A niched Pareto genetic algorithm for multiobjective environmental/economic dispatch

A niched Pareto genetic algorithm (NPGA) based approach to solve the multiobjective environmental/economic dispatch (EED) problem is presented in this paper. The EED problem is formulated as a non-linear constrained multiobjective optimization problem. The proposed NPGA based approach handles the problem as a multiobjective problem with competing and non-commensurable cost and emission objectives. One of the main advantages of the proposed approach is that there is no restriction on the number of optimized objectives. The proposed approach has a diversity-preserving mechanism to overcome the premature convergence problem. A hierarchical clustering algorithm is developed and imposed to provide the decision maker with a representative and manageable Pareto-optimal set. In addition, fuzzy set theory is employed to extract the best compromise solution. Several optimization runs of the proposed approach are carried out on the standard IEEE 30-bus test system. The results demonstrate the capabilities of the proposed approach to generate well-distributed Pareto-optimal non-dominated solutions of the multiobjective EED problem in one single run. The comparison with the classical methods demonstrates the superiority of the proposed approach and confirms its potential to solve the multiobjective EED problem.