Hyperheuristic model hy x-fpso cbr sii supported in metaheuristics x-pso multiobjective to solve a class of problems of combinatorial optimization

Authors

  • Carlos Casanova Universidad Tecnológica Nacional. Facultad Regional Concepción del Uruguay.
  • Gustavo Schweickardt Universidad Tecnológica Nacional. Facultad Regional Concepción del Uruguay.
  • Federico Camargo Universidad Tecnológica Nacional. Facultad Regional Concepción del Uruguay.

Keywords:

artificial neural networks, case based reasoning, particle swarm optimization, selection hyperheuristics, soft computing

Abstract

Selection HyperHeuristics are informed search methods that work in a higher abstraction level than heuristic or MetaHeuristics: they constitute heuristics to choose heuristics. Such selection is realized by a Choice Function (CF), whose target is to decide which heuristic strategy is applied in each decision instance of the algorithm, using for that non-domain data about the problem being solved. In this work a Case Based Reasoning Selection HyperHeuristic with X-PSO MultiObjective domain is presented, whose CF is constituted of a Feed-Forward Artificial Neural Network (ANN) of Multi-Layer Perceptron (MLP) type. The non-domain information used by the CF is composed of Swarm Intelligence Indicators, proposed by the authors in previous papers, which aims to give a measure on the abilities of a swarm to solve a particular problem. The design and the optimization problem associated to the CF Case Based Training are presented, so as the method to carry out such training. Finally, the process is applied to build a CF for a CBR Hyperheuristic that solves two Combinatorial Optimization Problems: the Load Balancing of a Three Phase Power Distribution System and the Reliability Optimization of Electrical Distribution Systems in Medium-Voltage.

Downloads

Download data is not yet available.

References

BURKE, E. K., HYDE, M., KENDALL, G., OCHOA, G., OZCAN, E., Y WOODWARD, J. R. (2010). A Classification of Hyper-heuristic Approaches. En Handbook of Meta-Heuristics 2nd Edition, pp. 449-468.

CASANOVA, C., Y SCHWEICKARDT, G. (2016a). Análisis Cuantitativo de las Habilidades de Metaheurísticas X-PSO Multiobjetivo mediante Indicadores de Inteligencia de Grupo. En IV Congreso Nacional de Ingeniería en Informática/Sistemas de Información. Salta, Argentina.

CASANOVA, C., Y SCHWEICKARDT, G. (2016b). Análisis de las Habilidades de Metaheurísticas X-PSO Multiobjetivo mediante Indicadores de Inteligencia de Grupo: Aplicación en el Balance de Carga en Redes Eléctricas de Baja Tensión. Revista de la Escuela de Perfeccionamiento en Investigación Operativa, (40), pgs. 23-42.

GARRO, B. A., Y VÁZQUEZ, R. A. (2015). Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms. Computational Intelligence and Neuroscience, pp. 20-28.

SCHWEICKARDT, G., Y CASANOVA, C. (2017). Modelo HiperHeurístico para la Optimización de la Confiabilidad en Sistemas de Distribución Eléctrica de Media Tensión. En Anales XXX ENDIO y XVIII EPIO, pgs. 138-145.

SCHWEICKARDT, G., CASANOVA, C., Y GIMENEZ ALVAREZ, J. M. (2013). HiperHeurística basada en Razonamiento con Dominio en MetaHeurísticas X-PSO MultiObjetivo HY X-FPSO CBR. Aplicación sobre una Optimización Dinámica Posibilística. Parte 1) Desarrollos Teóricos del Algoritmo HiperHeurístico HY X-FPSO CBR. Revista de la Escuela de Perfeccionamiento en Investigación Operativa, (34), pgs. 8-29.

SCHWEICKARDT, G., Y MIRANDA, V. (2010). Metaheurística FEPSO aplicada a Problemas de Optimización Combinatoria: Balance de Fases en Sistemas de Distribución Eléctrica. Ciencia, Docencia y Tecnología, (40), pgs. 133–163.

SCHWEICKARDT, G., MIRANDA, V., Y ALVAREZ, J. M. G. (2010). Dos Enfoques Metaheurísticos para resolver Problemas de Optimización Combinatoria Multicriterio: Fuzzy Evolutionary Particle Swarm Optimization con Topología Estrella Global/Individual (FEPSO GIST) y Fuzzy Simulated Annealing (FSA). En XXIII ENDIO - XXI EPIO - II ERABIO.

SWAN, J., WOODWARD, J., ÖZCAN, E., KENDALL, G., Y BURKE, E. (2014). Searching the Hyper-heuristic Design Space. Cognitive Computation, 6(1), pp. 66-73.

WOLPERT, D. H., Y MACREADY, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), pp. 67-82.

Downloads

Published

2018-11-30

Issue

Section

Artículos Científicos

How to Cite

Hyperheuristic model hy x-fpso cbr sii supported in metaheuristics x-pso multiobjective to solve a class of problems of combinatorial optimization. (2018). Revista De La Escuela De Perfeccionamiento En Investigación Operativa, 26(44), 4-20. https://revistas.psi.unc.edu.ar/index.php/epio/article/view/22200