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Soft Computing

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01.04.2014 | Methodologies and Application

Adaptive computational chemotaxis based on field in bacterial foraging optimization

verfasst von: Xin Xu, Hui-ling Chen

Erschienen in: Soft Computing | Ausgabe 4/2014

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Abstract

Bacterial foraging optimization (BFO) is predominately used to find solutions for real-world problems. One of the major characteristics of BFO is the chemotactic movement of a virtual bacterium that models a trial solution of the problems. It is pointed out that the chemotaxis employed by classical BFO usually results in sustained oscillation, especially on rough fitness landscapes, when a bacterium cell is close to the optima. In this paper we propose a novel adaptive computational chemotaxis based on the concept of field, in order to accelerate the convergence speed of the group of bacteria near the tolerance. Firstly, a simple scheme is designed for adapting the chemotactic step size of each field. Then, the scheme chooses the fields which perform better to boost further the convergence speed. Empirical simulations over several numerical benchmarks demonstrate that BFO with adaptive chemotactic operators based on field has better convergence behavior, as compared against other meta-heuristic algorithms.

Vorheriger Artikel MOIRAE: A computational strategy to extract and represent structural information from experimental protein templates

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Titel: Adaptive computational chemotaxis based on field in bacterial foraging optimization
verfasst von: Xin Xu
Hui-ling Chen
Publikationsdatum: 01.04.2014
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 4/2014
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-013-1089-4

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