NDLI: Combining Model-based and Genetics-based Offspring Generation for Multi-objective Optimization Using a Convergence Criterion

Content Provider	IEEE Xplore Digital Library
Author	Aimin Zhou Yaochu Jin Qingfu Zhang Sendhoff, B. Tsang, E.
Copyright Year	2006
Description	Author affiliation: Univ. of Essex, Colchester (Aimin Zhou)
Abstract	In our previous work conducted by Aimin Zhou et. al., (2005), it has been shown that the performance of multi-objective evolutionary algorithms can be greatly enhanced if the regularity in the distribution of Pareto-optimal solutions is used. This paper suggests a new hybrid multi-objective evolutionary algorithm by introducing a convergence based criterion to determine when the model-based method and when the genetics-based method should be used to generate offspring in each generation. The basic idea is that the genetics-based method, i.e., crossover and mutation, should be used when the population is far away from the Pareto front and no obvious regularity in population distribution can be observed. When the population moves towards the Pareto front, the distribution of the individuals will show increasing regularity and in this case, the model-based method should be used to generate offspring. The proposed hybrid method is verified on widely used test problems and our simulation results show that the method is effective in achieving Pareto-optimal solutions compared to two state-of-the-art evolutionary multi-objective algorithms: NSGA-II and SPEA2, and our pervious method in Aimin Zhou et. al., (2005).
Starting Page	892
Ending Page	899
File Size	251584
Page Count	8
File Format	PDF
ISBN	0780394879
DOI	10.1109/CEC.2006.1688406
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Convergence Evolutionary computation Electronic design automation and methodology Genetic mutations Hybrid power systems Clustering algorithms Partitioning algorithms Testing Computer science Europe
Content Type	Text
Resource Type	Article

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