NDLI: Adapting evolutionary dynamics of variation for multi-objective optimization

Content Provider	IEEE Xplore Digital Library
Author	Teoh, E.J. Chiam, S.C. Goh, C.K. Tan, K.C.
Copyright Year	2003
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore (Teoh, E.J.; Chiam, S.C.; Goh, C.K.; Tan, K.C.)
Abstract	Many real-world applications involve complex optimization problem with various competing specifications and constraints that are often difficult, if not impossible, to be solved without the aid of powerful and efficient optimization algorithms. Although evolutionary algorithms have proven to be successful with respect to the optimization goals of proximity and diversity, their capability is bottlenecked by the evolutionary operators' abilities to deal with the complicated search spaces. Furthermore, it is well known that the algorithm's performances in different problems are sensitive to the parameter setting of the operators. In an effort to adapt the evolutionary search ability along the different regions of the search space, this paper proposes a dynamic variation operator whose parameter value is deterministically adapted during the algorithm run so as to maintain a balance between the extensive exploration in the early phase and local fine-tuning in the end phase. Comparative studies with some representative variation operators are performed on different benchmark problems to illustrate the effectiveness and efficiency of the proposed operator.
Starting Page	1290
Ending Page	1297
File Size	3091263
Page Count	8
File Format	PDF
ISBN	0780393635
DOI	10.1109/CEC.2005.1554839
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-09-02
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genetic mutations Constraint optimization Power engineering and energy Evolutionary computation Runtime Space exploration Drives Application software Computer science Finance
Content Type	Text
Resource Type	Article

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