The search functionality is under construction.

IEICE TRANSACTIONS on Fundamentals

A Multi-Learning Immune Algorithm for Numerical Optimization

Shuaiqun WANG, Shangce GAO, Aorigele, Yuki TODO, Zheng TANG

  • Full Text Views

    0

  • Cite this

Summary :

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.

Publication
IEICE TRANSACTIONS on Fundamentals Vol.E98-A No.1 pp.362-377
Publication Date
2015/01/01
Publicized
Online ISSN
1745-1337
DOI
10.1587/transfun.E98.A.362
Type of Manuscript
PAPER
Category
Numerical Analysis and Optimization

Authors

Shuaiqun WANG
  Tongji University
Shangce GAO
  Donghua University,University of Toyama
Aorigele
  University of Toyama
Yuki TODO
  Kanazawa University
Zheng TANG
  University of Toyama

Keyword