Hybrid Self-Adaptive Evolution Strategies Guided by Neighborhood Structures for Combinatorial Optimization Problems.
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.
Undergraduate Program of Control and Automation Engineering, Universidade Federal de Ouro Preto, Ouro Preto, MG, 35400-000, BrazilGraduate Program in Electrical Engineering, Universidade Federal de Minas Gerais, Belo Horizonte, MG, 31270-901, Brazil vncoelho@gmail.com.
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Coelho
Department of Computer Science, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, 20550-900, Brazil igor.machado@ime.uerj.br.
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Coelho
Department of Computer Science, Universidade Federal de Ouro Preto, Ouro Preto, MG, 35400-000, Brazil marcone@iceb.ufop.br.
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Souza
Business Graduate Program, Department of Business and Economics, Universidade Federal de Lavras, Lavras, Brazil thaysoliveira7@gmail.com.
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Oliveira
Graduate Program in Electrical Engineering, Universidade Federal de Minas Gerais, Belo Horizonte, MG, 31270-901, Brazil lucianocota@ufmg.br.
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Cota
Computer Science Graduate Program, Universidade Federal Fluminense, Niterói, Brazil matheushaddad@ic.uff.br.
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Haddad
LAMIH, Université de Valenciennes et du Hainaut Cambrésis, Valenciennes, FranceMathematical Institute, Serbian Academy of Science and Arts, Serbia nenadmladenovic12@gmail.com.
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Mladenovic
Department of Electrical and Computer Engineering, McGill University, Montreal, QC, H3A 0E9, Canada rodrigo.silva@mail.mcgill.ca.
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Silva
Department of Electrical Engineering, Universidade Federal de Minas Gerais, Belo Horizonte, MG, 31270-010, Brazil fredericoguimaraes@ufmg.br.
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Guimarães
D000465:Algorithms; D005075:Biological Evolution; D000067508:Computer Heuristics; D003198:Computer Simulation; D006801:Humans; D000069550:Machine Learning; D008906:Mining; D009154:Mutation; D010561:Personnel Staffing and Scheduling
Evolution strategies; memetic algorithms; neighborhood structures; open-pit mining operational planning; reduced variable neighborhood search; short-term load forecasting.; unrelated parallel machine scheduling