Hybrid Immune Algorithm and Particle Swarm Optimization for Multi-Objective Reactive Power Optimization

In this paper, we investigate the hybrid approach combining Immune Algorithm (IA) and Particle Swarm Optimization (PSO) to solve multi-objective reactive power optimization problems. The hybrid algorithm leverages antibody diversity mechanisms from IA and swarm intelligence from PSO to enhance solution quality. Key implementation aspects include: - Antibody initialization using Pareto dominance criteria - Velocity update equations incorporating immune memory - Non-dominated sorting for multi-objective handling This integrated approach enables better exploration of solution space while minimizing reactive power losses and improving power system stability. The algorithm employs clustering techniques to maintain solution diversity and archive elite solutions through antibody selection operations. We will detail the algorithmic workflow and present case studies demonstrating its effectiveness in power system optimization scenarios, providing readers with practical insights for implementation.