Hybrid multiple objective artificial bee colony with differential evolution for the time-cost-quality tradeoff problem

Duc Hoc Tran*, Min Yuan Cheng, Minh Tu Cao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

85 Scopus citations

Abstract

Time, cost, and quality are three important but often conflicting factors that must be optimally balanced during the planning and management of construction projects. Tradeoff optimization among these three factors within the project scope is necessary to maximize overall project success. In this paper, the MOABCDE-TCQT, a new hybrid multiple objective evolutionary algorithm that is based on hybridization of artificial bee colony and differential evolution, is proposed to solve time-cost-quality tradeoff problems. The proposed algorithm integrates crossover operations from differential evolution (DE) with the original artificial bee colony (ABC) in order to balance the exploration and exploitation phases of the optimization process. A numerical construction project case study demonstrates the ability of MOABCDE-generated, non-dominated solutions to assist project managers to select an appropriate plan to optimize TCQT, which is an operation that is typically difficult and time-consuming. Comparisons between the MOABCDE and four currently used algorithms, including the non-dominated sorting genetic algorithm (NSGA-II), the multiple objective particle swarm optimization (MOPSO), the multiple objective differential evolution (MODE), and the multiple objective artificial bee colony (MOABC), verify the efficiency and effectiveness of the developed algorithm.

Original languageEnglish
Pages (from-to)176-186
Number of pages11
JournalKnowledge-Based Systems
Volume74
DOIs
StatePublished - 2015

Keywords

  • Artificial bee colony
  • Construction management
  • Differential evolution
  • Multi-objective analysis
  • Time-cost-quality tradeoff

Fingerprint

Dive into the research topics of 'Hybrid multiple objective artificial bee colony with differential evolution for the time-cost-quality tradeoff problem'. Together they form a unique fingerprint.

Cite this