Enhanced Krill Herd Algorithm Using Shuffled Frog Leaping and Meme Grouping for Multi-Objective Optimization Problems

Abstract

To solve the low performance problem of krill herd algorithm in the face of multi-modal optimization problems, this study proposes an improved krill herd algorithm based on a hybrid frog leaping algorithm and meme grouping method. This study analyzes the global optimization and local distribution behavior characteristics of the krill herd algorithm. Then, combined with the hybrid frog leaping algorithm, the krill individuals are optimized through meme grouping to enhance the algorithm's global and local search capabilities. This study conducted MATLAB simulation experiments to test the Schaffer function and Griebank function, and compared the results with traditional krill herd algorithms. The results showed that the improved algorithm began to converge in the search of Schaffer and Griebank functions at the 32nd and 68th iterations, and basically completed convergence at the 64th and 130th iterations, with minimum errors of 3% and 5%, respectively. The minimum errors of traditional krill herd algorithms were 5% and 8%. This study further validated the algorithm through logistics scheduling and showed that the optimized algorithm shortened the completion time of scheduling tasks by 3 hours and reduced costs by 13,500 yuan. Research has shown that the proposed method performs outstandingly in improving global optimization capability and computational efficiency, and has practical application value.