Hybrid PSO-GSA Algorithm: A Novel Population-Based Optimization Approach

The Hybrid PSO-GSA Algorithm represents an innovative optimization technique merging two established population-based methodologies: Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA). This hybrid approach strategically combines PSO's intensive local search exploitation with GSA's broad exploration capabilities, creating a balanced optimization framework. The implementation typically involves modifying the velocity update equation to incorporate both PSO's cognitive/social components and GSA's mass-based gravitational forces. For performance validation, the algorithm undergoes rigorous testing using standard benchmark functions, where its solution quality, convergence speed, and stability are compared against standalone PSO and GSA implementations. Key implementation aspects include: - Dynamic parameter adjustment for inertia weights and gravitational constants - Hybrid velocity calculation: v(t+1) = w*v(t) + c1*r1*(pbest-x) + c2*r2*(gbest-x) + GSA_force_component - Fitness evaluation mechanisms for both positional and gravitational updates This comparative analysis demonstrates the hybrid algorithm's enhanced capability in escaping local optima while maintaining rapid convergence toward global solutions, making it particularly effective for complex multimodal optimization problems.