Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
MOCell: A cellular genetic algorithm for multiobjective optimization
International Journal of Intelligent Systems - Special Issue on Nature Inspired Cooperative Strategies for Optimization
Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
AbYSS: Adapting Scatter Search to Multiobjective Optimization
IEEE Transactions on Evolutionary Computation
Distribution of computational effort in parallel MOEA/D
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Asynchronous master-slave parallelization of differential evolution for multi-objective optimization
Evolutionary Computation
An efficient dynamic load balancing algorithm
Computational Mechanics
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MOEA/D is a multi-objective metaheuristic which has shown a remarkable performance when solving hard optimization problems. In this paper, we propose a thread-based parallel version of MOEA/D designed to be executed on modern multi-core processors. Our interest is to study the potential benefits of the parallel approach in terms of speed-ups and the quality of the obtained Pareto front approximations when solving a benchmark composed of nine problems. The obtained results on two different multi-core based machines indicate that notable time reductions can be achieved. We have also found out that, with a few exceptions, there are not significant differences in terms of solution quality among the sequential MOEA/D and the parallel versions of it when using up to eight threads.