Paper Info
Title
An immunological algorithm for global numerical optimization
Abstract
Numerical optimization of given objective functions is a crucial task in many real-life problems. The present article introduces an immunological algorithm for continuous global optimization problems, called opt-IA. Several biologically inspired algorithms have been designed during the last few years and have shown to have very good performance on standard test bed for numerical optimization. In this paper we assess and evaluate the performance of opt-IA, FEP, IFEP, DIRECT, CEP, PSO, and EO with respect to their general applicability as numerical optimization algorithms. The experimental protocol has been performed on a suite of 23 widely used benchmarks problems. The experimental results show that opt-IA is a suitable numerical optimization technique that, in terms of accuracy, generally outperforms the other algorithms analyzed in this comparative study. The opt-IA is also shown to be able to solve large-scale problems.
Year
DOI
Venue
2005
10.1007/11740698_25
Artificial Evolution
Keywords
Field
DocType
benchmarks problem,immunological algorithm,continuous global optimization problem,global numerical optimization,comparative study,numerical optimization algorithm,crucial task,good performance,suitable numerical optimization technique,numerical optimization,experimental protocol,artificial immune system,global optimization,objective function,test bed
Artificial immune system,Probabilistic-based design optimization,Derivative-free optimization,Mathematical optimization,Global optimization,Meta-optimization,Test functions for optimization,Algorithm,Multi-swarm optimization,Engineering,Metaheuristic
Conference
Volume
ISSN
ISBN
3871
0302-9743
3-540-33589-7
Citations 
PageRank 
References 
15
1.24
9
Authors
4
Name
Order
Citations
PageRank
vincenzo cutello155357.63
Giuseppe Narzisi215711.30
Giuseppe Nicosia347946.53
Mario Pavone421219.41