Academic Journals Database
Disseminating quality controlled scientific knowledge

TOWARDS ENHANCING SOLUTION SPACE DIVERSITY IN MULTI-OBJECTIVE OPTIMIZATION: A HYPERVOLUME-BASED APPROACH

ADD TO MY LIST
 
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;
VIEW PDF   PDF DOWNLOAD PDF   Download PDF Original page

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.
Save time & money - Smart Internet Solutions     

Tango Jona
Tangokurs Rapperswil-Jona