Technical gazette, Vol. 27 No. 1, 2020.
Original scientific paper
https://doi.org/10.17559/TV-20191212113720
Multi-objective Optimization of Construction Project Based on Improved Ant Colony Algorithm
Yancang Li
; School of Water Conservancy and Hydroelectric Power, Hebei University of Engineering, No. 19, Taiji Road, Handan, Hebei Province, 056038, China
Shuren Wang*
orcid.org/0000-0001-5490-2825
; International Joint Research Laboratory of Henan Province for Underground Space Development and Disaster Prevention, Henan Polytechnic University, No. 2001 Century Avenue, Jiaozuo, Henan Province, 454003, China
Yongsheng He
; Institute of National Defence Engineering, Academy of Military Sciences, No. 3 Xishan Road, Luoyang, Henan Province, 471023, China
Abstract
It is the key and difficult problem for the current project management to consider the multi-objective optimization of the four elements, such as quality, duration, cost and safety. To improve the accuracy and efficiency of project management during the engineering construction, considering the advantages and disadvantages of the traditional quality-cost-time model, the four elements were regarded as a system, and a multi-objective optimization model was established. The improved ant colony algorithm was used to carry out multi-objectives of construction projects to overcome the premature defect of the traditional method. The optimal plan of the project was found and the overall efficiency of the construction project management was improved. Results show the optimized ant colony algorithm can avoid the low efficiency of the optimal solution search and the shortcoming of the initial pheromone. The engineering practice proves that the enhanced algorithm has solved the problem of the multi-objective optimization of quality, duration, cost and safety. The obtained conclusions are of significant reference value to direct the similar engineering practice.
Keywords
ant colony algorithm; fitness function; model; multi-objective optimization; project management
Hrčak ID:
234215
URI
Publication date:
15.2.2020.
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