An improved crow search algorithm based on oppositional forgetting learning

摘要

Crow search algorithm (CSA) is a novel meta-heuristic optimization algorithm based on the intelligent behavior of the crow population. Although the algorithm has the characteristics of few parameters, simple structure, and easy application, it has the shortcomings of low convergence accuracy and imbalance between exploration and exploitation capabilities. The occurrence of these issues is originated from crow learning from only one goal. In this paper, an improved crow search algorithm based on oppositional forgetting learning (OFLCSA) is proposed. In order to solve the shortcomings of CSA, the forgetting mechanism is introduced to help the algorithm jump out of the local optimum. Moreover, the opposition-based learning (OBL) strategy is combined with the forgetting mechanism to increase the probability of approaching the optimal solution. In addition, the elite crow and adaptive flight length are used to improve the convergence accuracy. To verify the performance of OFLCSA, experiments were conducted on the Congress on Evolutionary Computation (CEC) 2014 and CEC 2019 benchmark functions. OFLCSA is compared with the ten state-of-the-art meta-heuristic optimization algorithms. Moreover, OFLCSA is evaluated by four real-world engineering applications. Experimental results and analysis show that OFLCSA is a competitive meta-heuristic optimization algorithm.