An Investigation on Noisy Environments in Evolutionary Multiobjective Optimization

In addition to satisfying several competing objectives, many real-world applications are also characterized by a certain degree of noise, manifesting itself in the form of signal distortion or uncertain information. In this paper, extensive studies are carried out to examine the impact of noisy envi...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation Vol. 11; no. 3; pp. 354 - 381
Main Authors: Goh, C.K., Tan, K.C.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.06.2007
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Applied sciences
Archiving
Artificial intelligence
Computer science; control theory; systems
Constraint optimization
Convergence
Distortion
Evolutionary
Evolutionary algorithms
Evolutionary algorithms (EAs)
Evolutionary computation
Exact sciences and technology
Learning and adaptive systems
multiobjective optimization
Noise measurement
Noise robustness
noisy fitness function
Optimization
Parallel processing
Search methods
Searching
Signal distortion
Stochastic processes
Strategy
Studies
Working environment noise
Discontinuity
Local search
Probabilistic approach
Evolutionary algorithm
Empirical method
Multiobjective programming
Optimization
multiobjective optimization
Environment impact
Imperfect information
Evolutionary algorithms (EAs)
Robustness
noisy fitness function
Signal distortion
Signal to noise ratio
ISSN:1089-778X, 1941-0026
Online Access:Get full text
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Description
Summary:In addition to satisfying several competing objectives, many real-world applications are also characterized by a certain degree of noise, manifesting itself in the form of signal distortion or uncertain information. In this paper, extensive studies are carried out to examine the impact of noisy environments in evolutionary multiobjective optimization. Three noise-handling features are then proposed based upon the analysis of empirical results, including an experiential learning directed perturbation operator that adapts the magnitude and direction of variation according to past experiences for fast convergence, a gene adaptation selection strategy that helps the evolutionary search in escaping from local optima or premature convergence, and a possibilistic archiving model based on the concept of possibility and necessity measures to deal with problem of uncertainties. In addition, the performances of various multiobjective evolutionary algorithms in noisy environments, as well as the robustness and effectiveness of the proposed features are examined based upon five benchmark problems characterized by different difficulties in local optimality, nonuniformity, discontinuity, and nonconvexity
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2006.882428