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A constraint multi-objective artificial physics optimization algorithm

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2 Author(s)
Yan Wang ; College of Electrical and Information Engineering, Lanzhou University of Technology, 730050, China ; Jian-chao Zeng

The use of evolutionary algorithms to solve unconstraint multi-objective problems (MOPs) has attracted much attention recently. However, research on constraint multi-objective algorithms is relatively less. The authors introduce a novel evolutionary paradigm of artificial physics optimization (APO) into constraint multi-objective optimization domain and modify the original mass function and virtual force rules in order to fit constraint multi-objective optimization problems. Moreover the authors present a method of virtual force decreasing to improve the efficiency. Finally, simulation tests show that the algorithm is effective.

Published in:

Computational Intelligence and Natural Computing Proceedings (CINC), 2010 Second International Conference on  (Volume:1 )

Date of Conference:

13-14 Sept. 2010