Cutting Box Strategy: an algorithmic framework for improving metaheuristics for continuous global optimization
Abstract: In this work, we present a new framework to increase effectiveness of metaheuristics in seeking good solutions for the general nonlinear optimization problem, called Cutting Box Strategy (CBS). CBS is based on progressive reduction of the search space through the use of intelligent multi-starts, where solutions already obtained cannot be revisited by the adopted metaheuristic. Computational experiments with the CBS strategy are conducted with a variant of the population-based metaheuristic Differential Evolution to solve 36 test instances. The numerical results show that CBS can substantially increase the quality of the results of a metaheuristic applied for a nonlinear optimization problems.
Keywords: Metaheuristic, Constrained Optimization, Continuous Global Optimization, Differential Evolution
Category 1: Global Optimization (Stochastic Approaches )
Category 2: Nonlinear Optimization (Constrained Nonlinear Optimization )
Citation: Melo, Wendel ; Fampa, Marcia H.C. ; Raupp, Fernanda M.P. . Cutting Box Strategy: An Algorithmic Framework for Improving Metaheuristics for Continuous Global Optimization. In: Angelika Michalski. (Org.). Global Optimization: Theory, Developments and Applications. 1ed.: Nova Science Publishers, 2013, v.
Entry Submitted: 07/27/2015
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