ECOA: An Enhanced Crayfish Optimization Algorithm for Real-World Engineering Problems

The Crayfish Optimization Algorithm (COA) is a new metaheuristic algorithm that has been proposed in recent years. While it has a strong local search ability, its global search ability is weak. this paper presents an improved version called ECOA. Firstly, a novel scaling factor is introduced into lens imaging learning to enhance the diversity of the population. Secondly, a dynamic convergence curve is proposed, which broadens the search range and accelerates the convergence speed. Thirdly, a novel feeding strategy is proposed to enhance the local optimization ability of the algorithm. The performance of ECOA is demonstrated through the reducer design problem. The results show that the proposed algorithm has better global search ability and local search ability than the other four state-of-the-art metaheuristic algorithms.