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Scheduling Job Machines with Swap Sequence to Minimize Makespan Using Spider Monkey Optimization Algorithm
Asnan Cirua A.A.
Proceeding 6th International Conference on Information Technology Information Systems and Electrical Engineering Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability Icitisee 2022
Abstract
Job scheduling is an NP-Hard combinatorial problem, so it requires optimization techniques to solve the problem. Several studies were conducted using various heuristic and meta-heuristic techniques. This paper presents the spider monkey optimization (SMO) algorithm, a newcomer algorithm in Swarm Intelligence. We added the swap sequence technique in the solution search process. Swap rate parameter testing was carried out at 0.2, 0.5, and 0.8; the results show that a swap rate of 0.2 can provide a smaller makespan but increases computation time. We analyze against the JSPLIB dataset test, which indicates the feasibility of the proposed algorithm. A comparison was made of the SMO, GA, HGA, and MCSA algorithms, and the results showed that MCSA was still superior for the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$10\mathrm{x}10$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$10\mathrm{x}5$</tex> datasets. The results of the 15×5 and 20×5 datasets show that SMO tends to be superior to GA and HGA.