A Multi-learning Immune Algorithm for Numerical Optimization A Multi-Learning Immune Algorithm for Numerical Optimization

Access this Article

Search this Article

Author(s)

    • WANG Shuaiqun
    • College of Electronics & Information Engineering, Tongji University
    • GAO Shangce
    • College of Information Sciences and Technology, Donghua University|Faculty of Engineering, University of Toyama
    • Aorigele
    • Faculty of Engineering, University of Toyama
    • TODO Yuki
    • School of Electrical and Computer Engineering, Kanazawa University

Abstract

The emergence of nature-inspired algorithms (NIA) is a great milestone in the field of computational intelligence community. As one of the NIAs, the artificial immune algorithm (AIS) mimics the principles of the biological immune system, and has exhibited its effectiveness, implicit parallelism, flexibility and applicability when solving various engineering problems. Nevertheless, AIS still suffers from the issues of evolution premature, local minima trapping and slow convergence due to its inherent stochastic search dynamics. Much effort has been made to improve the search performance of AIS from different aspects, such as population diversity maintenance, adaptive parameter control, etc. In this paper, we propose a novel multi-learning operator into the AIS to further enrich the search dynamics of the algorithm. A framework of embedding multiple commonly used mutation operators into the antibody evolution procedure is also established. Four distinct learning operators including baldwinian learning, cauchy mutation, gaussian mutation and lateral mutation are selected to merge together as a multi-learning operator. It can be expected that the multi-learning operator can effectively balance the exploration and exploitation of the search by enriched dynamics. To verify its performance, the proposed algorithm, which is called multi-learning immune algorithm (MLIA), is applied on a number of benchmark functions. Experimental results demonstrate the superiority of the proposed algorithm in terms of convergence speed and solution quality. Copyright © 2015 The Institute of Electronics, Information and Communication Engineers.

The emergence of nature-inspired algorithms (NIA) is a great milestone in the field of computational intelligence community. As one of the NIAs, the artificial immune algorithm (AIS) mimics the principles of the biological immune system, and has exhibited its effectiveness, implicit parallelism, flexibility and applicability when solving various engineering problems. Nevertheless, AIS still suffers from the issues of evolution premature, local minima trapping and slow convergence due to its inherent stochastic search dynamics. Much effort has been made to improve the search performance of AIS from different aspects, such as population diversity maintenance, adaptive parameter control, etc. In this paper, we propose a novel multi-learning operator into the AIS to further enrich the search dynamics of the algorithm. A framework of embedding multiple commonly used mutation operators into the antibody evolution procedure is also established. Four distinct learning operators including baldwinian learning, cauchy mutation, gaussian mutation and lateral mutation are selected to merge together as a multi-learning operator. It can be expected that the multi-learning operator can effectively balance the exploration and exploitation of the search by enriched dynamics. To verify its performance, the proposed algorithm, which is called multi-learning immune algorithm (MLIA), is applied on a number of benchmark functions. Experimental results demonstrate the superiority of the proposed algorithm in terms of convergence speed and solution quality.

Journal

  • IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

    IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E98.A(1), 362-377, 2015

    The Institute of Electronics, Information and Communication Engineers

Codes

  • NII Article ID (NAID)
    130005129459
  • NII NACSIS-CAT ID (NCID)
    AA11510296
  • Text Lang
    ENG
  • Article Type
    journal article
  • ISSN
    0916-8508
  • Data Source
    IR  J-STAGE 
Page Top