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 -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 highli...
['Vitor Nazario Coelho', 'Igor Machado Coelho', 'Marcone Jamilson Freitas Souza', 'Thays A. Oliveira', 'Luciano Perdigão Cota', 'Matheus Nohra Haddad', 'Nenad Mladenović', 'Rodrigo César Pedrosa Silva', 'Frederico G. Guimarães']
Hybrid Self-Adaptive Evolution Strategies Guided by Neighborhood Structures for Combinatorial Optimization Problems