy strategy. The repeated tail characteristic of the Corchy distribution is utilized to expand the local search range, and the greedy selection is combined to retain the high-quality solution, effectively enhancing the algorithm's ability to escape from the local optimum. Through the optimization simulation experiments of the CEC2022 standard test set and eight typical benchmark test functions, a comprehensive evaluation was conducted from multiple aspects such as convergence, robustness, and Wilcoxon rank sum test, and compared with seven mainstream optimization algorithms. The experimental results show that the improved crayfish optimization algorithm has been enhanced in the three key indicators of optimization accuracy, convergence speed and algorithm stability. In addition, the improved crayfish optimization algorithm was applied to solve three typical engineering optimization problems, which proved the feasibility and efficiency of the proposed algorithm in solving practical engineering optimization problems.