Title: Bacterial colony foraging for multi-mode product colour planning

Authors: Hanning Chen; Yunlong Zhu; Lianbo Ma; Weixing Su

Addresses: School of Computer Science and Software, Tianjin Polytechnic University, 300387, Tianjin, China ' Laboratory of Information Service and Intelligent Control, Shenyang Institute of Automation, Chinese Academy of Sciences, 110016, Shenyang, China ' Laboratory of Information Service and Intelligent Control, Shenyang Institute of Automation, Chinese Academy of Sciences, 110016, Shenyang, China ' Laboratory of Information Service and Intelligent Control, Shenyang Institute of Automation, Chinese Academy of Sciences, 110016, Shenyang, China

Abstract: In this work, in order to assist designer in colour planning during product development, an efficient synthesised evaluation model is presented to evaluate colour-combination schemes of multi-working modes products (MMP). A novel bacterial colony foraging (BCF) algorithm is proposed to search for the optimal colour-combination schemes of MMP based on the evaluation model. The proposed BCF extend original bacterial foraging algorithm to adaptive and cooperative mode by combining bacterial chemotaxis, cell-to-cell communication, and a self-adaptive foraging strategy. The experiment presents an exhaustive comparison of the proposed BCF and two successful bio-inspired search techniques, namely the genetic algorithm (GA) and particle swarm optimisation (PSO), on three MMP tested cases of different nature, namely a hair-drier with two-coloured areas and two working modes, and two arm-type aerial work platforms both two-coloured products while with two and three working modes, respectively. Simulation results demonstrate that the proposed method is feasible and efficient.

Keywords: colour planning; multimode products; MMP; bacterial foraging; bacterial chemotaxis; cell-to-cell communication; self-adaptive foraging strategy; bacterial colony foraging; BCF; product colours; product development; product design; evaluation modelling; colour combinations; genetic algorithms; GAs; particle swarm optimisation; PSO; hair dryers; working modes; simulation.

DOI: 10.1504/IJBIC.2015.071064

International Journal of Bio-Inspired Computation, 2015 Vol.7 No.4, pp.240 - 262

Received: 13 Sep 2013
Accepted: 22 Sep 2013
Published online: 11 Aug 2015 *

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