Multi-Stage Improvement of Marine Predators Algorithm and Its Application

Abstract

The metaheuristic algorithms are widely used in solving the parameters of the optimization problem. The marine predators algorithm (MPA) is a novel population-based intelligent algorithm. Although MPA has shown a talented foraging strategy, it still needs a balance of exploration and exploitation. Therefore, a multi-stage improvement of marine predators algorithm (MSMPA) is proposed in this paper. The algorithm retains the advantage of multistage search and introduces a linear flight strategy in the middle stage to enhance the interaction between predators. Predators further away from the historical optimum are required to move, increasing the exploration capability of the algorithm. In the middle and late stages, the search mechanism of particle swarm optimization (PSO) is inserted, which enhances the exploitation capability of the algorithm. This means that the stochasticity is decreased, that is the optimal region where predators jumping out is effectively stifled. At the same time, self-adjusting weight is used to regulate the convergence speed of the algorithm, which can balance the exploration and exploitation capability of the algorithm. The algorithm is applied to different types of CEC2017 benchmark test functions and three multidimensional nonlinear structure design optimization problems, compared with other recent algorithms. The results show that the convergence speed and accuracy of MSMPA are significantly better than that of the comparison algorithms.