Abstract
In the large scale Single machine total weighted tardiness scheduling problem (SMTWTSP) is a group of not-interrelated tasks having different parameters to be executed on one machine. The problem’s objective is to identify the minimum total weighted tardiness using a newly developed variant of the particle swarm optimization (PSO) algorithm. In the past, the PSO algorithm has proved itself as an efficient swarm intelligence based strategy to solve complex combinatorial problems. Here, in this article, the lunar cycle inspired local search technique is assimilated into PSO, and the designed PSO variant is termed as lunar cycle inspired PSO (LCPSO). The performance of the designed LCPSO is tested over 25 large SMTWTSP instances of job size 1000. The reported results show that the designed LCPSO is a competitive PSO variant that can be applied to provide an effective solution for the SMTWTSP type combinatorial optimization problem.
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References
Jain S, Kumar S, Sharma VK, Poonia RC, Lamba NP (2020) Modified differential evolution algorithm for solving minimum spanning tree. J Inf Optim Sci 41(2):633–639
Pandey S, Kumar S (2013) Enhanced artificial bee colony algorithm and its application to travelling salesman problem. Int J Technol Innov Res 2:1–10
Goyal A, Sharma VK, Kumar S, Kumar K (2020) Modified local link failure recovery multicast routing protocol for MANET. J Inf Optim Sci 41(2):669–677
Jain S, Kumar S, Sharma VK, Poonia RC (2020) Peregrine preying pattern based differential evolution for robot path planning. J Interdiscip Math 23(2):555–562
Jain S, Sharma VK, Kumar S (2020) Robot path planning using differential evolution. In: Sharma H, Govindan K, Poonia RC, Kumar S, Wael M (eds) Advances in computing and intelligent systems. Springer, Berlin, pp 531–537
Nayyar A, Nguyen NG, Kumari R, Kumar S (2020) Robot path planning using modified artificial bee colony algorithm. In: Frontiers in intelligent computing: theory and applications. Springer, Berlin, pp 25–36
Kumar S, Jadon P (2014) A novel hybrid algorithm for permutation flow shop scheduling. Int J Comput Sci Inf Technol 5(4):5057–5061
Kumari R, Sharma VK, Kumar S (2014) Fuzzified job shop scheduling algorithm. Int J Technol Innov Res 7:1–20
Manju SS, Kumar S, Nayyar A, Al-Turjman F, Mostarda L et al (2020) Proficient QoS-based target coverage problem in wireless sensor networks. IEEE Access 8:74315–74325
Lenstra JK, Kan AR, Brucker P (1977) Complexity of machine scheduling problems. Ann Discret Math 1:343–362
Schrage L, Baker KR (1978) Dynamic programming solution of sequencing problems with precedence constraints. Oper Res 26(3):444–449
Jouglet A, Baptiste P, Carlier J (2002) Exact procedures for single machine total cost scheduling. In: 2002 IEEE international conference on systems, man and cybernetics. vol 6. IEEE, pp 4
Potts CN, Van Wassenhove LN (1985) A branch and bound algorithm for the total weighted tardiness problem. Oper Res 33(2):363–377
Tanaka S, Fujikuma S, Araki M (2009) An exact algorithm for single-machine scheduling without machine idle time. J Sched 12(6):575–593
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE international conference on neural networks, 1995. Proceedings, vol 4. IEEE, pp 1942–1948
Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Erciyes Univ Press, Erciyes
Passino KM (2010) Bacterial foraging optimization. Int J Swarm Intell Res IJSIR 1(1):1–16
Sharma A, Chaturvedi R, Dwivedi U, Kumar S, Reddy S (2018) Firefly algorithm based effective gray scale image segmentation using multilevel thresholding and entropy function. Int J Pure Appl Math 118(5):437–443
Yang XS et al (2008) Firefly algorithm. Nat Inspir Metaheur Algorithms 20:79–90
Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: 2009, World congress on nature and biologically inspired computing (NaBIC). IEEE, pp 210–214
Kumar S, Kumari R (2018) Artificial bee colony, firefly swarm optimization, and bat algorithms. In: Advances in swarm intelligence for optimizing problems in computer science. pp 145–182
Yang XS (2011) Bat algorithm for multi-objective optimisation. Int J Bio Inspir Comput 3(5):267–274
Bhambu P, Kumar S (2016) Levy flight based animal migration optimization algorithm. In: 2016 international conference on recent advances and innovations in engineering (ICRAIE). IEEE, pp 1–5
Li X, Zhang J, Yin M (2014) Animal migration optimization: an optimization algorithm inspired by animal migration behavior. Neural Comput Appl 24(7–8):1867–1877
Bansal JC, Sharma H, Jadon SS, Clerc M (2014) Spider monkey optimization algorithm for numerical optimization. Memet Comput 6(1):31–47
Kumar S, Kumari R, Sharma VK (2015) Fitness based position update in spider monney optimization algorithm. Procedia Comput Sci 62:442–449
Sharma H, Bansal JC, Arya K (2013) Diversity measures in artificial bee colony. In: Proceedings of seventh international conference on bio-inspired computing: theories and applications (BIC-TA 2012). Springer, Berlin, pp 299–314
Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-wesley, New York
Kumar S, Jain S, Sharma H (2018) Genetic algorithms. In: Advances in swarm intelligence for optimizing problems in computer science. pp 27–52
Sharma P, Sharma H, Kumar S, Bansal JC (2019) A review on scale factor strategies in differential evolution algorithm. In: Bansal JC, Das KN, Nagar A, Deep K, Ojha AK (eds) Soft computing for problem solving. Springer, Berlin, pp 925–943
Sharma P, Sharma H, Kumar S, Sharma K (2019) Black-hole gbest differential evolution algorithm for solving robot path planning problem. In: Dhiman G, Kumar V (eds) Harmony search and nature inspired optimization algorithms. Springer, Berlin, pp 1009–1022
Storn R, Price K (1995) Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces. International Computer Science Institute, New York
Rana PS, Sharma H, Bhattacharya M, Shukla A (2012) Guided reproduction in differential evolution. In: Asia-pacific conference on simulated evolution and learning. Springer, Berlin, pp 117–127
Tasgetiren MF, Sevkli M, Liang YC, Gencyilmaz G (2004) Particle swarm optimization algorithm for single machine total weighted tardiness problem. In: Evolutionary computation, 2004. CEC2004. Congress on. vol 2. IEEE, pp 1412–1419
Cheng TE, Ng C, Yuan J, Liu Z (2005) Single machine scheduling to minimize total weighted tardiness. Eur J Oper Res 165(2):423–443
Kolliopoulos SG, Steiner G (2006) Approximation algorithms for minimizing the total weighted tardiness on a single machine. Theoret Comput Sci 355(3):261–273
Ferrolho A, Crisóstomo M (2007) Single machine total weighted tardiness problem with genetic algorithms. In: Computer systems and applications, 2007. AICCSA’07. IEEE/ACS international conference on. IEEE, pp 1–8
Tasgetiren MF, Pan QK, Liang YC (2009) A discrete differential evolution algorithm for the single machine total weighted tardiness problem with sequence dependent setup times. Comput Oper Res 36(6):1900–1915
Ahmadizar F, Hosseini L (2011) A novel ant colony algorithm for the single-machine total weighted tardiness problem with sequence dependent setup times. Int J Comput Intell Syst 4(4):456–466
Yin Y, Wu CC, Wu WH, Cheng SR (2012) The single-machine total weighted tardiness scheduling problem with position-based learning effects. Comput Oper Res 39(5):1109–1116
Santosa B, Safitri AL (2015) Biogeography-based optimization (BBO) algorithm for single machine total weighted tardiness problem (SMTWTP). Procedia Manuf 4:552–557
Marichelvam M, Geetha M (2016) A hybrid cuckoo search metaheuristic algorithm for solving single machine total weighted tardiness scheduling problems with sequence dependent setup times. Int J Comput Complex Intell Algorithms 1(1):23–34
Ding J, Lü Z, Cheng T, Xu L (2017) A hybrid evolutionary approach for the single-machine total weighted tardiness problem. Comput Ind Eng 108:70–80
Petrynski K, Szost R, Pozniak-Koszalka I, Koszalka L, Kasprzak A (2018) Single machine weighted tardiness problem: an algorithm and experimentation system. In: International conference on computational collective intelligence. Springer, Berlin, pp 36–44
Chaabane L (2019) A novel hybrid algorithm for minimizing total weighted tardiness cost. Comput Sci J Moldova 80(2):230–241
Lamiche C, Bouchra D (2019) An efficient system for minimizing total weighted tardiness cost on single machine. In: 2019 International conference on digitization (ICD). IEEE, pp 57–59
Poongothai V, Godhandaraman P, Jenifer AA (2019) Single machine scheduling problem for minimizing total tardiness of a weighted jobs in a batch delivery system, stochastic rework and reprocessing times. In: AIP conference proceedings, vol 2112. AIP Publishing LLC, pp 020132
Harrath Y, Mahjoub A, Kaabi J (2019) A multi-objective genetic algorithm to solve a single machine scheduling problem with setup-times. Int J Serv Oper Manag 33(4):494–511
Romanuke VV (2019) Accurate total weighted tardiness minimization in tight-tardy progressive single machine scheduling with preemptions by no idle periods. KPI Sci News 5–6:26–42
Abitz D, Hartmann T, Middendorf M (2020) A weighted population update rule for PACO applied to the single machine total weighted tardiness problem. arXiv:200408433
Jun S, Lee S (2020) Learning dispatching rules for single machine scheduling with dynamic arrivals based on decision trees and feature construction. Int J Product Res 1–19. https://doi.org/10.1080/00207543.2020.1741716
Queiroga E, Pinheiro RG, Christ Q, Subramanian A, Pessoa AA (2020) Iterated local search for single machine total weighted tardiness batch scheduling. J Heuris 1–86. https://doi.org/10.1007/s10732-020-09461-x
Joshi SK, Bansal JC (2020) Parameter tuning for meta-heuristics. Knowl Based Syst 189:105094
Gopal A, Sultani MM, Bansal JC (2020) On stability analysis of particle swarm optimization algorithm. Arab J Sci Eng 45:2385–2394. https://doi.org/10.1007/s13369-019-03991-8
Lalwani S, Sharma H, Satapathy SC, Deep K, Bansal JC (2019) A survey on parallel particle swarm optimization algorithms. Arab J Sci Eng 44(4):2899–2923
Bhambu P, Kumar S, Sharma K (2018) Self balanced particle swarm optimization. Int J Syst Assur Eng Manag 9(4):774–783
Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: The 1998 IEEE international conference on evolutionary computation proceedings, 1998. IEEE world congress on computational intelligence. IEEE, pp 69–73
Çomak E (2019) A particle swarm optimizer with modified velocity update and adaptive diversity regulation. Expert Syst 36(1):e12330
Wang C, Song W (2019) A modified particle swarm optimization algorithm based on velocity updating mechanism. Ain Shams Eng J 10(4):847–866
Rathore A, Sharma H (2017) Review on inertia weight strategies for particle swarm optimization. In: Proceedings of sixth international conference on soft computing for problem solving. Springer, Berlin, pp 76–86
Grubelnik V, Marhl M, Repnik R (2018) Determination of the size and depth of craters on the moon. Center Educ Policy Stud J 8(1):35–53
Smith R (2015) Moon phases explained. https://www.absolutesoulsecrets.com/supermoon/moon-phases-explained/. Retrieved 10 May 2020
Gwern (2013) Lunar circadian rhythms. https://www.gwern.net/Lunar-sleep. Retrieved 10 May 2020
Sharma N, Sharma H, Sharma A (2019) An effective solution for large scale single machine total weighted tardiness problem using lunar cycle inspired artificial bee colony algorithm. IEEE/ACM Trans Comput Biol Bioinf 17(5):1573–1581. https://doi.org/10.1109/TCBB.2019.2897302
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Gupta, S., Kumari, R. & Singh, R.P. Lunar cycle inspired PSO for single machine total weighted tardiness scheduling problem. Evol. Intel. 14, 1355–1366 (2021). https://doi.org/10.1007/s12065-020-00556-9
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DOI: https://doi.org/10.1007/s12065-020-00556-9