NDLI: Indicator-based multi-objective local search

Content Provider	IEEE Xplore Digital Library
Author	Basseur, M. Burke, E.K.
Copyright Year	2007
Description	Author affiliation: Univ. of Nottingham, Nottingham (Basseur, M.; Burke, E.K.)
Abstract	This paper presents a simple and generic indicator-based multi-objective local search. This algorithm is a direct extension of the IBEA algorithm, an indicator- based evolutionary algorithm proposed in 2004 by Zitzler and Kuenzli, where the optimization goal is defined in terms of a binary indicator defining the selection operator. The methodology proposed in this paper has been defined in order to be easily adaptable and to be as parameter-independent as possible. We carry out a range of experiments on different binary indicators: Those used in IBEA experiments, and also the indicators derived from classical Pareto ranking methods taken from well-known multi-objective evolutionary algorithms of the literature. Experiments show that the best results are obtained using selection indicators which are not only based on Pareto dominance relation. Moreover, the generic local search algorithm presented in this paper and the proposed indicators obtain promising results which lead to a number of future research directions.
Starting Page	3100
Ending Page	3107
File Size	177950
Page Count	8
File Format	PDF
ISBN	9781424413393
DOI	10.1109/CEC.2007.4424867
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-25
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Evolutionary computation Search methods Pareto optimization Rail transportation Design methodology Genetic algorithms Application software Portfolios Processor scheduling Roads
Content Type	Text
Resource Type	Article

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