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An Enhanced Lévy-Based Whale Optimization Algorithm for Global Optimization

Whale optimization algorithm(WOA) is often used to solve engineering optimization and real parameter optimization problems. However, the original WOA algorithm has some problems. First, The position iteration mechanism based on the optimal whale makes the algorithm lack of exploration ability. In addition, due to the lack of mechanisms to guide the algorithm to escape from the local optimum, the algorithm is prone to stall prematurely. To solve these problems, an Enhanced Lévy-Based whale Optimization Algorithm(ELWOA) is proposed in this paper, which uses the Lévy flight mechanism to enhance the in-depth exploration capability of the algorithm and introduces a population diversity maintenance strategy to guide the population to escape from the local optimum. Finally, its performance is verified by the CEC2017 test suite, and the results show that it has a strong advantage in the convergence of the error and convergence speed than the other three algorithms.