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Abstract-The environmentalleconomic dispatch problem is a multiobjective nonlinear optimization problem with constraints. Until recently, this problem has been addressed by considering economic and emission objectives separately or as a weighted sum of both objectives.Multiobjective evolutionary algorithms can find multiple Pareto-optimal solutions in one single run and this ability makes them attractive for solving problems with multiple and conflicting objectives. This paper uses an elitist multiobjective evolutionary algorithm based on the Non-dominated Sorting Genetic Algorithm -11 (NSGA-11) for solving the environmentalleconomic dispatch problem. Elitism ensures that the population hest solution does not deteriorate in the next generations. Simulation results a r e presented for a sample power system.

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Abstract-Network reconfiguration in distribution systems is realised by changing the status of sectionalizing switches and is usually done for loss reduction. The distribution reconfiguration belongs to a complex combinatorial optimization problem. This is because there are multiple constraints, which must not be violated while finding an optimal o r near-optimal solution to the distribution network reconfiguration problem. An exhaustive search can definitely find the optimal solution but is computationally intensive. Moreover, solution produced by other heuristic search techniques often produce local optima. Consequently, to solve the problem with implementation simplicity, computation efficiency, solution feasibility and optimality, an improved method based on a modified genetic algorithm (CA) with real valued genes and an adaptive mutation rate is used.The distribution network reconfiguration (DNRC) model, in which the objective is to minimize the system power loss, is presented in this paper with application to 16-bus, 33-bus systems and a real distribution network of Mauritius. lntroductionNetwork reconfiguration in distribution systems is realised by changing the status of sectionalizing switches and is usually done for loss reduction. The distribution reconfiguration belongs to a complex combinatorial optimization problem. This is because there are multiple constraints, which must not be violated while finding an optimal or near-optimal solution to the distribution network reconfiguration problem.In network reconfiguration for loss reduction, the solution involves a search over relevant radial configurations. A variety of approaches to the network reconfiguration problem is surveyed in [l]. In fact, the generalized reconfiguration problem presents a considerable computational burden for a distribution system of even moderate proportions. Due to the nonlinear nature of the distribution system, a load flow operation bas to be performed to determine a new system operating point for each iteration of an optimization algorithm. Therefore, a direct or exhaustive solution is infeasible in practice. Most oftbe methods surveyed have adopted some heuristic search method, possibly guided by a simplified optimization procedure. The approach adopted in this paper is as follows. In order to obtain the globally optimal solution, the exhaustive search algorithm proposed by 191 is first used. The exact optimal solution is obtained by this method by enumeratively examining all possible switch options. Thus it requires prohibitively long computational time. To solve the problem with less computational burden, the solution approach proposed by [7] is then followed. However, the solution produced is locally optimal since it does not examine all possible switch options. Consequently, to solve the problem with implementation simplicity, computation efficiency, solution feasibility and optimality, an improved method based on a modified genetic algorithm (GA) with real valued genes and an adaptive mutation rate is proposed in thi...