Application of Metaheuristic techniques to generation expansion planning problem (1)

This paper reviews Metaheuristic techniques utilized in solving the generation expansion planning (GEP) problem. GEP isa large scale constrained nonlinear optimization problem. The best expansion plan could be found through comparison of every available plan. Considering the expanded dimensions of this problem concerned with the calculation times, this issue is not feasible. Therefore, in order to reach the ultimate optimal solution, different techniques are used for shortening the calculation times and also reducing the number of variables. Throughout the first part of this paper, different techniques of generation expansion planning including Particle Swarm Optimization (PSO), Tabu Search (TS), Artificial Neural Network (ANN), Cross-Entropy (CE) and finally the Non-dominated Sorting Genetic Algorithm (NSGA_II) are introduced. Also the majority of reported literatures based on the above mentioned techniques are reviewed.