An enhanced Mayfly optimization algorithm based on orthogonal learning and chaotic exploitation strategy

As a new method proposed to solve optimization problems, the mayfly algorithm that possesses the advantages of other advanced algorithms can play a very sound effect. However, there are still some shortcomings of local optimization and slow convergence speed when dealing with complex optimization problems. In this paper, two effective strategies are first integrated into the basic mayfly algorithm to enhance algorithm performance. Firstly, the orthogonal learning is applied to increase the diversity of primary male mayfly operators to guide the male mayfly to move more steadily, rather than oscillatory. Secondly, the chaotic exploitation is added to form the new position of an offspring to improve search capability. In order to verify the effectiveness of the enhanced algorithm, it is evaluated and compared with other excellent algorithms using benchmark functions. The Wilcoxon test, exploration-exploitation analysis and the time complexity analysis are also performed to analyze whether it yield promising results. In addition, three kinds of engineering optimization problems are also tested in the experiments including with constraints and without constraints. Computational results show that enhanced mayfly optimization algorithm achieves sound performance on all test problems and can attain high-quality solutions for different engineering optimization problems.