A population adaptive based immune algorithm for solving multi-objective optimization problems

Chen, Jun and Mahfouf, Mahdi (2006) A population adaptive based immune algorithm for solving multi-objective optimization problems. In: 5th International Conference on Artificial Immune System, 4-6th September 2006, Instituto Gulbenkian de Ciência, Oeiras, Portugal.

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Full text URL: http://dx.doi.org/10.1007/11823940_22

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.

Item Type:Conference or Workshop Item (Paper)
Additional Information: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:multi-objective, optimization, artificial immune system, adaptive population
Subjects:H Engineering > H131 Automated Engineering Design
H Engineering > H650 Systems Engineering
H Engineering > H130 Computer-Aided Engineering
Divisions:College of Science > School of Engineering
ID Code:2869
Deposited By: Rosaline Smith
Deposited On:14 Jul 2010 05:55
Last Modified:17 Jul 2014 10:58

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