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A Novel Multi-objective Orthogonal Simulated Annealing Algorithm for Solving Multi-objective Optimization Problems with a Large Number of Parameters
Li-Sun Shu1, Shinn-Jang Ho1, Shinn-Ying Ho2, Jian-Hung Chen1, and Ming-Hao Hung1
1Department of Information Engineering and Computer Science, Feng China University, Taichung, Taiwan 407, ROC
p860048@knight.fcu.edu.tw
syho@fcu.edu.tw
p8800146@knight.fcu.edu.tw
p8800043@knight.fcu.edu.tw
2Department of Automation Engineering, National Huwei Institute of Technology, Huwei, Yunlin, Taiwan 632, ROC
sjho@nhit.edu.tw
Abstract. In this paper, a novel multi-objective orthogonal simulated annealing algorithm MOOSA using a generalized Pareto-based scale-independent fitness function and multi-objective intelligent generation mechanism (MOIGM) is proposed to efficiently solve multi-objective optimization problems with large parameters. Instead of generate-and-test methods, MOIGM makes use of a systematic reasoning ability of orthogonal experimental design to efficiently search for a set of Pareto solutions. It is shown empirically that MOOSA is comparable to some existing population-based algorithms in solving some multi-objective test functions with a large number of parameters.
LNCS 3102, p. 737 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2004