Multi-objective Evolutionary Algorithms (MOEA) are used to solve complex multi-objective problems. As the number of objectives increases, Pareto-based MOEAs are unable to reproduce the same effectiveness showed for two or three objectives. Thus, several authors proposed preference-based methods as an alternative. On the other hand, parallelization has shown to be useful in evolutionary optimizations. This paper combines for the first time seven preference-based methods for many objective optimization in a multi-threading parallelization framework. Preference-based methods were used to replace the elitism procedure of the Non-dominated Sorting Genetic Algorithm II. Executions of each alternative were carried-out for the DTLZ-2 problem in a commodity multi-core platform. Obtained solutions were compared by different criteria, providing some insights into the improvements that the proposed combination may offer in many-objective optimization.

Multi-objective evolutionary algorithms, many-objective optimization, parallel evolutionary qlgo- rithms, computational mathematics. 1

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