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A Population Adaptive Based Immune Algorithm for Solving Multi-objective Optimization Problems

  • Jun Chen
  • Mahdi Mahfouf
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4163)

Abstract

The primary objective of this paper is to put forward a general framework under which clear definitions of immune operators and their roles are provided. To this aim, a novel Population Adaptive Based Immune Algorithm (PAIA) inspired by Clonal Selection and Immune Network theories for solving multi-objective optimization problems (MOP) is proposed. The algorithm is shown to be insensitive to the initial population size; the population and clone size are adaptive with respect to the search process and the problem at hand. It is argued that the algorithm can largely reduce the number of evaluation times and is more consistent with the vertebrate immune system than the previously proposed algorithms. Preliminary results suggest that the algorithm is a valuable alternative to already established evolutionary based optimization algorithms, such as NSGA II, SPEA and VIS.

Keywords

Pareto Front Multiobjective Optimization Clonal Selection Artificial Immune System Multiobjective Optimization Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jun Chen
    • 1
  • Mahdi Mahfouf
    • 1
  1. 1.Dept. of Automatic Control and Systems EngineeringThe University of SheffieldSheffieldUnited Kingdom

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