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


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Article Info

DATE

2011-06-10

AUTHORS

Mario Pavone, Giuseppe Narzisi, Giuseppe Nicosia

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. More... »

PAGES

769-808

References to SciGraph publications

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    http://scigraph.springernature.com/pub.10.1007/s10898-011-9736-8

    DOI

    http://dx.doi.org/10.1007/s10898-011-9736-8

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