[1]
|
Z.W. Geem, Music-inspired harmony search algorithm: theory and applications, Springer, 2009.
|
[2]
|
J. Kennedy, Particle swarm optimization, in Encyclopedia of machine learning, Springer, 2011, 760–766.
|
[3]
|
M. Dorigo, V. Maniezzo and A. Colorni, Ant system: optimization by a colony of cooperating agents, IEEE T. Syst. Man. Cy. B., 26 (1996), 29–41.
|
[4]
|
D. Karaboga and B. Basturk, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm, J. Global. Optim., 39 (2007), 459–471.
|
[5]
|
X. S. Yang and S. Deb, Cuckoo search via lévy flights, in Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on, IEEE, 2009, 210–214.
|
[6]
|
J. H. Holland, Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, MIT press, 1992.
|
[7]
|
R. Storn and K. Price, Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces, J. Global. Optim., 11 (1997), 341–359.
|
[8]
|
S. Kirkpatrick, Optimization by simulated annealing: Quantitative studies, J. Stat. Phy., 34 (1984), 975–986.
|
[9]
|
A. Y. Lam and V. O. Li, Chemical-reaction-inspired metaheuristic for optimization, IEEE Trans. Evol. Comput., 14 (2010), 381–399.
|
[10]
|
Z. W. Geem, J. H. Kim and G. V. Loganathan, A new heuristic optimization algorithm: harmony search, Simulation, 76 (2001), 60–68.
|
[11]
|
J. Yi, X. Li, L. Gao, et al., Optimal design of photovoltaic-wind hybrid renewable energy system using a discrete geometric selective harmony search, in Computer Supported Cooperative Work in Design (CSCWD), 2015 IEEE 19th International Conference on, IEEE, (2015), 499–504.
|
[12]
|
A. Chauhan and R. Saini, Discrete harmony search based size optimization of integrated renewable energy system for remote rural areas of uttarakhand state in india, Renew. Energy, 94 (2016), 587–604.
|
[13]
|
Z. W. Geem and Y. Yoon, Harmony search optimization of renewable energy charging with energy storage system, Int. J. Electr. Power Energy Syst., 86 (2017), 120–126.
|
[14]
|
C. Camacho-Gómez, S. Jiménez-Fernández, R. Mallol-Poyato, et al., Optimal design of microgrid's network topology and location of the distributed renewable energy resources using the harmony search algorithm, Soft Comput., 1 (2018), 1–16.
|
[15]
|
H. B. Ouyang, L. Q. Gao, S. Li, et al., Improved novel global harmony search with a new relaxation method for reliability optimization problems, Inform. Sci., 305 (2015), 14–55.
|
[16]
|
W. Zeng, J. Yi, X. Rao, et al., A two-stage path planning approach for multiple car-like robots based on ph curves and a modified harmony search algorithm, Eng. Optimiz., 49 (2017), 1995– 2012.
|
[17]
|
S. Kundu and D. R. Parhi, Navigation of underwater robot based on dynamically adaptive harmony search algorithm, Memet. Comput., 8 (2016), 125–146.
|
[18]
|
J. P. Papa, W. Scheirer and D. D. Cox, Fine-tuning deep belief networks using harmony search, Appl. Soft. Comput., 46 (2016), 875–885.
|
[19]
|
W. Y. Lee, S. M. Park and K. B. Sim, Optimal hyperparameter tuning of convolutional neural networks based on the parameter-setting-free harmony search algorithm, Optik, 172 (2018), 359– 367.
|
[20]
|
S. Kulluk, L. Ozbakir and A. Baykasoglu, Training neural networks with harmony search algorithms for classification problems, Eng. Appl. Artif. Intell., 25 (2012), 11–19.
|
[21]
|
S. Mun and Y. H. Cho, Modified harmony search optimization for constrained design problems, Expert. Syst. Appl., 39 (2012), 419–423.
|
[22]
|
V. R. Pandi and B. K. Panigrahi, Dynamic economic load dispatch using hybrid swarm intelligence based harmony search algorithm, Expert. Syst. Appl., 38 (2011), 8509–8514.
|
[23]
|
A. Kusiak, Intelligent manufacturing systems., Prentice Hall Press, 200 Old Tappan Toad, Old Tappan, NJ 07675, USA, (1990), 448.
|
[24]
|
T. Ghosh, S. Sengupta, M. Chattopadhyay, et al., Meta-heuristics in cellular manufacturing: A state-of-the-art review, Int. J. Ind. Eng. Comput., 2 (2011), 87–122.
|
[25]
|
M. Alavidoost, M. F. Zarandi, M. Tarimoradi, et al., Modified genetic algorithm for simple straight and u-shaped assembly line balancing with fuzzy processing times, J. Intell. Manuf., 28 (2017), 313–336.
|
[26]
|
C. L. Kuo, C. H. Chu, Y. Li, et al., Electromagnetism-like algorithms for optimized tool path planning in 5-axis flank machining, Comput. Ind. Eng., 84 (2015), 70–78.
|
[27]
|
X. Li, C. Lu, L. Gao, et al., An effective multi-objective algorithm for energy efficient scheduling in a real-life welding shop, IEEE T. Ind. Inform., 14 (2018), 5400–5409.
|
[28]
|
C. Lu, L. Gao, X. Li, et al., A multi-objective approach to welding shop scheduling for makespan, noise pollution and energy consumption, J. Cleaner. Prod., 196 (2018), 773–787.
|
[29]
|
C. Lu, L. Gao, Q. Pan, et al., A multi-objective cellular grey wolf optimizer for hybrid flowshop scheduling problem considering noise pollution, Appl. Soft. Comput., 75 (2019), 728–749.
|
[30]
|
X. S. Yang, Harmony search as a metaheuristic algorithm, in Music-inspired harmony search algorithm, Springer, (2009), 1–14.
|
[31]
|
G. Ingram and T. Zhang, Overview of applications and developments in the harmony search algorithm, in Music-inspired harmony search algorithm, Springer, (2009), 15–37.
|
[32]
|
O. Moh'd Alia and R. Mandava, The variants of the harmony search algorithm: an overview, Artif. Intell. Rev., 36 (2011), 49–68.
|
[33]
|
X. Wang, X. Z. Gao and K. Zenger, The variations of harmony search and its current research trends, in An Introduction to Harmony Search Optimization Method, Springer, (2015), 21–30.
|
[34]
|
A. Askarzadeh, Solving electrical power system problems by harmony search: a review, Artif. Intell. Rev., 47 (2017), 217–251.
|
[35]
|
A. Askarzadeh and E. Rashedi, Harmony search algorithm: Basic concepts and engineering applications, in Intelligent Systems: Concepts, Methodologies, Tools, and Applications, 1–30.
|
[36]
|
J. Yi, X. Li, C. H. Chu, et al., Parallel chaotic local search enhanced harmony search algorithm for engineering design optimization, J. Intell. Manuf., 30 (2019), 405–428.
|
[37]
|
M. Mahdavi, M. Fesanghary and E. Damangir, An improved harmony search algorithm for solving optimization problems, Appl. Math. Comput., 188 (2007), 1567–1579.
|
[38]
|
M. G. Omran and M. Mahdavi, Global-best harmony search, Appl. Math. Comput., 198 (2008), 643–656.
|
[39]
|
Q. K. Pan, P. N. Suganthan, M. F. Tasgetiren, et al., A self-adaptive global best harmony search algorithm for continuous optimization problems, Appl. Math. Comput., 216 (2010), 830–848.
|
[40]
|
D. Zou, L. Gao, J.Wu, et al., Novel global harmony search algorithm for unconstrained problems, Neurocomputing, 73 (2010), 3308–3318.
|
[41]
|
J. Chen, Q. K. Pan and J. Q. Li, Harmony search algorithm with dynamic control parameters, Appl. Math. Comput., 219 (2012), 592–604.
|
[42]
|
R. Enayatifar, M. Yousefi, A. H. Abdullah, et al., Lahs: a novel harmony search algorithm based on learning automata, Commun. Nonlinear Sci. Numer. Simul., 18 (2013), 3481–3497.
|
[43]
|
A. Kattan and R. Abdullah, A dynamic self-adaptive harmony search algorithm for continuous optimization problems, Appl. Math. Comput., 219 (2013), 8542–8567.
|
[44]
|
K. Luo, A novel self-adaptive harmony search algorithm, J. Appl. Math., 2013 (2013), 1–16.
|
[45]
|
X.Wang and X. Yan, Global best harmony search algorithm with control parameters co-evolution based on pso and its application to constrained optimal problems, Appl. Math. Comput., 219 (2013), 10059–10072.
|
[46]
|
J. Contreras, I. Amaya and R. Correa, An improved variant of the conventional harmony search algorithm, Appl. Math. Comput., 227 (2014), 821–830.
|
[47]
|
M. Khalili, R. Kharrat, K. Salahshoor, et al., Global dynamic harmony search algorithm: Gdhs, Appl. Math. Comput., 228 (2014), 195–219.
|
[48]
|
V. Kumar, J. K. Chhabra and D. Kumar, Parameter adaptive harmony search algorithm for unimodal and multimodal optimization problems, J. Comput. Sci., 5 (2014), 144–155.
|
[49]
|
G. Li and Q. Wang, A cooperative harmony search algorithm for function optimization, Math. Probl. Eng., 2014 (2014), 1–14.
|
[50]
|
I. Amaya, J. Cruz and R. Correa, Harmony search algorithm: a variant with self-regulated fretwidth, Appl. Math. Comput., 266 (2015), 1127–1152.
|
[51]
|
J. Kalivarapu, S. Jain and S. Bag, An improved harmony search algorithm with dynamically varying bandwidth, Eng. Optimiz., 48 (2016), 1091–1108.
|
[52]
|
Y. Wang, Z. Guo and Y. Wang, Enhanced harmony search with dual strategies and adaptive parameters, Soft Comput., 21 (2017), 4431–4445.
|
[53]
|
Z. Guo, H. Yang, S. Wang, et al., Adaptive harmony search with best-based search strategy, Soft Comput., 22 (2018), 1335–1349.
|
[54]
|
M. A. Al-Betar, I. A. Doush, A. T. Khader, et al., Novel selection schemes for harmony search, Appl. Math. Comput., 218 (2012), 6095–6117.
|
[55]
|
M. A. Al-Betar, A. T. Khader, Z. W. Geem, et al., An analysis of selection methods in memory consideration for harmony search, Appl. Math. Comput., 219 (2013), 10753–10767.
|
[56]
|
M. Castelli, S. Silva, L. Manzoni, et al., Geometric selective harmony search, Inform. Sci., 279 (2014), 468–482.
|
[57]
|
X. Gao, X. Wang, S. Ovaska, et al., A hybrid optimization method of harmony search and opposition-based learning, Eng. Optimiz., 44 (2012), 895–914.
|
[58]
|
A. Kaveh and M. Ahangaran, Social harmony search algorithm for continuous optimization, Iran. J. Sci. Technol. Trans. B-Eng., 36 (2012), 121–137.
|
[59]
|
P. Yadav, R. Kumar, S. K. Panda, et al., An intelligent tuned harmony search algorithm for optimisation, Inform. Sci., 196 (2012), 47–72.
|
[60]
|
M. A. Al-Betar, A. T. Khader, M. A. Awadallah, et al., Cellular harmony search for optimization problems, J. Appl. Math., 2013 (2013), 1–20.
|
[61]
|
S. Ashrafi and A. Dariane, Performance evaluation of an improved harmony search algorithm for numerical optimization: Melody search (ms), Eng. Appl. Artif. Intell., 26 (2013), 1301–1321.
|
[62]
|
M. El-Abd, An improved global-best harmony search algorithm, Appl. Math. Comput., 222 (2013), 94–106.
|
[63]
|
B. H. F. Hasan, I. A. Doush, E. Al Maghayreh, et al., Hybridizing harmony search algorithm with different mutation operators for continuous problems, Appl. Math. Comput., 232 (2014), 1166–1182.
|
[64]
|
E. Valian, S. Tavakoli and S. Mohanna, An intelligent global harmony search approach to continuous optimization problems, Appl. Math. Comput., 232 (2014), 670–684.
|
[65]
|
A. M. Turky and S. Abdullah, A multi-population harmony search algorithm with external archive for dynamic optimization problems, Inform. Sci., 272 (2014), 84–95.
|
[66]
|
M. A. Al-Betar, M. A. Awadallah, A. T. Khader, et al., Island-based harmony search for optimization problems, Expert. Syst. Appl., 42 (2015), 2026–2035.
|
[67]
|
J. Yi, L. Gao, X. Li, et al., An efficient modified harmony search algorithm with intersect mutation operator and cellular local search for continuous function optimization problems, Appl. Intell., 44 (2016), 725–753.
|
[68]
|
B. Keshtegar and M. O. Sadeq, Gaussian global-best harmony search algorithm for optimization problems, Soft Comput., 21 (2017), 7337–7349.
|
[69]
|
H. B. Ouyang, L. Q. Gao, S. Li, et al., Improved harmony search algorithm: Lhs, Appl. Soft. Comput., 53 (2017), 133–167.
|
[70]
|
E. A. Portilla-Flores, Á . Sańchez-Maŕquez, L. Flores-Pulido, et al., Enhancing the harmony search algorithm performance on constrained numerical optimization, IEEE Access, 5 (2017), 25759–25780.
|
[71]
|
S. Tuo, L. Yong and T. Zhou, An improved harmony search based on teaching-learning strategy for unconstrained optimization problems, Math. Probl. Eng., 2013 (2013), 1–21.
|
[72]
|
G. Wang, L. Guo, H. Duan, et al., Hybridizing harmony search with biogeography based optimization for global numerical optimization, J. Comput. Theor. Nanosci., 10 (2013), 2312–2322.
|
[73]
|
G. G.Wang, A. H. Gandomi, X. Zhao, et al., Hybridizing harmony search algorithm with cuckoo search for global numerical optimization, Soft Comput., 20 (2016), 273–285.
|
[74]
|
X. Yuan, J. Zhao, Y. Yang, et al., Hybrid parallel chaos optimization algorithm with harmony search algorithm, Appl. Soft. Comput., 17 (2014), 12–22.
|
[75]
|
F. Zhao, Y. Liu, C. Zhang, et al., A self-adaptive harmony pso search algorithm and its performance analysis, Expert. Syst. Appl., 42 (2015), 7436–7455.
|
[76]
|
A. Fouad, D. Boukhetala, F. Boudjema, et al., A novel global harmony search method based on ant colony optimisation algorithm, J. Exp. Theor. Artif. Intell., 28 (2016), 215–238.
|
[77]
|
G. Zhang and Y. Li, A memetic algorithm for global optimization of multimodal nonseparable problems, IEEE T. Cy., 46 (2016), 1375–1387.
|
[78]
|
A. Assad and K. Deep, A hybrid harmony search and simulated annealing algorithm for continuous optimization, Inform. Sci., 450 (2018), 246–266.
|
[79]
|
A. Sadollah, H. Sayyaadi, D. G. Yoo, et al., Mine blast harmony search: A new hybrid optimization method for improving exploration and exploitation capabilities, Appl. Soft. Comput., 68 (2018), 548–564.
|
[80]
|
L. Wang, H. Hu, R. Liu, et al., An improved differential harmony search algorithm for function optimization problems, Soft Comput., (2018), 1–26.
|
[81]
|
B. L. Miller and D. E. Goldberg, Genetic algorithms, selection schemes, and the varying effects of noise, Evol. Comput., 4 (1996), 113–131.
|
[82]
|
Y. Shi, H. Liu, L. Gao, et al., Cellular particle swarm optimization, Inform. Sci., 181 (2011), 4460–4493.
|
[83]
|
C. Lu, L. Gao and J. Yi, Grey wolf optimizer with cellular topological structure, Expert. Syst. Appl., 107 (2018), 89–114.
|
[84]
|
A. Turky, S. Abdullah and A. Dawod, A dual-population multi operators harmony search algorithm for dynamic optimization problems, Comput. Ind. Eng., 117 (2018), 19–28.
|
[85]
|
G. Wang and L. Guo, A novel hybrid bat algorithm with harmony search for global numerical optimization, J. Appl. Math., 2013 (2013), 1–21.
|
[86]
|
K. Z. Gao, P. N. Suganthan, Q. K. Pan, et al., Pareto-based grouping discrete harmony search algorithm for multi-objective flexible job shop scheduling, Inform. Sci., 289 (2014), 76–90.
|
[87]
|
K. Z. Gao, P. N. Suganthan, Q. K. Pan, et al., An effective discrete harmony search algorithm for flexible job shop scheduling problem with fuzzy processing time, Int. J. Prod. Res., 53 (2015), 5896–5911.
|
[88]
|
K. Z. Gao, P. N. Suganthan, Q. K. Pan, et al., Discrete harmony search algorithm for flexible job shop scheduling problem with multiple objectives, J. Intell. Manuf., 27 (2016), 363–374.
|
[89]
|
K. Gao, L.Wang, J. Luo, et al., Discrete harmony search algorithm for scheduling and rescheduling the reprocessing problems in remanufacturing: a case study, Eng. Optimiz., 50 (2018), 965– 981.
|
[90]
|
L. Liu and H. Zhou, Hybridization of harmony search with variable neighborhood search for restrictive single-machine earliness/tardiness problem, Inform. Sci., 226 (2013), 68–92.
|
[91]
|
Y. Yuan, H. Xu and J. Yang, A hybrid harmony search algorithm for the flexible job shop scheduling problem, Appl. Soft. Comput., 13 (2013), 3259–3272.
|
[92]
|
F. Zammori, M. Braglia and D. Castellano, Harmony search algorithm for single-machine scheduling problem with planned maintenance, Comput. Ind. Eng., 76 (2014), 333–346.
|
[93]
|
Y. Li, X. Li and J. N. Gupta, Solving the multi-objective flowline manufacturing cell scheduling problem by hybrid harmony search, Expert. Syst. Appl., 42 (2015), 1409–1417.
|
[94]
|
C. Garcia-Santiago, J. Del Ser, C. Upton, et al., A random-key encoded harmony search approach for energy-efficient production scheduling with shared resources, Eng. Optimiz., 47 (2015), 1481–1496.
|
[95]
|
A. Maroosi, R. C. Muniyandi, E. Sundararajan, et al., A parallel membrane inspired harmony search for optimization problems: A case study based on a flexible job shop scheduling problem, Appl. Soft. Comput., 49 (2016), 120–136.
|
[96]
|
Z. Guo, L. Shi, L. Chen, et al., A harmony search-based memetic optimization model for integrated production and transportation scheduling in mto manufacturing, Omega, 66 (2017), 327– 343.
|
[97]
|
F. Zhao, Y. Liu, Y. Zhang, et al., A hybrid harmony search algorithm with efficient job sequence scheme and variable neighborhood search for the permutation flow shop scheduling problems, Eng. Appl. Artif. Intell., 65 (2017), 178–199.
|
[98]
|
M. Gaham, B. Bouzouia and N. Achour, An effective operations permutation-based discrete harmony search approach for the flexible job shop scheduling problem with makespan criterion, Appl. Intell., 48 (2018), 1423–1441.
|
[99]
|
F. Zhao, S. Qin, G. Yang, et al., A differential-based harmony search algorithm with variable neighborhood search for job shop scheduling problem and its runtime analysis, IEEE Access, 6 (2018), 76313–76330.
|
[100]
|
S. M. Lee and S. Y. Han, Topology optimization scheme for dynamic stiffness problems using harmony search method, Int. J. Precis. Eng. Manuf., 17 (2016), 1187–1194.
|
[101]
|
S. M. Lee and S. Y. Han, Topology optimization based on the harmony search method, J. Mech. Sci. Technol., 31 (2017), 2875–2882.
|
[102]
|
J. Yi, X. Li, M. Xiao, et al., Construction of nested maximin designs based on successive local enumeration and modified novel global harmony search algorithm, Eng. Optimiz., 49 (2017), 161–180.
|
[103]
|
B. Keshtegar, P. Hao, Y. Wang, et al., Optimum design of aircraft panels based on adaptive dynamic harmony search, Thin-Walled Struct., 118 (2017), 37–45.
|
[104]
|
B. Keshtegar, P. Hao, Y. Wang, et al., An adaptive response surface method and gaussian globalbest harmony search algorithm for optimization of aircraft stiffened panels, Appl. Soft. Comput., 66 (2018), 196–207.
|
[105]
|
H. Ouyang, W. Wu, C. Zhang, et al., Improved harmony search with general iteration models for engineering design optimization problems, Soft Comput., 0 (2018), 1–36.
|
[106]
|
X. Li, K. Qin, B. Zeng, et al., Assembly sequence planning based on an improved harmony search algorithm, Int. J. Adv. Manuf. Tech., 84 (2016), 2367–2380.
|
[107]
|
X. Li, K. Qin, B. Zeng, et al., A dynamic parameter controlled harmony search algorithm for assembly sequence planning, Int. J. Adv. Manuf. Tech., 92 (2017), 3399–3411.
|
[108]
|
G. Li, B. Zeng, W. Liao, et al., A new agv scheduling algorithm based on harmony search for material transfer in a real-world manufacturing system, Adv. Mech. Eng., 10 (2018), 1–13.
|
[109]
|
G. Li, X. Li, L. Gao, et al., Tasks assigning and sequencing of multiple agvs based on an improved harmony search algorithm, J. Ambient Intell. Humaniz., 0 (2018), 1–14.
|
[110]
|
M. B. B. Mahaleh and S. A. Mirroshandel, Harmony search path detection for vision based automated guided vehicle, Robot. Auton. Syst., 107 (2018), 156–166.
|
[111]
|
M. Ayyıldız and K. C¸ etinkaya, Comparison of four different heuristic optimization algorithms for the inverse kinematics solution of a real 4-dof serial robot manipulator, Neural Comput. Appl., 27 (2016), 825–836.
|
[112]
|
O. Zarei, M. Fesanghary, B. Farshi, et al., Optimization of multi-pass face-milling via harmony search algorithm, J. Mater. Process. Technol., 209 (2009), 2386–2392.
|
[113]
|
K. Abhishek, S. Datta and S. S. Mahapatra, Multi-objective optimization in drilling of cfrp (polyester) Measurement, 77 (2016), 222–239.
|
[114]
|
S. Kumari, A. Kumar, R. K. Yadav, et al., Optimisation of machining parameters using grey relation analysis integrated with harmony search for turning of aisi d2 steel, Materials Today: Proceedings, 5 (2018), 12750–12756.
|
[115]
|
J. Yi, C. H. Chu, C. L. Kuo, et al., Optimized tool path planning for five-axis flank milling of ruled surfaces using geometric decomposition strategy and multi-population harmony search algorithm, Appl. Soft. Comput., 73 (2018), 547–561.
|
[116]
|
S. Atta, P. R. S. Mahapatra and A. Mukhopadhyay, Solving tool indexing problem using harmony search algorithm with harmony refinement, Soft Comput., (2018), 1–17.
|
[117]
|
C. C. Lin, D. J. Deng, Z. Y. Chen, et al., Key design of driving industry 4.0: Joint energy-efficient deployment and scheduling in group-based industrial wireless sensor networks, IEEE Commun. Mag., 54 (2016), 46–52.
|
[118]
|
B. Zeng and Y. Dong, An improved harmony search based energy-efficient routing algorithm for wireless sensor networks, Appl. Soft. Comput., 41 (2016), 135–147.
|
[119]
|
L. Wang, L. An, H. Q. Ni, et al., Pareto-based multi-objective node placement of industrial wireless sensor networks using binary differential evolution harmony search, Adv. Manuf., 4 (2016), 66–78.
|
[120]
|
O. Moh'd Alia and A. Al-Ajouri, Maximizing wireless sensor network coverage with minimum cost using harmony search algorithm, IEEE Sens. J., 17 (2017), 882–896.
|
[121]
|
C. C. Lin, D. J. Deng, J. R. Kang, et al., Forecasting rare faults of critical components in led epitaxy plants using a hybrid grey forecasting and harmony search approach, IEEE Trans. Ind. Inform., 12 (2016), 2228–2235.
|
[122]
|
S. Kang and J. Chae, Harmony search for the layout design of an unequal area facility, Expert. Syst. Appl., 79 (2017), 269–281.
|
[123]
|
J. Lin, M. Liu, J. Hao, et al., Many-objective harmony search for integrated order planning in steelmaking-continuous casting-hot rolling production of multi-plants, Int. J. Prod. Res., 55 (2017), 4003–4020.
|
[124]
|
Y. H. Kim, Y. Yoon and Z. W. Geem, A comparison study of harmony search and genetic algorithm for the max-cut problem, Swarm Evol. Comput., 44 (2018), 130–135.
|
[125]
|
B. Naderi, R. Tavakkoli-Moghaddam, et al., Electromagnetism-like mechanism and simulated annealing algorithms for flowshop scheduling problems minimizing the total weighted tardiness and makespan, Knowledge-Based Syst., 23 (2010), 77–85.
|
[126]
|
M. R. Garey, D. S. Johnson and R. Sethi, The complexity of flowshop and jobshop scheduling, Math. Oper. Res., 1 (1976), 117–129.
|
[127]
|
P. J. Van Laarhoven, E. H. Aarts and J. K. Lenstra, Job shop scheduling by simulated annealing, Oper. Res., 40 (1992), 113–125.
|
[128]
|
M. Dell'Amico and M. Trubian, Applying tabu search to the job-shop scheduling problem, Ann. Oper. Res., 41 (1993), 231–252.
|
[129]
|
I. Kacem, S. Hammadi and P. Borne, Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems, IEEE T. Syst. Man. Cy. C., 32 (2002), 1–13.
|
[130]
|
W. Xia and Z. Wu, An effective hybrid optimization approach for multi-objective flexible jobshop scheduling problems, Comput. Ind. Eng., 48 (2005), 409–425.
|
[131]
|
G. Zhang, X. Shao, P. Li, et al., An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem, Comput. Ind. Eng., 56 (2009), 1309–1318.
|
[132]
|
G. Zhang, L. Gao and Y. Shi, An effective genetic algorithm for the flexible job-shop scheduling problem, Expert. Syst. Appl., 38 (2011), 3563–3573.
|
[133]
|
Q. Lin, L. Gao, X. Li, et al., A hybrid backtracking search algorithm for permutation flow-shop scheduling problem, Comput. Ind. Eng., 85 (2015), 437–446.
|
[134]
|
C. Lu, L. Gao, X. Li, et al., Energy-efficient permutation flow shop scheduling problem using a hybrid multi-objective backtracking search algorithm, J. Cleaner. Prod., 144 (2017), 228–238.
|
[135]
|
C. Viergutz and S. Knust, Integrated production and distribution scheduling with lifespan constraints, Ann. Oper. Res., 213 (2014), 293–318.
|
[136]
|
R. T. Lund, Remanufacturing, Technol. Rev., 87 (1984), 18.
|
[137]
|
Y. Liu, H. Dong, N. Lohse, et al., An investigation into minimising total energy consumption and total weighted tardiness in job shops, J. Cleaner. Prod., 65 (2014), 87–96.
|
[138]
|
M. Mashayekhi, E. Salajegheh and M. Dehghani, Topology optimization of double and triple layer grid structures using a modified gravitational harmony search algorithm with efficient member grouping strategy, Comput. Struct., 172 (2016), 40–58.
|
[139]
|
M. F. F. Rashid, W. Hutabarat and A. Tiwari, A review on assembly sequence planning and assembly line balancing optimisation using soft computing approaches, Int. J. Adv. Manuf. Tech., 59 (2012), 335–349.
|
[140]
|
D. Ghosh, A new genetic algorithm for the tool indexing problem, Technical report, Indian Institute of Management Ahmedabad, 2016.
|
[141]
|
D. Ghosh, Exploring Lin Kernighan neighborhoods for the indexing problem, Technical report, Indian Institute of Management Ahmedabad, 2016.
|
[142]
|
M. Hermann, T. Pentek and B. Otto, Design principles for industrie 4.0 scenarios, in System Sciences (HICSS), 2016 49th Hawaii International Conference on, IEEE, (2016), 3928–3937.
|
[143]
|
S. Das, A. Mukhopadhyay, A. Roy, et al., Exploratory power of the harmony search algorithm: analysis and improvements for global numerical optimization, IEEE T. Syst. Man. Cy. B., 41 (2011), 89–106.
|
[144]
|
L. Q. Gao, S. Li, X. Kong, et al., On the iterative convergence of harmony search algorithm and a proposed modification, Appl. Math. Comput., 247 (2014), 1064–1095.
|
[145]
|
T. G. Dietterich, Ensemble methods in machine learning, in International workshop on multiple classifier systems, Springer, 2000, 1–15.
|
[146]
|
S. Mahdavi, M. E. Shiri and S. Rahnamayan, Metaheuristics in large-scale global continues optimization: A survey, Inform. Sci., 295 (2015), 407–428.
|
[147]
|
G. Karafotias, M. Hoogendoorn and Á . E. Eiben, Parameter control in evolutionary algorithms: Trends and challenges, IEEE Trans. Evol. Comput., 19 (2015), 167–187.
|