Title: Moth optimisation algorithm with local search for the permutation flow shop scheduling problem
Authors: Anmar Abuhamdah; Malek Alzaqebah; Sana Jawarneh; Ahmad Althunibat; Mustafa Banikhalaf
Addresses: Department of Management Information Systems, College of Business Administration, Taibah University, Madina, Saudi Arabia ' Department of Mathematics, College of Science, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia; Basic & Applied Scientific Research Centre, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia ' Computer Science Department, Community College Dammam, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia ' Department of Software Engineering, Faculty of Science and Information Technology, Al-Zaytoonah University of Jordan, Amman, Jordan ' Department of Computer Science, Yarmouk University, Irbid, Jordan
Abstract: This work investigates the use of the Moth-Flame Optimisation (MFO) algorithm in solving the Permutation Flow Shop Scheduling Problem and proposes further optimisations. MFO is a population-based approach that simulates the behaviour of real moths by exploiting the search space randomly without employing any local searches that may stick in local optima. Therefore, we propose a Hybrid Moth Optimisation Algorithm (HMOA) that is embedded within a local search to better exploit the search space. HMOA entails employing three search procedures to intensify and diversify the search space in order to prevent the algorithm's becoming trapped in local optima. Furthermore, HMOA adaptively selects the search procedure based on improvement ranks. In order to evaluate the performances of MFO and HMOA, we perform a comparison against other approaches drawn from the literature. Experimental results demonstrate that HMOA is able to produce better-quality solutions and outperforms many other approaches on the Taillard benchmark.
Keywords: flow shop scheduling; flow shop scheduling problem; makespan; moth-flame optimisation algorithm; local search; adaptive moth optimisation algorithm.
International Journal of Computer Applications in Technology, 2021 Vol.65 No.3, pp.189 - 208
Received: 08 Aug 2020
Accepted: 16 Oct 2020
Published online: 19 Jun 2021 *