Abstract
Generally, local search algorithms are better than population-based algorithms in exploiting the search space. Meanwhile, the population-based algorithms are better in exploring the search space. Lately, a Hybrid Moth Optimization Algorithm (HMOA) was proposed as a decent algorithm for the permutation flow shop scheduling problem. However, this algorithm has a limitation in determining the stopping condition and its operation may be early terminated, thus adversely impacting exploration and exploitation of the solution. Therefore, the objective of this paper was to propose a Modified Hybrid Moth Optimization Algorithm (MHMOA) in an effort to adaptively tune the stopping condition and, then, to evaluate performance of this algorithm. Taillard benchmark datasets for the flow shop scheduling problem were used in this study as an assessment domain. Taking this study goal into consideration, performance of MHMOA was compared with levels of performance of the HMOA and other related algorithms retrieved from the literature. The study results demonstrate that performance of the MHMOA compares with levels of performance of the other investigated algorithms and that it has reasonably good performance on many of the studied Taillard benchmark datasets.
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Data Availability
The raw data (benchmarks) of the quantitative evaluations are available from http://mistic.heig-vd.ch/taillard/.
References
Johnson SM. Optimal two-and three-stage production schedules with setup times included. Nav Res Logist Q. 1954;1(1):61–8.
Nawaz M, Enscore EE, Ham I. A heuristic algorithm for the m-machine, n-job flowshop sequencing problem. Omega. 1983;11(1):91–5.
Ayvaz D, Topcuoglu H, Gurgen F. Performance evaluation of evolutionary heuristics in dynamic environments. Appl Intell. 2012;37(1):130–44. https://doi.org/10.1007/s10489-011-0317-9.
Abuhamdah A. Adaptive acceptance criterion (AAC) algorithm for optimization problems. J Comput Sci. 2015;11(4):675–91. https://doi.org/10.3844/jcssp.2015.675.691.
Abuhamdah A, Alzaqebah M, Jawarneh S, Althunibat A, Banikhalaf M. Moth optimisation algorithm with local search for the permutation flow shop scheduling problem. Int J Comput Appl Technol. 2021. https://doi.org/10.1504/IJCAT.2021.10038917.
Mirjalili S. Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst. 2015;89:228–49. https://doi.org/10.1016/j.knosys.2015.07.006.
Dorigo M. Ant colony optimization. Scholarpedia. 2007;2(3):1461. https://doi.org/10.4249/scholarpedia.1461.
Ding JY, Song S, Gupta JN, Zhang R, Chiong R, Wu C. An improved iterated greedy algorithm with a Tabu-based reconstruction strategy for the no-wait flowshop scheduling problem. Appl Soft Comput. 2015;30:604–13. https://doi.org/10.1016/j.asoc.2015.02.006.
Tasgetiren MF, Kizilay D, Pan QK, Suganthan PN. Iterated greedy algorithms for the blocking flowshop scheduling problem with makespan criterion. Comput Oper Res. 2017;77:111–26. https://doi.org/10.1016/j.cor.2016.07.002.
Zhao F, Liu H, Zhang Y, Ma W, Zhang C. A discrete water wave optimization algorithm for no-wait flow shop scheduling problem. Expert Syst Appl. 2017. https://doi.org/10.1016/j.eswa.2017.09.028.
Jeen Robert BJ, Rajkumar R. A hybrid algorithm for multi-objective optimization of minimizing makespan and total flow time in permutation flow shop scheduling problems. J Inf Technol Control. 2019;48(1):47–57. https://doi.org/10.5755/j01.itc.48.1.20909.
Syarid A, Eamiliana A, Lumbanraja P. Study on genetic algorithm (GA) approaches for solving flow shopscheduling problem (FSSP). IOP Conf Ser Mater Sci Eng. 2020;857:012009. https://doi.org/10.1088/1757-899X/857/1/012009.
Taillard E. Benchmarks for basic scheduling problems. Eur J Oper Res. 1993;64(2):278–85.
Mehne SHH, Mirjalili S. Moth-flame optimization algorithm: theory, literature review, and application in optimal nonlinear feedback control design. In: Nature-inspired optimizers. Cham: Springer; 2020.
Yang X, Luo Q, Zhang J, Wu X, Zhou Y. Moth swarm algorithm for clustering analysis. In: Intelligent computing methodologies, ICIC 2017. Cham: Springer; 2017.
Fausto F, Reyna-Orta A, Cuevas E, Andrade ÁG, Perez-Cisneros M. From ants to whales: metaheuristics for all tastes. Artif Intell Rev. 2019;53:753–810. https://doi.org/10.1007/s10462-018-09676-2.
Khalilpourazari S, Khalilpourazary S. An efficient hybrid algorithm based on water cycle and Moth-flame optimization algorithms for solving numerical and constrained engineering optimization problems. Soft Comput. 2019;23(5):1699–722. https://doi.org/10.1007/s00500-017-2894-y.
Holland JH, Langton C, Wilson SW, Varela FJ, Bourgine P, Koza JR, Book AB. Genetic programming complex adaptive systems genetic programming on the programming of computers by means of natural selection. London: MIT Press; 1992.
Simon D. Biogeography-based optimization. IEEE Trans Evolut Comput. 2008;12(6):702–13. https://doi.org/10.1109/TEVC.2008.919004.
Mirjalili S, Mirjalili SM, Lewis A. Grey wolf optimizer. Adv Eng Softw. 2014;69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007.
Buch H, Trivedi IN, Jangir P. Moth flame optimization to solve flow with non-parametric statistical evaluation validation. Cogent Eng. 2017;4:1286731. https://doi.org/10.1080/23311916.2017.1286731.
Abdel-Basset M, Manogaran G, El-Shahat D, Mirjalili S. A hybrid whale optimization algorithm based on local search strategy for the permutation flow shop scheduling problem. Future Gener Comput Syst. 2018;85:129–45.
Alzaqebah M, Jawarneh S, Sarim HM, Abdullah S. Bees algorithm for vehicle routing problems with time windows. Int J Mach Learn Comput. 2018;8(3):236–40. https://doi.org/10.18178/ijmlc.2018.8.3.693.
Zhang T, Yang C, Zhao X. Using improved brainstorm optimization algorithm for hardware/software partitioning. Appl Sci. 2019;9(5):866. https://doi.org/10.3390/app9050866.
Qawqzeh YK, Jaradat G, Al-Yousef A, Abu-Hamdah A, Almarashdeh I, Alsmadi M, Tayfour M, Shaker K, Haddad F. Applying the big bang-big crunch metaheuristic to large-sized operational problems. Int J Electr Comput Eng (IJECE). 2020;10(3):2848–2502. https://doi.org/10.11591/ijece.v10i3.pp2484-2502.
Abuhamdah A. Adaptive elitist-ant system for medical clustering problem. J King Saud Univ Comput Inf Sci. 2020;32(6):709–17. https://doi.org/10.1016/j.jksuci.2018.08.007.
Abuhamdah A. Adaptive black widow optimization algorithm for data clustering. Int J Math Oper Res. 2020. https://doi.org/10.1504/IJMOR.2020.10032253.
Abuhamdah A, Boulila W, Jaradat GM, Quteishat AM, Alsmadi MK, Almarashdeh IA. A novel population-based local search for nurse rostering problem. Int J Electr Comput Eng (IJECE). 2021;11(1):471–80. https://doi.org/10.11591/ijece.v11i1.pp471-480.
Abuhamdah A. Adaptive elitist-ant system for solving combinatorial optimization problems. Appl Soft Comput. 2021;105:107293. https://doi.org/10.1016/j.asoc.2021.107293.
Pan QK, Wang L, Zhao BH. An improved iterated greedy algorithm for the no-wait flow shop scheduling problem with makespan criterion. Int J Adv Manuf Technol. 2008;38(7):778–86. https://doi.org/10.1007/s00170-007-1120-y.
Tseng LY, Lin YT. A hybrid genetic algorithm for no-wait flowshop scheduling problem. Int J Prod Econ. 2010;128(1):144–52.
Davendra D, Bialic-Davendra M. Scheduling flow shops with blocking using a discrete self-organising migrating algorithm. Int J Prod Res. 2013;51(8):2200–18. https://doi.org/10.1080/00207543.2012.711968.
Zhang J, Chen J, Zhang H. Job-shop schedule modelling and parents-crossover evolutionary optimisation for integrated production schedules. Int J Comput Appl Technol. 2018;58(4):288–95. https://doi.org/10.1504/IJCAT.2018.095947.
Abuhamdah A, Ayob M, Kendall G, Sabar NR. Population based local search for university course timetabling problems. Appl Intell. 2014;40:44–53. https://doi.org/10.1007/s10489-013-0444-6.
Farag TH, Hassan WA, Ayad HA, AlBahussain AS, Badawi UA, Alsmadi MK. Extended absolute fuzzy connectedness segmentation algorithm utilizing region and boundary-based information. Arabian J Sci Eng. 2017;42:3573–83. https://doi.org/10.1007/s13369-017-2577-0.
Guezouli L, Abdelhamid S. Multi-objective optimisation using genetic algorithm based clustering for multi-depot heterogeneous fleet vehicle routing problem with time windows. Int J Math Oper Res. 2018;13(3):332–49. https://doi.org/10.1504/IJMOR.2018.094850.
Rajak S, Parthiban P, Dhanalakshmi R. A hybrid metaheuristics approach for a multi-depot vehicle routing problem with simultaneous deliveries and pickups. Int J Math Oper Res. 2019;15(2):197–210. https://doi.org/10.1504/IJMOR.2019.101619.
Hamid M, Bastan M, Hamid M, Sheikhahmadi F. Solving a stochastic multi-objective and multi-period hub location problem considering economic aspects by meta-heuristics: application in public transportation. Int J Comput Appl Technol. 2019;60(3):183–202. https://doi.org/10.1504/IJCAT.2019.100304.
Keynia F, Heydari A. A new short-term energy price forecasting method based on wavelet neural network. Int J Math Oper Res. 2019;14(1):1–14. https://doi.org/10.1504/IJMOR.2019.096975.
Abu Khurma R, Aljarah I, Sharieh A, Mirjalili S. Evolopy-fs: an open-source nature-inspired optimization framework in python for feature selection. In: Evolutionary machine learning techniques. Singapore: Springer; 2020. p. 131–73.
Taha A, Darwish A, Hassanien AE, ElKholy A. Arabian horse identification based on whale optimised multi-class support vector machine. Int J Comput Appl Technol. 2020;63(1/2):83–92. https://doi.org/10.1504/IJCAT.2020.107910.
Prakash B, Viswanathan V. A comparative study of meta-heuristic optimisation techniques for prioritisation of risks in agile software development selection. Int J Comput Appl Technol. 2020;62(2):175–88. https://doi.org/10.1504/IJCAT.2020.104688.
Zobolas GI, Tarantilis CD, Ioannou G. Minimizing makespan in permutation flow shop scheduling problems using a hybrid metaheuristic algorithm. Comput Oper Res. 2009;36(4):1249–67. https://doi.org/10.1016/j.cor.2008.01.007.
Agárdi A, Nehéz K, Hornyák O, Kóczy LT. A hybrid discrete bacterial memetic algorithm with simulated annealing for optimization of the flow shop scheduling problem. Symmetry. 2021. https://doi.org/10.3390/sym13071131.13,1131.
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Abuhamdah, A. Modified Hybrid Moth Optimization Algorithm for PFSS Problem. SN COMPUT. SCI. 4, 298 (2023). https://doi.org/10.1007/s42979-023-01743-y
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DOI: https://doi.org/10.1007/s42979-023-01743-y