Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/30480
Title: A computationally efficient vector optimizer using ant colony optimizations algorithm for multobjective designs
Authors: Ho, SL 
Yang, S
Keywords: Ant colony optimization (ACO)
Evolutionary computation
Multiobjective optimization
Optimal design
Issue Date: 2008
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on magnetics, 2008, v. 44, no. 6, 4526889, p. 1034-1037 How to cite?
Journal: IEEE transactions on magnetics 
Abstract: An efficient vector optimizer is proposed based on the hybridization of an ant colony optimization method and a novel exploiting search mechanism. To inherit the learning and searching power of an ant colony algorithm while excluding the usage of a tedious and awkward pheromone updating scheme, it is proposed that an algorithm that models the foraging strategy of pachycodyla apicalis ants is employed and modified. In order to yield better Pareto solutions, the gradient balance concept is used to design the exploitation search process in which some a priori information about the characteristics of the objective functions is used in the selection of nests for subsequent intensifying searches. Numerical experiments are reported to validate the merits and advantages of the proposed vector optimizer for solving practical engineering design problems.
URI: http://hdl.handle.net/10397/30480
ISSN: 0018-9464
EISSN: 1941-0069
DOI: 10.1109/TMAG.2007.914864
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