MATLAB Implementation of Hybrid Frog Leaping Algorithm

Through comprehensive testing, we have successfully implemented and executed the hybrid frog leaping algorithm using MATLAB programming. This heuristic optimization algorithm combines the advantages of both frog leaping algorithm and particle swarm optimization, making it suitable for solving various optimization problems. The implementation includes key functions for population initialization, fitness evaluation, and position updating using both local search strategies from frog leaping and global exploration mechanisms from PSO. During our testing phase, we validated the algorithm's performance across multiple datasets and optimization scenarios, comparing it against other established optimization methods. Our results demonstrate that the hybrid frog leaping algorithm excels particularly in solving specific types of problems, especially those involving high-dimensional data where traditional algorithms might struggle. Furthermore, we discovered that through strategic modifications to parameters such as step size adjustment, population partitioning, and iteration control mechanisms, the algorithm's performance could be significantly enhanced. The MATLAB code implementation effectively handles complex optimization landscapes through adaptive weighting and dynamic neighborhood search strategies. Based on our findings, we conclude that the hybrid frog leaping algorithm presents a highly promising optimization approach worthy of in-depth research and practical applications in computational intelligence.