A Hybrid Population-Based Algorithm Integrating Particle Swarm Optimization and Gravitational Search Algorithm

This program introduces a hybrid population-based algorithm that integrates Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA). The primary objective is to enhance the synergistic capabilities of combining PSO and GSA algorithms. The algorithm was developed to address the limitations of traditional PSO and GSA approaches when solving complex optimization problems. By strategically merging both algorithms, it leverages their respective advantages - PSO's velocity-based exploration and GSA's mass-inspired gravitational forces - to improve problem-solving effectiveness. The implementation includes an adaptive mechanism that dynamically adjusts parameters based on problem characteristics, enabling better adaptation to diverse problem types. During experimentation, the algorithm was tested on multiple benchmark functions and compared against conventional PSO and GSA algorithms. The results demonstrate superior performance and faster convergence rates when solving complex problems. Consequently, this hybrid algorithm shows significant application potential across various domains including engineering design, machine learning parameter tuning, and computational finance.