%0 Journal Article
%T Bacterial Colony Optimization
%A Ben Niu
%A Hong Wang
%J Discrete Dynamics in Nature and Society
%D 2012
%I Hindawi Publishing Corporation
%R 10.1155/2012/698057
%X This paper investigates the behaviors at different developmental stages in Escherichia coli (E. coli) lifecycle and developing a new biologically inspired optimization algorithm named bacterial colony optimization (BCO). BCO is based on a lifecycle model that simulates some typical behaviors of E. coli bacteria during their whole lifecycle, including chemotaxis, communication, elimination, reproduction, and migration. A newly created chemotaxis strategy combined with communication mechanism is developed to simplify the bacterial optimization, which is spread over the whole optimization process. However, the other behaviors such as elimination, reproduction, and migration are implemented only when the given conditions are satisfied. Two types of interactive communication schemas: individuals exchange schema and group exchange schema are designed to improve the optimization efficiency. In the simulation studies, a set of 12 benchmark functions belonging to three classes (unimodal, multimodal, and rotated problems) are performed, and the performances of the proposed algorithms are compared with five recent evolutionary algorithms to demonstrate the superiority of BCO. 1. Introduction Swarm intelligence is the emergent collective intelligent behaviors from a large number of autonomous individuals. It provides an alternative way to design novel intelligent algorithms to solve complex real-world problems. Different from conventional computing paradigms [1¨C3], such algorithms have no constraints of central control, and the searching result of the group will not be affected by individual failures. What is more, swarm intelligent algorithms maintain a population of potential solutions to a problem instead of only one solution. Nowadays, most of swarm intelligent optimization algorithms are inspired by the behavior of animals with higher complexity. Particle swarm optimization (PSO) [4, 5] was gleaned ideas from swarm behavior of bird flocking or fish schooling. Ant colony optimization (ACO) was motivated from the foraging behavior of ants [6, 7]. Artificial fish swarm algorithm (AFSA) was originated in the swarming behavior of fish [8], and artificial bee colony algorithm (ABCA) [9, 10] was stimulated by social specialization behavior of bees. However, the states of the abovementioned animals are more complex, and their behaviors are difficult to describe qualitatively. As prokaryote, bacteria behave in a simple pattern which can be easily described. Inspired by the foraging behavior of Escherichia coli (E. coli) in human intestines, Passion proposed an
%U http://www.hindawi.com/journals/ddns/2012/698057/