Title: An improved multi-objective ant colony algorithm for building life cycle energy consumption optimisation
Authors: Yan Yuan; Jingling Yuan; Hongfu Du; Li Li
Addresses: School of Urban Design, Wuhan University, Luojia Hill, Wuhan, 430072, China. ' Computer Science and Technology School, Wuhan University of Technology, 122 Luoshi Road, Wuhan, 430070, China. ' Computer Science and Technology School, Wuhan University of Technology, 122 Luoshi Road, Wuhan, 430070, China. ' School of Urban Design, Wuhan University, Luojia Hill, Wuhan, 430072, China
Abstract: Building energy consumption (BEC) is very important for the environmental sustainability. Because of complexity and variety of building energy consumption, to achieve building energy consumption optimisation, especially for building life-cycle (BLC), multiple objectives have to be satisfied. In this paper, a novel mathematical model for BLC energy consumption assessment is formalised, a novel algorithm for optimisation of BLC energy consumption is developed by improving the multi-objective ant colony optimisation (MACO). In the algorithm, the estimation mechanism of Pareto optimal solution and the update rule of pheromone are derived. An efficacious optimisation solution for BLC energy consumption and an innovative application of MACO algorithm in the building energy efficiency area are presented in the paper.
Keywords: multi-objective ACO; ant colony optimisation; MACO; building life-cycle; BLC; building energy consumption; BEC; multi-objective optimisation; energy consumption optimisation; sustainability; sustainable buildings; mathematical modelling; energy efficiency.
International Journal of Computer Applications in Technology, 2012 Vol.43 No.1, pp.60 - 66
Published online: 13 Mar 2012 *Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article