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

To solve the low-performance problem of the 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 and Griebank functions and compared the results with traditional krill herd algorithms. The results demonstrated that the enhanced algorithm commenced convergence at the 32nd iteration of the Schaffer function search and reached a minimum error of 3% at the 64th iteration. The conventional Krill foraging optimization algorithm reached convergence at the 72nd iteration with a minimum error of 5%. The convergence of the improved algorithm was improved by 11.1% and the error was reduced by 2%. In the search for the Griewank function, convergence commenced at the 68th iteration and was largely completed at the 130th iteration, with a minimum error of 5%. In comparison, the traditional krill foraging optimization algorithm was completed at the 143rd iteration, with a minimum error of 8%. The convergence of the enhanced algorithm was enhanced by 9.1%, and the error was diminished by 3%. 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. © 2024 Slovene Society Informatika. All rights reserved.