Optimal budget assignment for service quality improvement in distribution systems using non-Dominated sorting genetic algorithm II (NSGAII) and memetic algorithm

RESUMEN: Se presenta un modelo de asignación óptima de presupuesto para mejoramiento de calidad del servicio en sistemas de distribución. El modelo consiste en un problema multiobjetivo que busca al mismo tiempo minimizar el costo de mantenimiento en sistemas de distribución y maximizar la reducción de la tasa de fallas. Este último objetivo se evalúa a través del indicador SAIFI (Frecuencia Media de Interrupción del Sistema). Para resolver el modelo propuesto se implementaron dos algoritmos poblacionales: Algoritmo Genético No-Dominado II (NSGA-II) y un Algoritmo Memético. Se realizan pruebas con dos sistemas eléctricos reales del Departamento de Antioquia en Colombia de 100 y 200 nodos, mostrando la aplicabilidad del modelo propuesto. Los frentes de Pareto óptimos obtenidos en la solución del problema muestran un set de posibles soluciones que representan un compromiso entre ambos objetivos y le dan al operador de red un estimado de cuánto debe invertir en mantenimiento para lograr un valor deseado del indicador SAIFI.ABSTRACT: This paper presents an optimal budget assignment model for improving quality service in distribution systems. The model consists on a multi objective problem which aims at minimizing maintenance costs in distribution systems while maximizing the reduction of faults rate. This last objective is measured through the SAIFI indicator (System Average Interruption Frequency Index). To solve the proposed model two algorithms were implemented: NSGAII (Non-Dominated Sorting Genetic Algorithm II) and a Memetic Algorithm. Several tests were performed with two real electrical systems of 100 and 200 nodes in the Department of Antioquia in Colombia, showing the applicability of the proposed approach. The optimal Pareto fronts obtained in the problem solution show a set of available options that represent a trade-off between both objectives and provides the system operator with an estimate of how much to invest in maintenance to achieve a desired value of the SAIFI indicator

Memetic Differential Evolution with an Improved Contraction Criterion

Memetic algorithms with an appropriate trade-off between the exploration and exploitation can obtain very good results in continuous optimization. In this paper, we present an improved memetic differential evolution algorithm for solving global optimization problems. The proposed approach, called memetic DE (MDE), hybridizes differential evolution (DE) with a local search (LS) operator and periodic reinitialization to balance the exploration and exploitation. A new contraction criterion, which is based on the improved maximum distance in objective space, is proposed to decide when the local search starts. The proposed algorithm is compared with six well-known evolutionary algorithms on twenty-one benchmark functions, and the experimental results are analyzed with two kinds of nonparametric statistical tests. Moreover, sensitivity analyses for parameters in MDE are also made. Experimental results have demonstrated the competitive performance of the proposed method with respect to the six compared algorithms