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F. Streichert, H. Ulmer and A. Zell, “Parallelization of Multi-Objective Evolutionary Algorithms Using Clustering Algorithms,” In: C. A. Coello, A. H. Aguirre and E. Zitzler, Eds., Evolutionary Multi-Criterion Optimization, Springer, Berlin/Heidelberg, Vol. 3410, 2005, pp. 92-107. doi:10.1007/978-3-540-31880-4_7

has been cited by the following article:

• TITLE: A Hybrid Parallel Multi-Objective Genetic Algorithm for 0/1 Knapsack Problem

  AUTHORS: Sudhir B. Jagtap, Subhendu Kumar Pani, Ganeshchandra Shinde

  KEYWORDS: Multi-Objective Genetic Algorithm, Parallel Processing Techniques, NSGA-II, 0/1 Knapsack Problem, Trigger Model, Cone Separation Model, Island Model

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.4 No.5, May 26, 2011

  ABSTRACT: In this paper a hybrid parallel multi-objective genetic algorithm is proposed for solving 0/1 knapsack problem. Multi-objective problems with non-convex and discrete Pareto front can take enormous computation time to converge to the true Pareto front. Hence, the classical multi-objective genetic algorithms (MOGAs) (i.e., non- Parallel MOGAs) may fail to solve such intractable problem in a reasonable amount of time. The proposed hybrid model will combine the best attribute of island and Jakobovic master slave models. We conduct an extensive experimental study in a multi-core system by varying the different size of processors and the result is compared with basic parallel model i.e., master-slave model which is used to parallelize NSGA-II. The experimental results confirm that the hybrid model is showing a clear edge over master-slave model in terms of processing time and approximation to the true Pareto front.