Group Search Optimizer (GSO) Algorithm with Standard Test Functions

This article presents the function optimization implementation of the Group Search Optimizer (GSO) algorithm. The program incorporates several standard unimodal and multimodal test functions commonly used for benchmarking optimization algorithms. The implementation demonstrates GSO's capability to effectively handle complex optimization challenges including neural network training and image processing applications. The algorithm operates through a population-based search mechanism where individuals (group members) interact through producer-scrounger roles, utilizing animal foraging behavior principles for efficient exploration of solution spaces. Key functions include fitness evaluation, position updating, and role adaptation mechanisms. Users can run the provided demo to observe GSO's performance characteristics and practical application scenarios, including parameter configuration options and convergence behavior visualization for different test functions.