Hybridization of genetic algorithm and fully informed particle swarm for solving the multi-mode resource-constrained project scheduling problem. Issue 3 (4th March 2017)

• Record Type: Journal Article
• Authors: Sebt, M. H.; Afshar, M. R.; Alipouri, Y.
• Is Part Of: Engineering optimization. Volume 49:Issue 3(2017)
• Page Start: 513
• Page End: 530
• Publication Date: 2017-03-04
• Subjects: combinatorial optimization -- multi-mode project scheduling -- resource constraints -- hybrid GA-FIPS algorithm -- random key representation; Engineering design -- Periodicals; Mathematical optimization -- Periodicals; 620.0042
• DOI: 10.1080/0305215X.2016.1197610
• Languages: English
• ISSNs: 0305-215X

Abstract

ABSTRACT: In this article, the genetic algorithm (GA) and fully informed particle swarm (FIPS) are hybridized for solving the multi-mode resource-constrained project scheduling problem (MRCPSP) with minimization of project makespan as the objective subject to resource and precedence constraints. In the proposed hybrid genetic algorithm–fully informed particle swarm algorithm (HGFA), FIPS is a popular variant of the particle swarm optimization algorithm. A random key and the related mode list representation schemes are used as encoding schemes, and the multi-mode serial schedule generation scheme (MSSGS) is considered as the decoding procedure. Furthermore, the existing mode improvement procedure in the literature is modified. The results show that the proposed mode improvement procedure remarkably improves the project makespan. Comparing the results of the proposed HGFA with other approaches using the well-known PSPLIB benchmark sets validates the effectiveness of the proposed algorithm to solve the MRCPSP.