Paper Info
Title
Clonal selection: an immunological algorithm for global optimization over continuous spaces
Abstract
In this research paper we present an immunological algorithm (IA) to solve global numerical optimization problems for high-dimensional instances. Such optimization problems are a crucial component for many real-world applications. We designed two versions of the IA: the first based on binary-code representation and the second based on real values, called opt-IMMALG01 and opt-IMMALG, respectively. A large set of experiments is presented to evaluate the effectiveness of the two proposed versions of IA. Both opt-IMMALG01 and opt-IMMALG were extensively compared against several nature inspired methodologies including a set of Differential Evolution algorithms whose performance is known to be superior to many other bio-inspired and deterministic algorithms on the same test bed. Also hybrid and deterministic global search algorithms (e.g., DIRECT, LeGO, PSwarm) are compared with both IA versions, for a total 39 optimization algorithms.The results suggest that the proposed immunological algorithm is effective, in terms of accuracy, and capable of solving large-scale instances for well-known benchmarks. Experimental results also indicate that both IA versions are comparable, and often outperform, the state-of-the-art optimization algorithms.
Year
DOI
Venue
2012
10.1007/s10898-011-9736-8
J. Global Optimization
Keywords
Field
DocType
Nonlinear optimization,Global optimization,Derivative-free optimization,Black-box optimization,Immunological algorithms,Evolutionary algorithms
Mathematical optimization,Derivative-free optimization,Search algorithm,Evolutionary algorithm,Global optimization,Nonlinear programming,Algorithm,Differential evolution,Optimization problem,Clonal selection,Mathematics
Journal
Volume
Issue
ISSN
53
4
0925-5001
Citations 
PageRank 
References 
20
0.75
29
Authors
3
Name
Order
Citations
PageRank
Mario Pavone121219.41
Giuseppe Narzisi215711.30
Giuseppe Nicosia347946.53