NDLI: A parallel surrogate-assisted multi-objective evolutionary algorithm for computationally expensive optimization problems

Content Provider	IEEE Xplore Digital Library
Author	Syberfeldt, A. Grimm, H. Ng, A. John, R.I.
Copyright Year	2008
Description	Author affiliation: Centre for Intell. Autom., Skovde, Univ., Skovde (Syberfeldt, A.; Grimm, H.; Ng, A.)
Abstract	This paper presents a new efficient multi-objective evolutionary algorithm for solving computationally-intensive optimization problems. To support a high degree of parallelism, the algorithm is based on a steady-state design. For improved efficiency the algorithm utilizes a surrogate to identify promising candidate solutions and filter out poor ones. To handle the uncertainties associated with the approximative surrogate evaluations, a new method for multi-objective optimization is described which is generally applicable to all surrogate techniques. In this method, basically, surrogate objective values assigned to offspring are adjusted to consider the error of the surrogate. The algorithm is evaluated on the ZDT benchmark functions and on a real-world problem of manufacturing optimization. In assessing the performance of the algorithm, a new performance metric is suggested that combines convergence and diversity into one single measure. Results from both the benchmark experiments and the real-world test case indicate the potential of the proposed algorithm.
Starting Page	3177
Ending Page	3184
File Size	294936
Page Count	8
File Format	PDF
ISBN	9781424418220
DOI	10.1109/CEC.2008.4631228
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Evolutionary computation
Content Type	Text
Resource Type	Article

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