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A multi-modal immune algorithm for the job-shop scheduling problem

INFORMATION SCIENCES 2009年第10期179卷 1516-1532页

作者： Luh, Guan-Chun Chueh, Chung-Huei Tatung Univ Dept Mech Engn Taipei 104 Taiwan Chenming Mold Ind Corp MIM Dept Taipei Taiwan

This paper describes the application of an artificial immune system to a scheduling application. A novel approach multi-modal immune algorithm is proposed for finding optimal solutions to job-shop scheduling problems emulating the features of a biological immune system. Inter-relationships within the proposed algorithm resemble antibody molecule structure, antibody-antigen relationships in terms of specificity, clonal proliferation, germinal center, and the memory characteristics of adaptive immune responses. Gene fragment recombination and several antibody diversification schemes including somatic recombination, somatic mutation, gene conversion, gene reversion, gene drift, and nucleotide addition were incorporated into the algorithm in order to improve the balance between exploitation and exploration. In addition, niche antibody was employed to discover multi-modal solutions. Numerous well-studied benchmark examples in job-shop scheduling problems were utilized to evaluate the proposed approach. The results indicate the effectiveness and flexibility of the immune algorithm. (c) 2008 Elsevier Inc. All rights reserved.

关键词： Artificial immune system Job-shop scheduling problem multi-modal immune algorithm Biological immune system

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Job shop scheduling optimization using multi-modal immune algorithm

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20th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems

作者： Luh, Guan-Chun Chueh, Chung-Huei Tatung Univ Dept Mech Engn Taipei Taiwan

ISBN: (纸本)9783540733225

A multi-modal immune algorithm is utilized for finding optimal solutions to job-shop scheduling problem emulating the features of a biological immune system. Inter-relation ships within the algorithm resemble antibody molecule structure, antibody-antigen relationships in terms of specificity, clonal proliferation, germinal center, and the memory characteristics of adaptive immune responses. In addition, Gene fragment recombination and several antibody diversification schemes were incorporated into the algorithm in order to improve the balance between exploitation and exploration. Moreover, niche scheme is employed to discover multi-modal solutions. Numerous well-studied benchmark examples were utilized to evaluate the effectiveness of the proposed approach.

关键词： multi-modal immune algorithm job-shop scheduling problem clonal proliferation gene fragment recombination antibody diversification

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multi-modal topological optimization of structure using immune algorithm

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2004年第36-38期193卷 4035-4055页

作者： Luh, GC Chueh, CH Tatung Univ Dept Mech Engn Taipei Taiwan

In this paper the authors describe a novel approach MMIA (multi-modal immune algorithm) for finding optimal solutions to multi-modal structural problems emulating the features of a biological immune system. The use of an immune algorithm as opposed to a genetic algorithm provides this methodology with superior local search ability. Inter-relationships within the proposed algorithm resemble antibody-antigen relationships in terms of specificity, germinal center, and the memory characteristics of adaptive immune responses. Gene fragment recombination and several antibody diversification schemes (including somatic recombination, somatic mutation, gene conversion, gene reversion, gene drift, and nucleotide addition) were incorporated into the MMIA in order to improve the balance between exploitation and exploration. Moreover the concept of cytokines is applied for constraint handling. Two well-studied benchmark examples in structural topology optimization problems were used to evaluate the proposed approach. The results indicate the effectiveness of MMIA. (C) 2004 Elsevier B.V. All rights reserved.

关键词： multi-modal immune algorithm genetic algorithm structural optimization constraint handling

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