Chapter

Artificial Immune Systems

Volume 4163 of the series Lecture Notes in Computer Science pp 280-293

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

  • Jun ChenAffiliated withDept. of Automatic Control and Systems Engineering, The University of Sheffield
  • , Mahdi MahfoufAffiliated withDept. of Automatic Control and Systems Engineering, The University of Sheffield

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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.