Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/8386
Title: | Approximating Pareto frontier using a hybrid line search approach |
Authors: | Grosan, C Abraham, A |
Keywords: | Pareto frontier;Global optimization;Line search;Metaheuristics;Multiobjective optimization |
Issue Date: | 2010 |
Publisher: | Elsevier Science Inc. |
Citation: | Information Sciences, 180(14), 2674 - 2695, 2010 |
Abstract: | The aggregation of objectives in multiple criteria programming is one of the simplest and widely used approach. But it is well known that this technique sometimes fail in different aspects for determining the Pareto frontier. This paper proposes a new approach for multicriteria optimization, which aggregates the objective functions and uses a line search method in order to locate an approximate efficient point. Once the first Pareto solution is obtained, a simplified version of the former one is used in the context of Pareto dominance to obtain a set of efficient points, which will assure a thorough distribution of solutions on the Pareto frontier. In the current form, the proposed technique is well suitable for problems having multiple objectives (it is not limited to bi-objective problems) and require the functions to be continuous twice differentiable. In order to assess the effectiveness of this approach, some experiments were performed and compared with two recent well known population-based metaheuristics namely ParEGO and NSGA II. When compared to ParEGO and NSGA II, the proposed approach not only assures a better convergence to the Pareto frontier but also illustrates a good distribution of solutions. From a computational point of view, both stages of the line search converge within a short time (average about 150 ms for the first stage and about 20 ms for the second stage). Apart from this, the proposed technique is very simple, easy to implement and use to solve multiobjective problems. |
Description: | This is the post-print version of the final paper published in Information Sciences. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V. |
URI: | http://www.sciencedirect.com/science/article/pii/S0020025509005489 http://bura.brunel.ac.uk/handle/2438/8386 |
DOI: | http://dx.doi.org/10.1016/j.ins.2009.12.018 |
ISSN: | 0020-0255 |
Appears in Collections: | Publications Computer Science Dept of Computer Science Research Papers |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Fulltext.pdf | 941.07 kB | Adobe PDF | View/Open |
Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.