Solving Unit Commitment Problem Using Chemo-tactic PSO–DE Optimization Algorithm Combined with Lagrange Relaxation

This paper presents Chemo-tactic PSO-DE (CPSO-DE) optimization algorithm combined with Lagrange Relaxation method (LR) for solving Unit Commitment (UC) problem. The proposed approach employs Chemo-tactic PSO-DE algorithm for optimal settings of Lagrange multipliers. It provides high-quality performance and reaches global solution and is a hybrid heuristic algorithm based on Bacterial Foraging Optimization (BFO), Particle Swarm Optimization (PSO) and Differential Evolution (DE). The feasibility of the proposed method is demonstrated for 10-unit, 20-unit, and 40-unit systems respectively. The test results are compared with those obtained by Lagrangian relaxation (LR), genetic algorithm (GA), evolutionary programming (EP), and genetic algorithm based on unit characteristic classification (GAUC), enhanced adaptive Lagrangian relaxation (ELR), integer-coded genetic algorithm (ICGA) and hybrid particle swarm optimization (HPSO) in terms of solution quality. Simulation results show that the proposed method can provide a better solution.