Hybrid Self-Adaptive Evolution Strategies Guided by Neighborhood Structures for Combinatorial Optimization Problems

Autor: Vitor Nazário Coelho, Thays A. Oliveira, Rodrigo Silva, Matheus Nohra Haddad, Frederico Gadelha Guimarães, Igor Machado Coelho, Marcone Jamilson Freitas Souza, Luciano Perdigão Cota, Nenad Mladenović
Přispěvatelé: Universidade Federal de Ouro Preto (UFOP), Universidade do Estado do Rio de Janeiro [Rio de Janeiro] (UERJ), Universidade Federal de Lavras (UFLA), Departamento de Química, Universidade Federal de Minas Gerais, Universidade Federal de Minas Gerais [Belo Horizonte] (UFMG), Universidade Federal Fluminense [Rio de Janeiro] (UFF), Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201 (LAMIH), Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Centre National de la Recherche Scientifique (CNRS)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), McGill University = Université McGill [Montréal, Canada]
Rok vydání: 2016
Předmět:
Mathematical optimization
Optimization problem
Population
"Evolution strategies"
Personnel Staffing and Scheduling
0211 other engineering and technologies
02 engineering and technology
"Short-term load forecasting"
Mining
Machine Learning
0202 electrical engineering
electronic engineering
information engineering
Humans
[INFO]Computer Science [cs]
Computer Simulation
Local search (optimization)
education
Metaheuristic
Greedy randomized adaptive search procedure
Mathematics
"Unrelated parallel machine scheduling"
education.field_of_study
"Memetic algorithms"
021103 operations research
business.industry
"Reduced variable neighborhood search"
Biological Evolution
"Open-pit mining operational planning"
Computational Mathematics
Computer Heuristics
Mutation
Memetic algorithm
020201 artificial intelligence & image processing
Evolution strategy
business
"Neighborhood structures"
Algorithms
Variable neighborhood search
Zdroj: Evolutionary Computation
Evolutionary Computation, Massachusetts Institute of Technology Press (MIT Press), 2016, 24 (4), pp.637-666. ⟨10.1162/EVCO_a_00187⟩
ISSN: 1530-9304
1063-6560
DOI: 10.1162/evco_a_00187
Popis: International audience; 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 Randomized 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 [Formula: see text]-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.
Databáze: OpenAIRE
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