Clonal selection: an immunological algorithm for global optimization over continuous spaces

  • Authors:

  • Mario Pavone;Giuseppe Narzisi;Giuseppe Nicosia

  • Affiliations:

  • Department of Mathematics and Computer Science, University of Catania, Catania, Italy 95125;Computer Science Department, Courant Institute of Mathematical Sciences, New York University, New York, USA 10012;Department of Mathematics and Computer Science, University of Catania, Catania, Italy 95125

  • Venue:
  • Journal of Global Optimization
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

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.