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Multi-objective reconfiguration of the distribution systems by using NSGA-II and local improvement
Reconfiguración multi-objetivo de sistemas de distribución utilizando NSGA-II y mejora local
DOI:
https://doi.org/10.15446/dyna.v90n229.108399Palabras clave:
reconfiguration; distribution systems; genetic algorithm; NSGA-II (en)reconfiguración; systemas de distribución; algoritmo genético; NSGA-II (es)
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Many heuristics for network reconfiguration rely on the systematic applying of the branch-exchange technique. In this work, two novel genetic operators for crossover and mutation have been developed that are based on the referred technique. The chromosome's codification to use these operators is straightforward and is not required any additional knowledge of graph theory to achieve the feasibility of individuals. As one of their main novelties, the methodology shows how can be employed a local improvement step, used commonly in the single-objective optimization, in the multi-objective optimization. This step increases the convergence of the optimization with populations of much reduced size. The proposed methodology is tested by solving several examples of the literature, including or not the local improvement step. The comparison of the results with the best solutions published for these examples shows the effectiveness of the method.
Muchas heurísticas para la reconfiguración de redes se basan en la aplicación sistemática de la técnica de intercambio de ramas. En este trabajo se han desarrollado dos operadores genéticos novedosos para cruce y mutación basados en la técnica referida. La codificación del cromosoma para utilizar estos operadores es sencilla y no se requiere ningún conocimiento adicional de teoría de grafos para lograr la factibilidad de los individuos. Como una de sus principales novedades, la metodología muestra cómo se puede emplear un paso de mejora local, utilizado comúnmente en la optimización de un solo objetivo, en la optimización de múltiples objetivos. Este paso aumenta la convergencia de la optimización con poblaciones de tamaño muy reducido. La metodología propuesta se pone a prueba resolviendo varios ejemplos de la literatura, incluyendo o no el paso de mejora local. La comparación de los resultados con las mejores soluciones publicadas para estos ejemplos muestra la eficacia del método.
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1. Bansendeka Theo Nyingu, Lebogang Masike, Mwana Wa Kalaga Mbukani. (2025). Multi-Objective Optimization of Load Flow in Power Systems: An Overview. Energies, 18(22), p.6056. https://doi.org/10.3390/en18226056.
2. Samuel Onodjohwo, Bankole Adebanji, Folashade Ariba, Emmanuel Taiwo Fasina, Isaac Onimisi Yusuf, Adenike Josephine. (2024). Need for Network Reconfiguration in Nigerian Distribution Systems: A Review. 2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG). , p.1. https://doi.org/10.1109/SEB4SDG60871.2024.10630443.
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