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Hybrid method coupling AIS and zeroth order deterministic search

Hybrid method coupling AIS and zeroth order deterministic search Purpose – The paper presents an hybrid optimization technique which couples the artificial immune system (AIS) algorithm with a zeroth order deterministic method. Design/methodology/approach – AIS has been developed to tackle multi‐modal optimization problems and it has shown a great ability to explore the objective function space. The algorithm is subdivided into two phases: an outer and an inner cycle. The outer cycle is devoted to the exploration of the space while the inner is a local exploration of the objective function. The new hybrid method proposes to replace the local search by a zeroth order deterministic search to speed up the overall convergence. Findings – Results on two multi‐modal analytical objective functions show an increase of speed of the new procedure with respect to the standard AIS. The method is also tested on the TEAM 22 numerical problem and some a posteriori techniques for the analysis of multimodal blind objective functions are discussed. Originality/value – The new Multimodal optimization algorithm has allowed to explore thoroughly feasibility space giving rise to a partition of the whole space, the use of hybrid technique increases the performances of standard AIS increasing the convergence to the optimal points. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering Emerald Publishing

Hybrid method coupling AIS and zeroth order deterministic search

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Publisher
Emerald Publishing
Copyright
Copyright © 2005 Emerald Group Publishing Limited. All rights reserved.
ISSN
0332-1649
DOI
10.1108/03321640510598139
Publisher site
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Abstract

Purpose – The paper presents an hybrid optimization technique which couples the artificial immune system (AIS) algorithm with a zeroth order deterministic method. Design/methodology/approach – AIS has been developed to tackle multi‐modal optimization problems and it has shown a great ability to explore the objective function space. The algorithm is subdivided into two phases: an outer and an inner cycle. The outer cycle is devoted to the exploration of the space while the inner is a local exploration of the objective function. The new hybrid method proposes to replace the local search by a zeroth order deterministic search to speed up the overall convergence. Findings – Results on two multi‐modal analytical objective functions show an increase of speed of the new procedure with respect to the standard AIS. The method is also tested on the TEAM 22 numerical problem and some a posteriori techniques for the analysis of multimodal blind objective functions are discussed. Originality/value – The new Multimodal optimization algorithm has allowed to explore thoroughly feasibility space giving rise to a partition of the whole space, the use of hybrid technique increases the performances of standard AIS increasing the convergence to the optimal points.

Journal

COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering – Emerald Publishing

Published: Sep 1, 2005

Keywords: Optimization techniques; Body systems and organs; Numerical analysis

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