Hyperheuristics based on reasoning with mastery in metaheuristics x-pso multiobjective hy x-fpso cbr. Application on a dynamic optimization of possibilities part 1) theoretical developments of the hyperheuristic algorithm hy x-fpso cbr
Keywords:
optimization, particles swarm, HyperHeuristics, artificial neural network, electric distribution systemAbstract
In this work the conceptual/theoretical framework of a novel HyperHeuristic, Case Based Reasoning and supported on some variants of MultiObjective Particle Swarm Optimization MetaHeuristic, called X-PSO, are presented.
This HyperHeuristic, referred as HY X-FPSO CBR (Case Based Reasoning), works by mean of selection function, aproximated with an Artificial Backporpagation Neural Network. To design and, especially, training the Artificial Neural Network, Swarm Intelligence Principles and the skill that each X-FPSO form exhibit to satisfy it, as well as the Search Space that is defined by the Problems Class to solve, are considered. This HyperHeuristic are designed to be applied in the definition of States Space required for a Possibilistic Optimization on the Mid/Short term Planning of a Electric Distribution System (EDS).
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