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

Optimization algorithms for multi-objective problems with fuzzy data

International audienceThis paper addresses multi-objective problems withfuzzy data which are expressed by means of triangular fuzzynumbers. In our previous work, we have proposed a fuzzy Paretoapproach for ranking the generated triangular-valued functions.Then, since the classical multi-objective optimization methods canonly use crisp values, we have applied a defuzzification process.In this paper, we propose a fuzzy extension of two well-knownmulti-objective evolutionary algorithms: SPEA2 and NSGAII byintegrating the fuzzy Pareto approach and by adapting theirclassical techniques of diversity preservation to the triangularfuzzy context. An application on multi-objective Vehicle RoutingProblem (VRP) with uncertain demands is finally proposed andevaluated using some experimental tests

Optimization algorithms for multi-objective problems with fuzzy data

International audienceThis paper addresses multi-objective problems withfuzzy data which are expressed by means of triangular fuzzynumbers. In our previous work, we have proposed a fuzzy Paretoapproach for ranking the generated triangular-valued functions.Then, since the classical multi-objective optimization methods canonly use crisp values, we have applied a defuzzification process.In this paper, we propose a fuzzy extension of two well-knownmulti-objective evolutionary algorithms: SPEA2 and NSGAII byintegrating the fuzzy Pareto approach and by adapting theirclassical techniques of diversity preservation to the triangularfuzzy context. An application on multi-objective Vehicle RoutingProblem (VRP) with uncertain demands is finally proposed andevaluated using some experimental tests