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AN IMMUNE-INSPIRED EVOLUTION STRATEGY FOR CONSTRAINED OPTIMIZATION PROBLEMS.

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Title: AN IMMUNE-INSPIRED EVOLUTION STRATEGY FOR CONSTRAINED OPTIMIZATION PROBLEMS.
Authors: CHEN, JIANYONG1 cjyok2000@hotmail.com, LIN, QIUZHEN1 qiuzheng658@163.com, SHEN, LINLIN1,2 llshen@szu.edu.cn
Source: International Journal on Artificial Intelligence Tools. Jun2011, Vol. 20 Issue 3, p549-561. 13p.
Subjects: Constrained optimization, Computer simulation of immune system, Cloning, Immunologic memory, Algorithms, Search engines, Generations, Population
Abstract: Based on clonal selection principle, this paper proposes an immune-inspired evolution strategy (IIES) for constrained optimization problems with two improvements. Firstly, in order to enhance global search capability, more clones are produced by individuals that have far-off nearest neighbors in the less-crowed regions. On the other hand, immune update mechanism is proposed to replace the worst individuals in clone population with the best individuals stored in immune memory in every generation. Therefore, search direction can always focus on the fittest individuals. These proposals are able to avoid being trapped in local optimal regions and remarkably enhance global search capability. In order to examine the optimization performance of IIES, 13 well-known benchmark test functions are used. When comparing with various state-of-the-arts and recently proposed competent algorithms, simulation results show that IIES performs better or comparably in most cases. [ABSTRACT FROM AUTHOR]
Copyright of International Journal on Artificial Intelligence Tools is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 61407928
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  Data: AN IMMUNE-INSPIRED EVOLUTION STRATEGY FOR CONSTRAINED OPTIMIZATION PROBLEMS.
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  Data: <searchLink fieldCode="AR" term="%22CHEN%2C+JIANYONG%22">CHEN, JIANYONG</searchLink><relatesTo>1</relatesTo><i> cjyok2000@hotmail.com</i><br /><searchLink fieldCode="AR" term="%22LIN%2C+QIUZHEN%22">LIN, QIUZHEN</searchLink><relatesTo>1</relatesTo><i> qiuzheng658@163.com</i><br /><searchLink fieldCode="AR" term="%22SHEN%2C+LINLIN%22">SHEN, LINLIN</searchLink><relatesTo>1,2</relatesTo><i> llshen@szu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+on+Artificial+Intelligence+Tools%22">International Journal on Artificial Intelligence Tools</searchLink>. Jun2011, Vol. 20 Issue 3, p549-561. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Constrained+optimization%22">Constrained optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation+of+immune+system%22">Computer simulation of immune system</searchLink><br /><searchLink fieldCode="DE" term="%22Cloning%22">Cloning</searchLink><br /><searchLink fieldCode="DE" term="%22Immunologic+memory%22">Immunologic memory</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Search+engines%22">Search engines</searchLink><br /><searchLink fieldCode="DE" term="%22Generations%22">Generations</searchLink><br /><searchLink fieldCode="DE" term="%22Population%22">Population</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Based on clonal selection principle, this paper proposes an immune-inspired evolution strategy (IIES) for constrained optimization problems with two improvements. Firstly, in order to enhance global search capability, more clones are produced by individuals that have far-off nearest neighbors in the less-crowed regions. On the other hand, immune update mechanism is proposed to replace the worst individuals in clone population with the best individuals stored in immune memory in every generation. Therefore, search direction can always focus on the fittest individuals. These proposals are able to avoid being trapped in local optimal regions and remarkably enhance global search capability. In order to examine the optimization performance of IIES, 13 well-known benchmark test functions are used. When comparing with various state-of-the-arts and recently proposed competent algorithms, simulation results show that IIES performs better or comparably in most cases. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal on Artificial Intelligence Tools is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1142/S0218213011000279
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 13
        StartPage: 549
    Subjects:
      – SubjectFull: Constrained optimization
        Type: general
      – SubjectFull: Computer simulation of immune system
        Type: general
      – SubjectFull: Cloning
        Type: general
      – SubjectFull: Immunologic memory
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Search engines
        Type: general
      – SubjectFull: Generations
        Type: general
      – SubjectFull: Population
        Type: general
    Titles:
      – TitleFull: AN IMMUNE-INSPIRED EVOLUTION STRATEGY FOR CONSTRAINED OPTIMIZATION PROBLEMS.
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            NameFull: CHEN, JIANYONG
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            NameFull: LIN, QIUZHEN
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            NameFull: SHEN, LINLIN
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              M: 06
              Text: Jun2011
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              Y: 2011
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            – TitleFull: International Journal on Artificial Intelligence Tools
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