A novel multi-objective orthogonal simulated annealing algorithm for solving multi-objective optimization problems with a large number of parameters

Li Sun Shu; Shinn Jang Ho; Shinn-Ying Ho; Jian Hung Chen; Ming Hao Hung

doi:10.1007/978-3-540-24854-5_75

A novel multi-objective orthogonal simulated annealing algorithm for solving multi-objective optimization problems with a large number of parameters

Li Sun Shu^*, Shinn Jang Ho, Shinn-Ying Ho, Jian Hung Chen, Ming Hao Hung

^*Corresponding author for this work

Department of Biological Science and Technology

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

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.

Original language	English
Pages (from-to)	737-747
Number of pages	11
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3102
DOIs	https://doi.org/10.1007/978-3-540-24854-5_75
State	Published - 1 Dec 2004

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Shu, L. S., Ho, S. J., Ho, S-Y., Chen, J. H., & Hung, M. H. (2004). A novel multi-objective orthogonal simulated annealing algorithm for solving multi-objective optimization problems with a large number of parameters. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3102, 737-747. https://doi.org/10.1007/978-3-540-24854-5_75

Shu, Li Sun ; Ho, Shinn Jang ; Ho, Shinn-Ying ; Chen, Jian Hung ; Hung, Ming Hao. / A novel multi-objective orthogonal simulated annealing algorithm for solving multi-objective optimization problems with a large number of parameters. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2004 ; Vol. 3102. pp. 737-747.

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title = "A novel multi-objective orthogonal simulated annealing algorithm for solving multi-objective optimization problems with a large number of parameters",

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.",

author = "Shu, {Li Sun} and Ho, {Shinn Jang} and Shinn-Ying Ho and Chen, {Jian Hung} and Hung, {Ming Hao}",

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Shu, LS, Ho, SJ, Ho, S-Y, Chen, JH & Hung, MH 2004, 'A novel multi-objective orthogonal simulated annealing algorithm for solving multi-objective optimization problems with a large number of parameters', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3102, pp. 737-747. https://doi.org/10.1007/978-3-540-24854-5_75

A novel multi-objective orthogonal simulated annealing algorithm for solving multi-objective optimization problems with a large number of parameters. / Shu, Li Sun; Ho, Shinn Jang; Ho, Shinn-Ying; Chen, Jian Hung; Hung, Ming Hao.

In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 3102, 01.12.2004, p. 737-747.

Research output: Contribution to journal › Article › peer-review

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AU - Hung, Ming Hao

PY - 2004/12/1

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N2 - 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.

AB - 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.

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Shu LS, Ho SJ, Ho S-Y, Chen JH, Hung MH. A novel multi-objective orthogonal simulated annealing algorithm for solving multi-objective optimization problems with a large number of parameters. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2004 Dec 1;3102:737-747. https://doi.org/10.1007/978-3-540-24854-5_75

A novel multi-objective orthogonal simulated annealing algorithm for solving multi-objective optimization problems with a large number of parameters

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