A multi-strategy fusion-based Rat Swarm Optimization algorithm

Abstract

As a new metaheuristic algorithm, the Rat Swarm Optimization (RSO) has been increasingly applied to solve practical problems. However, RSO still suffers from slow convergence speed and easy trapping into local optima, especially for large-scale optimization problems. To overcome these drawbacks, a multi-strategy improved Rat Swarm Optimization algorithm with Whale Optimization Algorithm (MSRSO-WOA) is proposed. First, a segmented chaotic mapping is used to initialize the population to improve the quality of initial solutions. Second, a cosine oscillation weight is added to the position update process of the rat swarm, and new nonlinear exploration parameters and Levy flight development parameters are used to increase the convergence speed and exploration ability of the algorithm. Finally, the whale bubble spiral position update method of the Whale Optimization Algorithm is incorporated into RSO to improve the local search capability of the algorithm. The performance of MSRSO-WOA is evaluated by 23 well-known benchmark functions, 10 CEC testing functions, and 3 practical engineering problems. The results show that MSRSO-WOA has better optimization performance and stronger robustness than other compared algorithms.