Comparing classical generating methods with an evolutionary multi-objective optimization method

Shukla, Pradyumn Kumar ; Deb, Kalyanmoy ; Tiwari, Santosh (2005) Comparing classical generating methods with an evolutionary multi-objective optimization method Lecture Notes in Computer Science, 3410/2 . pp. 311-325. ISSN 0302-9743

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Official URL: http://www.springerlink.com/index/C6YHDHJ23ULH3BLQ...

Related URL: http://dx.doi.org/10.1007/978-3-540-31880-4_22

Abstract

For the past decade, many evolutionary multi-objective optimization (EMO) methodologies have been developed and applied to find multiple Pareto-optimal solutions in a single simulation run. In this paper, we discuss three different classical generating methods, some of which were suggested even before the inception of EMO methodologies. These methods specialize in finding multiple Pareto-optimal solutions in a single simulation run. On visual comparisons of the efficient frontiers obtained for a number of two and three-objective test problems, these algorithms are evaluated with an EMO methodology. The results bring out interesting insights about the strengths and weaknesses of these approaches. Further investigations of such classical generating methodologies and their evaluation should enable researchers to design a hybrid multi-objective optimization algorithm which may be better than each individual method.

Item Type:Article
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ID Code:83508
Deposited On:21 Feb 2012 07:11
Last Modified:19 May 2016 00:20

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