Author(s): Kamyab Tahernezhadiani | Ali Hamzeh | Sattar Hashemi
Journal: International Journal of Artificial Intelligence & Applications
ISSN 0976-2191
Volume: 3;
Issue: 1;
Start page: 65;
Date: 2012;
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Keywords: Multi-objective optimization | Evolutionary algorithm | Diversity | Pareto-set and Hypervolume
ABSTRACT
Diversity is an important notion in multi-objective evolutionary algorithms (MOEAs) and a lot ofresearchers have investigated this issue by means of appropriate methods. However most of evolutionarymulti-objective algorithms have attempted to take control on diversity in the objective space only andmaximized diversity of solutions (population) on Pareto- front. Nowadays due to importance of Multiobjectiveoptimization in industry and engineering, most of the designers want to find a diverse set ofPareto-optimal solutions which cover as much as space in its feasible regain of the solution space. Thispaper addresses this issue and attempt to introduce a method for preserving diversity of non-dominatedsolution (i.e. Pareto-set) in the solution space. This paper introduces the novel diversity measure as a firsttime, and then this new diversity measure is integrated efficiently into the hypervolume based Multiobjectivemethod. At end of this paper we compare the proposed method with other state-of-the-artalgorithms on well-established test problems. Experimental results show that the proposed methodoutperforms its competitive MOEAs respect to the quality of solution space criteria and Pareto-setapproximation.
Journal: International Journal of Artificial Intelligence & Applications
ISSN 0976-2191
Volume: 3;
Issue: 1;
Start page: 65;
Date: 2012;
VIEW PDF
DOWNLOAD PDF Original pageKeywords: Multi-objective optimization | Evolutionary algorithm | Diversity | Pareto-set and Hypervolume
ABSTRACT
Diversity is an important notion in multi-objective evolutionary algorithms (MOEAs) and a lot ofresearchers have investigated this issue by means of appropriate methods. However most of evolutionarymulti-objective algorithms have attempted to take control on diversity in the objective space only andmaximized diversity of solutions (population) on Pareto- front. Nowadays due to importance of Multiobjectiveoptimization in industry and engineering, most of the designers want to find a diverse set ofPareto-optimal solutions which cover as much as space in its feasible regain of the solution space. Thispaper addresses this issue and attempt to introduce a method for preserving diversity of non-dominatedsolution (i.e. Pareto-set) in the solution space. This paper introduces the novel diversity measure as a firsttime, and then this new diversity measure is integrated efficiently into the hypervolume based Multiobjectivemethod. At end of this paper we compare the proposed method with other state-of-the-artalgorithms on well-established test problems. Experimental results show that the proposed methodoutperforms its competitive MOEAs respect to the quality of solution space criteria and Pareto-setapproximation.
