Abstract
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 maintain the same effectiveness showed for two or three objectives. Therefore, as a way to ameliorate this performance degradation several authors proposed preference-based methods as an alternative to Pareto based approaches. On the other hand, parallelization has shown to be useful in evolutionary optimizations. A central aspect for the parallelization of evolutionary algorithms is the population partitioning approach. Thus, this paper presents a new parallelization approach based on clustering by the shape of objective vectors to deal with many-objective problems. The proposed method was compared with random and \(k\)-means clustering approaches using a multi-threading framework in parallelization of the NSGA-II and six variants using preference-based relations for fitness assignment. Executions were carried-out for the DTLZ problem suite, and the obtained solutions were compared using the generational distance metric. Experimental results show that the proposed shape-based partition achieves competitive results when comparing to the sequential and to other partitioning approaches.
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von Lücken, C., Brizuela, C., Barán, B. (2015). Clustering Based Parallel Many-Objective Evolutionary Algorithms Using the Shape of the Objective Vectors. In: Gaspar-Cunha, A., Henggeler Antunes, C., Coello, C. (eds) Evolutionary Multi-Criterion Optimization. EMO 2015. Lecture Notes in Computer Science(), vol 9019. Springer, Cham. https://doi.org/10.1007/978-3-319-15892-1_4
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