Reference point based multi-objective optimization using evolutionary algorithms

Deb, Kalyanmoy ; Sundar, J. ; Udaya Bhaskara Rao, N. ; Chaudhuri, Shamik (2006) Reference point based multi-objective optimization using evolutionary algorithms International Journal of Computational Intelligence Research, 2 (3). pp. 273-286. ISSN 0973-1873

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Evolutionary multi-objective optimization (EMO) methodologies have been amply applied to find a representative set of Pareto-optimal solutions in the past decade and beyond. Although there are advantages of knowing the range of each objective for Pareto-optimality and the shape of the Pareto-optimal frontier itself in a problem for an adequate decision-making, the task of choosing a single preferred Paretooptimal solution is also an important task which has received a lukewarm attention so far. In this paper, we combine one such preference-based strategy with an EMO methodology and demonstrate how, instead of one solution, a preferred set of solutions near the reference points can be found parallely. We propose two approaches for this task: (i) a modified EMO procedure based on the elitist non-dominated sorting GA or NSGAII [1] and (ii) a predator-prey approach based on original grid based procedure [2]. On two-objective to 10-objective optimization test problems, the modified NSGA-II approach shows its efficacy in finding an adequate set of Pareto-optimal points. On two and three-objective problems, the predator-prey approach also demonstrate its usefulness. Such procedures will provide the decision-maker with a set of solutions near her/his preference so that a better and a more reliable decision can be made.

Item Type:Article
Source:Copyright of this article belongs to Research India Publications.
Keywords:Reference Point Approach; Interactive Multi-objective Method; Decision-making; Predator-prey Approach; Multi-objective Optimization
ID Code:81053
Deposited On:03 Feb 2012 11:43
Last Modified:18 May 2016 22:46

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