Fully informed ABC algorithm for large scale job shop scheduling problem. (26th September 2022)

• Record Type: Journal Article
• Authors: Sharma, Kavita; Gupta, P.C.
• Is Part Of: International journal of intelligent engineering informatics. Volume 10:Number 2(2022)
• Page Start: 105
• Page End: 118
• Publication Date: 2022-09-26
• Subjects: fully informed learning -- swarm intelligence -- nature-inspired algorithms -- ABC -- artificial bee colony -- large-scale JSS problem; Artificial intelligence -- Engineering applications -- Periodicals; Engineering -- Computer programs -- Periodicals; Knowledge management -- Periodicals; 620.0028563
• Languages: English
• ISSNs: 1758-8715

Abstract

The large-scale job-shop scheduling problem (LSJSSP) is a complex scheduling problems. Previously, although the nature-inspired algorithm, specially the swarm intelligence (SIA) based algorithms have been efficiently applied to solve the LSJSSP, finding the best solution for LSJSSP instances remains a challenging task. Therefore, in this paper, a novel SIA is applied to solve the 105 LSJSSP instances. The selected SIA is fully informed artificial bee colony (FABC) algorithm. The FABC algorithm is a variant of the ABC algorithm in which position update process is inspired from the GABC. In the FABC, onlooker bee process of the ABC strategy is modified and designed such that the new position of the solution search agent is obtained while learning from all the nearby agents. The results obtained by the FABC is compared with the strategies available in the literature. The results analysis shows that the proposed approach to solving LSJSSP is competitive in the field of SIA.