Source Code Implementation of a Chaotic Particle Swarm Optimization Algorithm

This is a highly practical source code implementation of a chaotic particle swarm optimization (CPSO) algorithm. The package includes complete algorithm source code along with several benchmark test functions, enabling users to better understand and apply this optimization technique. The chaotic particle swarm algorithm represents an advanced optimization method particularly effective for solving complex optimization problems. By integrating chaotic sequences with traditional particle swarm behavior simulation, this algorithm achieves improved global search capabilities and avoids premature convergence to local optima. The implementation typically includes key components such as chaos initialization, velocity updating with chaotic factors, and position updating mechanisms. The source code provides a modular structure with functions for population initialization, fitness evaluation, chaos mapping (using logistic maps or similar chaotic systems), and iterative optimization processes. Users can easily apply this CPSO algorithm to various optimization challenges, enhancing problem-solving efficiency and solution accuracy through its balanced exploration-exploitation characteristics. The included test functions cover different optimization scenarios, allowing for comprehensive algorithm validation and performance benchmarking.