Bacteria Foraging Optimization for Drug Design

• Published in: Computational Science and Its Applications - ICCSA 2016 Vol. 9788; pp. 322 - 331
• Main Authors: Peh, Sally Chen Woon, Hong, Jer Lang
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2016; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Bacterial foraging optimization; Bioinformatics; Drug design; Meta-heuristics; Network hardware; Protein ligand docking
• ISBN: 3319421107; 9783319421100
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-42111-7_25

The bacterial foraging optimization (BFO) method has been successfully applied in a number of optimization problems, especially alongside Particle Swarm Optimization as hybrid combinations. This relatively recent method is based on the locomotion and behavior of bacteria E.coli, with modifications made over the years to increase search time, space and reduce convergence time. Regardless of changes, BFO algorithms are still based on 4 main features which are Chemotaxis, reproduction, swarming, elimination and dispersal behaviours of E.coli. A nature based algorithm, BFO has been utilized in several optimization problems such as the power loss reduction problem and in the area of PID applications. Ligand docking is another optimization problem that can potentially benefit from BFO application and this paper will focus on the methodology of BFO application and its results. We are of the opinion that the incorporation of BFO in the ligand docking problem is effective and efficient.