Application of Clonal Selection Algorithm in Construction Site Utilization Planning Optimization

Xi Wang, Abhijeet S. Deshpande, Gabriel B. Dadi, Baris Salman

Research output: Contribution to journalArticle

Abstract

Various algorithms inspired by evolutionary and physical processes have been extensively applied in solving complex construction engineering optimization problems. In this paper, Artificial Immune Systems (AIS), a computational approach inspired by the processes of human immune system, is introduced in terms of its basic mechanisms and its applications in construction engineering. Specifically, Clonal Selection Algorithm (CSA), one of main algorithms that form AIS, is based on clonal selection process of the immune system which includes the selection, hypermutation, and receptor editing processes. We discuss the CSA in detail and present its application in the classic construction optimization problem, construction site utilization planning (CSUP), which is the decision making process for identifying the most optimal layout of temporary facilities designed to support the construction process. When applied to a test case published in research literature, we found that CSA shows a robust capacity to search the solution space effectively and efficiently.

Original languageEnglish (US)
Pages (from-to)267-273
Number of pages7
JournalProcedia Engineering
Volume145
DOIs
StatePublished - 2016

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Immune system
Planning
Decision making

Keywords

  • Artificial Immune Systems
  • Clonal Selection Algorithm
  • CLONALG
  • Optimization

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Application of Clonal Selection Algorithm in Construction Site Utilization Planning Optimization. / Wang, Xi; Deshpande, Abhijeet S.; Dadi, Gabriel B.; Salman, Baris.

In: Procedia Engineering, Vol. 145, 2016, p. 267-273.

Research output: Contribution to journalArticle

Wang, Xi ; Deshpande, Abhijeet S. ; Dadi, Gabriel B. ; Salman, Baris. / Application of Clonal Selection Algorithm in Construction Site Utilization Planning Optimization. In: Procedia Engineering. 2016 ; Vol. 145. pp. 267-273.
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