Abstract
Resource allocation is the optimal distribution in a limited number of resources available for certain activities. The allocation of the resources for a large number of activities requires exponentially multiplying a computation cost. Therefore, the resource allocation problem is known as NP-Hard problem in the literature. In this study, a multi-objective binary artificial bee colony algorithm has been proposed for solving the multi-objective resource allocation problems. The proposed algorithm has benefited from the robust structure and easy implementation properties of the artificial bee colony algorithm. The contribution is to introduce the multi-objective version of the artificial bee colony algorithm with advanced local search and binary format using transfer functions. The multi-objective binary artificial bee colony algorithm has been improved as two versions using sigmoid and hyperbolic tangent transfer functions to be able to search in the binary search space. With the proposed algorithms, the multi-objective resource allocation problems in the literature are solved, and the algorithms are compared with other algorithms that develop for the same problems. The results obtained show that the proposed algorithms give effective results on the problem. Especially, in large-scale problems, higher accuracy values are reached with a smaller number of evaluations.
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References
Liang, Y.-C.; Chuang, C.-Y.: Variable neighborhood search for multi-objective resource allocation problems. Robot. Comput.-Integr. Manuf. 29(3), 73–78 (2013). https://doi.org/10.1016/j.rcim.2012.04.015
Karaboga, D. : An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department, 200, 1–10 (2005)
Yilmaz, Z.: Çok Amaçlı Kaynak Tahsis Problemine İkili Yapay Arı Koloni Algoritması Yaklaşımı. Master Thesis, Selçuk University (2017)
Yılmaz Acar, Z.; Aydemir, F.; Başçiftçi, F.: A New Multi-Objective Artificial Bee Colony Algorithm for Multi-Objective Optimization Problems. Selçuk-Teknik Dergisi, 144–152 (2019)
Zou, W.-Q.; Pan, Q.-K.; Meng, T.; Gao, L.; Wang, Y.-L.: An effective discrete artificial bee colony algorithm for multi-AGVs dispatching problem in a matrix manufacturing workshop. Exp. Syst. Appl. (2020). https://doi.org/10.1016/j.eswa.2020.113675
Osman, M.S.; Abo-Sinna, M.A.; Mousa, A.A.: An effective genetic algorithm approach to multi-objective resource allocation problems (MORAPs). Appl. Math. Comput. 163(2), 755–768 (2005). https://doi.org/10.1016/j.amc.2003.10.057
Lin, C.-M.; Gen, M.: Multiobjective resource allocation problem by multistage decision-based hybrid genetic algorithm. Appl. Math. Comput. 187(2), 574–583 (2007). https://doi.org/10.1016/j.amc.2006.08.170
Lin, C.-M.; Gen, M.: Multi-criteria human resource allocation for solving multistage combinatorial optimization problems using multiobjective hybrid genetic algorithm. Exp. Syst. Appl. 34(4), 2480–2490 (2008). https://doi.org/10.1016/j.eswa.2007.04.016
Chaharsooghi, S.K.; Meimand Kermani, A.H.: An effective ant colony optimization algorithm (ACO) for multi-objective resource allocation problem (MORAP). Appl. Math. Comput. 200(1), 167–177 (2008). https://doi.org/10.1016/j.amc.2007.09.070
Fan, K.; You, W.; Li, Y.: An effective modified binary particle swarm optimization (mBPSO) algorithm for multi-objective resource allocation problem (MORAP). Appl. Math. Comput. 221, 257–267 (2013). https://doi.org/10.1016/j.amc.2013.06.039
Hussein, M.L.; Abo-Sinna, M.A.: A fuzzy dynamic approach to the multicriterion resource allocation problem. Fuzzy Sets Syst. 69(2), 115–124 (1995). https://doi.org/10.1016/0165-0114(94)00231-U
Li, J.-Q.; Han, Y.-Q.: A hybrid multi-objective artificial bee colony algorithm for flexible task scheduling problems in cloud computing system. Clust. Comput. 23(4), 2483–2499 (2020). https://doi.org/10.1007/s10586-019-03022-z
Li, Y.; Huang, W.; Wu, R.; Guo, K.: An improved artificial bee colony algorithm for solving multi-objective low-carbon flexible job shop scheduling problem. Appl. Soft Comput. (2020). https://doi.org/10.1016/j.asoc.2020.106544
Xie, J.; Gao, L.; Pan, Q.-K.; Tasgetiren, M.F.: An effective multi-objective artificial bee colony algorithm for energy efficient distributed job shop scheduling. Procedia Manuf. 39, 1194–1203 (2019). https://doi.org/10.1016/j.promfg.2020.01.350
Gong, D.; Han, Y.; Sun, J.: A novel hybrid multi-objective artificial bee colony algorithm for blocking lot-streaming flow shop scheduling problems. Knowl.-Based Syst. 148, 115–130 (2018). https://doi.org/10.1016/j.knosys.2018.02.029
Panda, M., Dehuri, S., Jagadev, A.K.: Multi-Objective Artificial Bee Colony Algorithms and Chaotic-TOPSIS Method for Solving Flowshop Scheduling Problem and Decision Making. Informatica 44(2) (2020)
Zhou, J.; Liao, X.; Ouyang, S.; Zhang, R.; Zhang, Y.: Multi-objective artificial bee colony algorithm for short-term scheduling of hydrothermal system. Int J Elec Power 55, 542–553 (2014). https://doi.org/10.1016/j.ijepes.2013.10.013
Alrezaamiri, H., Ebrahimnejad, A., Motameni, H.: Parallel multi-objective artificial bee colony algorithm for software requirement optimization. Requirements Engineering, 1-18 (2020)
Saad, A.; Khan, S.A.; Mahmood, A.: A multi-objective evolutionary artificial bee colony algorithm for optimizing network topology design. Swarm Evol Comput 38, 187–201 (2018). https://doi.org/10.1016/j.swevo.2017.07.010
Zhou, J.; Yao, X.; Lin, Y.; Chan, F.T.S.; Li, Y.: An adaptive multi-population differential artificial bee colony algorithm for many-objective service composition in cloud manufacturing. Inf. Sci. 456, 50–82 (2018). https://doi.org/10.1016/j.ins.2018.05.009
Ma, K.; Liu, X.; Li, G.; Hu, S.; Yang, J.; Guan, X.: Resource allocation for smart grid communication based on a multi-swarm artificial bee colony algorithm with cooperative learning. Eng. Appl. Artif. Intell. 81, 29–36 (2019). https://doi.org/10.1016/j.engappai.2018.12.002
Xu, X.; Hao, J.; Zheng, Y.: Multi-objective artificial bee colony algorithm for multi-stage resource leveling problem in sharing logistics network. Comput. Ind. Eng. (2020). https://doi.org/10.1016/j.cie.2020.106338
Baradaran, V.; Shafaei, A.; Hosseinian, A.H.: Stochastic vehicle routing problem with heterogeneous vehicles and multiple prioritized time windows: mathematical modeling and solution approach. Comput. Ind. Eng. 131, 187–199 (2019). https://doi.org/10.1016/j.cie.2019.03.047
Yong, Z.; Chun-lin, H.; Xian-fang, S.; Xiao-yan, S.: A multi-strategy integrated multi-objective artificial bee colony for unsupervised band selection of hyperspectral images. Swarm Evol. Comput. (2021). https://doi.org/10.1016/j.swevo.2020.100806
Wang, X.-H.; Zhang, Y.; Sun, X.-Y.; Wang, Y.-L.; Du, C.-H.: Multi-objective feature selection based on artificial bee colony: an acceleration approach with variable sample size. Appl. Soft Comput. (2020). https://doi.org/10.1016/j.asoc.2019.106041
Zhang, Y.; Cheng, S.; Shi, Y.; Gong, D.-W.; Zhao, X.: Cost-sensitive feature selection using two-archive multi-objective artificial bee colony algorithm. Exp. Syst. Appl. 137, 46–58 (2019). https://doi.org/10.1016/j.eswa.2019.06.044
Hancer, E.; Xue, B.; Zhang, M.; Karaboga, D.; Akay, B.: Pareto front feature selection based on artificial bee colony optimization. Inf. Sci. 422, 462–479 (2018). https://doi.org/10.1016/j.ins.2017.09.028
Kashan, M.H.; Nahavandi, N.; Kashan, A.H.: DisABC: A new artificial bee colony algorithm for binary optimization. Appl. Soft Comput. 12(1), 342–352 (2012)
Kiran, M.S.; Gündüz, M.: XOR-based artificial bee colony algorithm for binary optimization. Turkish J. Electr Eng Comput. Sci. 21(2), 2307–2328 (2013)
Jia, D.; Duan, X.; Khan, M.K.: Binary artificial bee colony optimization using bitwise operation. Comput. Ind. Eng. 76, 360–365 (2014). https://doi.org/10.1016/j.cie.2014.08.016
He, Y.; Xie, H.; Wong, T.-L.; Wang, X.: A novel binary artificial bee colony algorithm for the set-union knapsack problem. Future Gen. Comput. Syst. 78, 77–86 (2018). https://doi.org/10.1016/j.future.2017.05.044
Santana, C.J.; Macedo, M.; Siqueira, H.; Gokhale, A.; Bastos-Filho, C.J.A.: A novel binary artificial bee colony algorithm. Future Gen. Comput. Syst. 98, 180–196 (2019). https://doi.org/10.1016/j.future.2019.03.032
Karaboga, N.; Latifoglu, F.: Adaptive filtering noisy transcranial Doppler signal by using artificial bee colony algorithm. Eng. Appl. Artifi. Intell. 26(2), 677–684 (2013). https://doi.org/10.1016/j.engappai.2012.10.011
Karaboga, D.: Yapay Zeka Optimizasyon Algoritmaları, Third ed. Nobel Akademik Publishing, (2014)
Zou, W.P.; Zhu, Y.L.; Chen, H.N.; Zhang, B.W.: Solving multiobjective optimization problems using artificial bee colony algorithm. Discrete Dyn. Nat. Soc. (2011). https://doi.org/10.1155/2011/569784
Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. Ieee Sys Man Cybern, 4104-4108 (1997).
Rodrigues, D., Pereira, L.A.M., Almeida, T.N.S., Papa, J.P., Souza, A.N., Ramos, C.C.O., Yang, X.S.: BCS: A Binary Cuckoo Search Algorithm for Feature Selection. Ieee Int Symp Circ S, 465-468 (2013).
Emary, E.; Zawbaa, H.M.; Hassanien, A.E.: Binary grey wolf optimization approaches for feature selection. Neurocomputing 172, 371–381 (2016). https://doi.org/10.1016/j.neucom.2015.06.083
Deng, C., Zhao, B., Yang, Y., Deng, A.: Novel Binary Differential Evolution Algorithm for Discrete Optimization. In: 2009 Fifth International Conference on Natural Computation, 14-16 Aug. 2009 2009, pp. 346-349
Rashedi, E.; Nezamabadi-pour, H.; Saryazdi, S.: BGSA: binary gravitational search algorithm. Nat. Comput. 9(3), 727–745 (2010). https://doi.org/10.1007/s11047-009-9175-3
Mirjalili, S.; Lewis, A.: S-shaped versus V-shaped transfer functions for binary particle swarm optimization. Swarm Evol. Comput. 9, 1–14 (2013)
Mirjalili, S.; Hashim, S.Z.M.: BMOA: binary magnetic optimization algorithm. Int. J. Mach. Learn. Comput. 2(3), 204–208 (2012)
Chandrasekaran, K., Simon, S.P.: Binary/real coded particle swarm optimization for unit commitment problem. In: 2012 International Conference on Power, Signals, Controls and Computation, 3-6 Jan. 2012 2012, pp. 1-6
Deb, K.; Pratap, A.; Agarwal, S.; Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002). https://doi.org/10.1109/4235.996017
Sag, T.; Cunkas, M.: A new ABC-based multiobjective optimization algorithm with an improvement approach (IBMO: improved bee colony algorithm for multiobjective optimization). Turk J. Electr. Eng. Co 24(4), 2349–2373 (2016)
Czyzżak, P.; Jaszkiewicz, A.: Pareto simulated annealing—a metaheuristic technique for multiple-objective combinatorial optimization. J. Multi-Crit. Decis. Anal. 7(1), 34–47 (1998)
Knowles, J., Corne, D.: On metrics for comparing nondominated sets. In: Evolutionary Computation, 2002. CEC '02. Proceedings of the 2002 Congress on, 12-17 May 2002 2002, pp. 711-716
Zitzler, E.; Thiele, L.; Laumanns, M.; Fonseca, C.M.; Fonseca, V.G.d. : Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans. Evol. Comput. 7(2), 117–132 (2003). https://doi.org/10.1109/TEVC.2003.810758
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Yilmaz Acar, Z., Başçiftçi, F. Solving Multi-Objective Resource Allocation Problem Using Multi-Objective Binary Artificial Bee Colony Algorithm. Arab J Sci Eng 46, 8535–8547 (2021). https://doi.org/10.1007/s13369-021-05521-x
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DOI: https://doi.org/10.1007/s13369-021-05521-x