NDLI: Multiobjective evolutionary algorithm portfolio: Choosing suitable algorithm for multiobjective optimization problem

Content Provider	IEEE Xplore Digital Library
Author	Shiu Yin Yuen Xin Zhang
Copyright Year	2014
Description	Author affiliation: Dept. of Electron. Eng., City Univ. of Hong Kong, Hong Kong, China (Shiu Yin Yuen; Xin Zhang)
Abstract	The concept of algorithm portfolio has a long history. Recently this concept draws increasing attention from researchers, though most of the researches have concentrated on single objective optimization problems. This paper is intended to solve multiobjective optimization problems by proposing a multiple evolutionary algorithm portfolio. Differing from previous approaches, each component algorithm in our portfolio method has an independent population and the component algorithms do not communicate in any way with each other. Another difference is that our algorithm introduces no control parameters. This parameter-less characteristic is desirable as each additional parameter requires independent parameter tuning or control. A novel score calculation method, based on predicted performance, is used to assess the contributions of component algorithms during the optimization process. Such information is used by an algorithm selector which decides, for each generation, which algorithm to use. Experimental results show that our portfolio method outperforms individual algorithms in the portfolio. Moreover, it outperforms the AMALGAM method.
Starting Page	1967
Ending Page	1973
File Size	1443509
Page Count	7
File Format	PDF
e-ISBN	9781479914883
DOI	10.1109/CEC.2014.6900470
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Prediction algorithms Portfolios Sociology Statistics Algorithm design and analysis Optimization Evolutionary computation
Content Type	Text
Resource Type	Article

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