Adaptive dynamic crayfish algorithm with multi-enhanced strategy for global high-dimensional optimization and real-engineering problems

The Crayfish Optimization Algorithm (COA) is a recent powerful algorithm that is sometimes plagued by poor convergence speed and a tendency to rapidly converge to the local optimum. This study introduces a variation of the COA called Adaptive Dynamic COA with a Locally enhanced escape operator (AD-COA-L) to tackle these issues. Firstly, the algorithm utilizes the Bernoulli map initialization strategy to quickly establish a high-quality population that is evenly distributed. This helps the algorithm to promptly reach the proper search area. Additionally, in order to mitigate the likelihood of getting trapped in local optima and improve the quality of the obtained solution, an Adaptive Lens Opposition-Based Learning (ALOBL) mechanism is applied. Moreover, the local escape operator (LEO) is utilized to aggressively discourage the adoption of isolated solutions and encourage the sharing of information within the search area. Finally, a new inertia weight is suggested to improve the search capability of COA and prevent it from being stuck in local optima by enhancing the exploitation capability of COA. AD-COA-L is evaluated against eight advanced state-of-the-art variations and ten classical and recent metaheuristic algorithms on 29 benchmark functions from CEC2017 of varying dimensions (50 and 100). AD-COA-L demonstrates superior accuracy, balanced exploration-exploitation and convergence speed, compared to other algorithms across most benchmark functions. Furthermore, we evaluated the proficiency of AD-COA-L in tackling seven demanding real-world and restricted engineering optimization challenges. The experimental findings clearly illustrate the competitiveness and advantages of the proposed AD-COA-L algorithm.