An artificial immune network for multi-objective optimization

Authors:
Aris Lanaridis;Andreas Stafylopatis
Affiliations:
Intelligent Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece;Intelligent Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
Venue:
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
Year:
2010

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Abstract

This paper presents a method for approximating the Pareto front of a given function using Artificial Immune Networks. The proposed algorithm uses cloning and mutation to create local subsets of the Pareto front, and combines elements of these local fronts in a way that maximizes the diversity. The method is compared against SPEA and NSGA-II in a number of problems from the ZDT test suite, yielding satisfactory results.