Swarm Intelligence and Bio-inspired Computing - MATLAB Implementation of Hybrid Frog Leaping Algorithm Code

Swarm intelligence and bio-inspired computing represent innovative computational approaches that simulate and leverage intelligent behaviors from biological systems to solve complex problems using computer technology. One application of swarm intelligence is the Hybrid Frog Leaping Algorithm, which combines characteristics of the original frog leaping algorithm with other optimization techniques to enhance the search for optimal solutions. The MATLAB implementation of this algorithm involves several key components: population initialization with random frog positions, fitness evaluation using objective functions, grouping frogs into memeplexes based on fitness rankings, and performing local search within each memeplex. The algorithm incorporates crossover operations between memeplexes to facilitate global information exchange, followed by periodic population shuffling to maintain diversity. Key MATLAB functions used in this implementation include: - rand() for generating initial population positions - sort() for ranking frogs based on fitness values - mod() and floor() for memeplex grouping operations - min() and max() for boundary constraint handling - Custom local search functions implementing frog position updates This MATLAB code implementation provides a practical framework for understanding and applying the hybrid frog leaping algorithm, featuring modular structure with separate functions for initialization, local search, global shuffling, and convergence checking. The implementation includes parameter tuning options for population size, iteration limits, and local search depth to accommodate various optimization scenarios.