Authors: Coelho, Vitor Nazário
Coelho, Igor Machado
Souza, Marcone Jamilson Freitas
Oliveira, Thays
Cota, Luciano Perdigão
Haddad, Matheus Nohra
Mladenović, Nenad 
Silva, Rodrigo
Guimarães, Frederico Gadelha
Title: Hybrid self-adaptive evolution strategies guided by neighborhood structures for combinatorial optimization problems
Journal: Evolutionary Computation
Volume: 24
Issue: 4
First page: 637
Last page: 666
Issue Date: 1-Dec-2016
Rank: M21a
ISSN: 1063-6560
DOI: 10.1162/EVCO_a_00187
Abstract: 
This article presents an Evolution Strategy (ES)-based algorithm, designed to self-adapt its mutation operators, guiding the search into the solution space using a Self-Adaptive Reduced Variable Neighborhood Search procedure. In view of the specific local search operators for each individual, the proposed population-based approach also fits into the context of the Memetic Algorithms. The proposed variant uses the Greedy Rand omized Adaptive Search Procedure with different greedy parameters for generating its initial population, providing an interesting exploration-exploitation balance. To validate the proposal, this framework is applied to solve three different NP-Hard combinatorial optimization problems: an Open-Pit-Mining Operational Planning Problem with dynamic allocation of trucks, an Unrelated Parallel Machine Scheduling Problem with Setup Times, and the calibration of a hybrid fuzzy model for Short-Term Load Forecasting. Computational results point out the convergence of the proposed model and highlight its ability in combining the application of move operations from distinct neighborhood structures along the optimization. The results gathered and reported in this article represent a collective evidence of the performance of the method in challenging combinatorial optimization problems from different application domains. The proposed evolution strategy demonstrates an ability of adapting the strength of the mutation disturbance during the generations of its evolution process. The effectiveness of the proposal motivates the application of this novel evolutionary framework for solving other combinatorial optimization problems.
Keywords: Evolution strategies | Memetic algorithms | Neighborhood structures | Open-pitmining operational planning | Reduced variable neighborhood search | Short-term load forecasting | Unrelated parallel machine scheduling
Publisher: MIT Press
Project: CNPq, Grants 306694/2013-1 and 312276/2013-3
FAPEMIG, Grants CEX PPM 772/15 and APQ-02449-14

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