Laplace crossover and random replacement strategy boosted Harris hawks optimization: performance optimization and analysis

Abstract

Harris hawks optimization has been a popular swarm intelligence algorithm in recent years. In order to improve the local exploitation ability of the algorithm and improve the problem of slow convergence speed, an enhanced Harris hawks optimization algorithm based on Laplace crossover and random replacement strategy is proposed. This variant uses two optimization mechanisms. Firstly, Laplace crossover is added to enhance the exploitation ability of the algorithm. At the same time, the random replacement strategy is introduced into the original algorithm, which accelerates the convergence speed. The basic functions, IEEE CEC2011 and IEEE CEC2017 functions are used for algorithms comparison, balance diversity analysis, and high-dimensional experiments to verify the superiority of the algorithm proposed in this paper. The experimental results show that the improved algorithm has the advantages of strong optimization ability, high convergence accuracy, and fast convergence speed. The algorithm has solved five engineering design problems using these advantages and can effectively deal with constraint problems.