Advancing large-scale cement vehicle distribution: the modified salp swarm algorithm

References

• Abed-Alguni, B. H., Paul, D., & Hammad, R. (2022). Improved salp swarm algorithm for solving single-objective continuous optimization problems. Applied Intelligence, 52(15), 17217–17236. https://doi.org/10.1007/s10489-022-03269-x
   Web of Science ®Google Scholar
• Abualigah, L., et al. (2020). Salp swarm algorithm: A comprehensive survey. Neural Computing and Applications, 32(15), 11195–11215. https://doi.org/10.1007/s00521-019-04629-4
   Web of Science ®Google Scholar
• Ahmadianfar, I., et al. (2021). RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method. Expert Systems with Applications, 181, 115079. https://doi.org/10.1016/j.eswa.2021.115079
   Web of Science ®Google Scholar
• Ahmadianfar, I., et al. (2022). INFO: An efficient optimization algorithm based on weighted mean of vectors. Expert Systems with Applications, 195, 116516. https://doi.org/10.1016/j.eswa.2022.116516
   Web of Science ®Google Scholar
• Ai, T. J., & Kachitvichyanukul, V. (2009). A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery. Computers & Operations Research, 36(5), 1693–1702. https://doi.org/10.1016/j.cor.2008.04.003
   Web of Science ®Google Scholar
• Altay, O., Cetindemir, O., & Aydogdu, I. (2023). Size optimization of planar truss systems using the modified salp swarm algorithm. Engineering Optimization, 1–17. https://doi.org/10.1080/0305215X.2022.2160449
   Google Scholar
• Archetti, C., et al. (2011). Complexity of the VRP and SDVRP. Transportation Research Part C: Emerging Technologies, 19(5), 741–750. https://doi.org/10.1016/j.trc.2009.12.006
   Web of Science ®Google Scholar
• Asghari, K., et al. (2021). Multi-swarm and chaotic whale-particle swarm optimization algorithm with a selection method based on roulette wheel. Expert Systems, 38(8), e12779. https://doi.org/10.1111/exsy.12779
   Web of Science ®Google Scholar
• Barhoush, M., Abed-alguni, B. H., & Al-qudah, N. E. A. (2023). Improved discrete salp swarm algorithm using exploration and exploitation techniques for feature selection in intrusion detection systems. The Journal of Supercomputing, 79, 1–45. https://doi.org/10.1007/s11227-023-05444-4
   Google Scholar
• Baygi, S. M. H., & Karsaz, A. (2018). A hybrid optimal PID-LQR control of structural system: A case study of salp swarm optimization. In 2018 3rd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) (pp. 1–6). IEEE. https://doi.org/10.1109/CSIEC.2018.8405416
   Google Scholar
• Bell, J. E., & McMullen, P. R. (2004). Ant colony optimization techniques for the vehicle routing problem. Advanced Engineering Informatics, 18(1), 41–48. https://doi.org/10.1016/j.aei.2004.07.001
   Web of Science ®Google Scholar
• Castelli, M., et al. (2022). Salp swarm optimization: A critical review. Expert Systems with Applications, 189, 116029. https://doi.org/10.1016/j.eswa.2021.116029
   Web of Science ®Google Scholar
• Cheng, Y.-S., et al. (2016). A particle swarm optimization based power dispatch algorithm with roulette wheel re-distribution mechanism for equality constraint. Renewable Energy, 88, 58–72. https://doi.org/10.1016/j.renene.2015.11.023
   Web of Science ®Google Scholar
• Cordeau, J.-F., et al. (2007). Vehicle routing. Handbooks in Operations Research and Management Science, 14, 367–428. https://doi.org/10.1016/S0927-0507(06)14006-2
   Google Scholar
• da Costa, P. R. d. O., et al. (2018). A genetic algorithm for a green vehicle routing problem. Electronic Notes in Discrete Mathematics, 64, 65–74. https://doi.org/10.1016/j.endm.2018.01.008
   Google Scholar
• Demirtaş, Y. E., Özdemir, E., & Demirtaş, U. (2015). A particle swarm optimization for the dynamic vehicle routing problem. In 2015 6th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO) (pp. 1–5). IEEE. https://doi.org/10.1109/ICMSAO.2015.7152224
   Google Scholar
• Elhassania, M., Jaouad, B., & Ahmed, E. A. (2014). Solving the dynamic vehicle routing problem using genetic algorithms. In 2014 international conference on logistics operations management (pp. 62–69). IEEE. https://doi.org/10.1109/GOL.2014.6887419
   Google Scholar
• Ewees, A. A., Elaziz, M. A., & Houssein, E. H. (2018). Improved grasshopper optimization algorithm using opposition-based learning. Expert Systems with Applications, 112, 156–172. https://doi.org/10.1016/j.eswa.2018.06.023
   Web of Science ®Google Scholar
• Faris, H., et al. (2018). An efficient binary salp swarm algorithm with crossover scheme for feature selection problems. Knowledge-Based Systems, 154, 43–67. https://doi.org/10.1016/j.knosys.2018.05.009
   Web of Science ®Google Scholar
• Friedman, M. (1940). A comparison of alternative tests of significance for the problem of m rankings. The Annals of Mathematical Statistics, 11(1), 86–92. https://doi.org/10.1214/aoms/1177731944
   Google Scholar
• Gong, Y.-J., et al. (2011). Optimizing the vehicle routing problem with time windows: A discrete particle swarm optimization approach. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(2), 254–267. https://doi.org/10.1109/TSMCC.2011.2148712
   Web of Science ®Google Scholar
• Gupta, S., & Deep, K. (2019). A hybrid self-adaptive sine cosine algorithm with opposition based learning. Expert Systems with Applications, 119, 210–230. https://doi.org/10.1016/j.eswa.2018.10.050
   Web of Science ®Google Scholar
• Heidari, A. A., et al. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849–872. https://doi.org/10.1016/j.future.2019.02.028
   Web of Science ®Google Scholar
• Ho, W., et al. (2008). A hybrid genetic algorithm for the multi-depot vehicle routing problem. Engineering Applications of Artificial Intelligence, 21(4), 548–557. https://doi.org/10.1016/j.engappai.2007.06.001
   Web of Science ®Google Scholar
• Ho-Huu, V., et al. (2018). An improved differential evolution based on roulette wheel selection for shape and size optimization of truss structures with frequency constraints. Neural Computing and Applications, 29(1), 167–185. https://doi.org/10.1007/s00521-016-2426-1
   Web of Science ®Google Scholar
• Huang, S.-H., et al. (2022). Solving the vehicle routing problem with drone for delivery services using an ant colony optimization algorithm. Advanced Engineering Informatics, 51, 101536. https://doi.org/10.1016/j.aei.2022.101536
   Web of Science ®Google Scholar
• Karakatič, S. (2021). Optimizing nonlinear charging times of electric vehicle routing with genetic algorithm. Expert Systems with Applications, 164, 114039. https://doi.org/10.1016/j.eswa.2020.114039
   Web of Science ®Google Scholar
• Khamees, M., Albakry, A., & Shaker, K. (2018). Multi-objective feature selection: Hybrid of salp swarm and simulated annealing approach. In Al-mamory S., Alwan J., & Hussein A. (Eds.), International conference on new trends in information and communications technology applications (pp. 129–142). Springer.
   Google Scholar
• Li, S., et al. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems, 111, 300–323. https://doi.org/10.1016/j.future.2020.03.055
   Web of Science ®Google Scholar
• Li, Y., Soleimani, H., & Zohal, M. (2019). An improved ant colony optimization algorithm for the multi-depot green vehicle routing problem with multiple objectives. Journal of Cleaner Production, 227, 1161–1172. https://doi.org/10.1016/j.jclepro.2019.03.185
   Web of Science ®Google Scholar
• Luus, R. (1975). Optimization of system reliability by a new nonlinear integer programming procedure. IEEE Transactions on Reliability, 24(1), 14–16. https://doi.org/10.1109/TR.1975.5215316
   Web of Science ®Google Scholar
• Madhumathi, R., & Radhakrishnan, R. (2015). A resource allocation strategy in cloud using roulette wheel selection method. In 2015 international Conference on Green Computing and Internet of Things (ICGCIoT) (pp. 341–345). IEEE. https://doi.org/10.1109/ICGCIoT.2015.7380485
   Google Scholar
• Mahdavi, S., Rahnamayan, S., & Deb, K. (2018). Opposition based learning: A literature review. Swarm and Evolutionary Computation, 39, 1–23. https://doi.org/10.1016/j.swevo.2017.09.010
   Web of Science ®Google Scholar
• Marinakis, Y., Marinaki, M., & Migdalas, A. (2019). A multi-adaptive particle swarm optimization for the vehicle routing problem with time windows. Information Sciences, 481, 311–329. https://doi.org/10.1016/j.ins.2018.12.086
   Web of Science ®Google Scholar
• Meraihi, Y., et al. (2019). A chaotic binary salp swarm algorithm for solving the graph coloring problem. In Chikhi S., Amine A., Chaoui A., & Saidouni D.E. (Eds.), Modelling and implementation of complex systems: Proceedings of the 5th international symposium, MISC 2018, December 16-18, 2018, Laghouat, Algeria 5 (pp. 106–118). Springer.
   Google Scholar
• Mirjalili, S., et al. (2017). Salp swarm algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163–191. https://doi.org/10.1016/j.advengsoft.2017.07.002
   Web of Science ®Google Scholar
• Moradi, B. (2020). The new optimization algorithm for the vehicle routing problem with time windows using multi-objective discrete learnable evolution model. Soft Computing, 24(9), 6741–6769. https://doi.org/10.1007/s00500-019-04312-9
   Web of Science ®Google Scholar
• Muñoz, D. M., et al. (2014). Hardware opposition-based PSO applied to mobile robot controllers. Engineering Applications of Artificial Intelligence, 28, 64–77. https://doi.org/10.1016/j.engappai.2013.12.003
   Web of Science ®Google Scholar
• Narasimha, K. V., et al. (2013). An ant colony optimization technique for solving min–max multi-depot vehicle routing problem. Swarm and Evolutionary Computation, 13, 63–73. https://doi.org/10.1016/j.swevo.2013.05.005
   Web of Science ®Google Scholar
• Neggaz, N., et al. (2020). Boosting salp swarm algorithm by sine cosine algorithm and disrupt operator for feature selection. Expert Systems with Applications, 145, 113103. https://doi.org/10.1016/j.eswa.2019.113103
   Web of Science ®Google Scholar
• Niu, Y., et al. (2022). Multi-objective evolutionary algorithm based on RBF network for solving the stochastic vehicle routing problem. Information Sciences, 609, 387–410. https://doi.org/10.1016/j.ins.2022.07.087
   Web of Science ®Google Scholar
• Norouzi, N., Sadegh-Amalnick, M., & Tavakkoli-Moghaddam, R. (2017). Modified particle swarm optimization in a time-dependent vehicle routing problem: Minimizing fuel consumption. Optimization Letters, 11(1), 121–134. https://doi.org/10.1007/s11590-015-0996-y
   Web of Science ®Google Scholar
• Pham, V. H. S., & Nguyen, V. N. (2023). Cement transport vehicle routing with a hybrid sine cosine optimization algorithm. Advances in Civil Engineering, 2023, 2728039. https://doi.org/10.1155/2023/2728039
   Web of Science ®Google Scholar
• Pham, V. H. S., & Nguyen, V. N. (2024). Cement transport vehicle routing problem with hybrid sine cosine optimization algorithm in construction management. Springer Nature Singapore.
   Google Scholar
• Pham, V. H. S., Nguyen Dang, N. T., & Nguyen, V. N. (2023). Hybrid sine cosine algorithm with integrated roulette wheel selection and opposition-based learning for engineering optimization problems. International Journal of Computational Intelligence Systems, 16(1), 171. https://doi.org/10.1007/s44196-023-00350-2
   Web of Science ®Google Scholar
• Pierre, D. M., & Zakaria, N. (2017). Stochastic partially optimized cyclic shift crossover for multi-objective genetic algorithms for the vehicle routing problem with time-windows. Applied Soft Computing, 52, 863–876. https://doi.org/10.1016/j.asoc.2016.09.039
   Web of Science ®Google Scholar
• Piri, J., & Mohapatra, P. (2021). An analytical study of modified multi-objective Harris Hawk Optimizer towards medical data feature selection. Computers in Biology and Medicine, 135, 104558. https://doi.org/10.1016/j.compbiomed.2021.104558
   PubMed Web of Science ®Google Scholar
• Qais, M. H., Hasanien, H. M., & Alghuwainem, S. (2019). Enhanced salp swarm algorithm: Application to variable speed wind generators. Engineering Applications of Artificial Intelligence, 80, 82–96. https://doi.org/10.1016/j.engappai.2019.01.011
   Web of Science ®Google Scholar
• Qi, C., & Hu, L. (2020). Optimization of vehicle routing problem for emergency cold chain logistics based on minimum loss. Physical Communication, 40, 101085. https://doi.org/10.1016/j.phycom.2020.101085
   Web of Science ®Google Scholar
• Razali, N. M. (2015). An efficient genetic algorithm for large scale vehicle routing problem subject to precedence constraints. Procedia - Social and Behavioral Sciences, 195, 1922–1931. https://doi.org/10.1016/j.sbspro.2015.06.203
   Google Scholar
• Reed, M., Yiannakou, A., & Evering, R. (2014). An ant colony algorithm for the multi-compartment vehicle routing problem. Applied Soft Computing, 15, 169–176. https://doi.org/10.1016/j.asoc.2013.10.017
   Web of Science ®Google Scholar
• Rizk-Allah, R. M., et al. (2019). A new binary salp swarm algorithm: Development and application for optimization tasks. Neural Computing and Applications, 31(5), 1641–1663. https://doi.org/10.1007/s00521-018-3613-z
   Web of Science ®Google Scholar
• Rosen, I. I., et al. (1991). Treatment plan optimization using linear programming. Medical Physics, 18(2), 141–152. https://doi.org/10.1118/1.596700
   PubMed Web of Science ®Google Scholar
• Sabar, N. R., et al. (2019). A self-adaptive evolutionary algorithm for dynamic vehicle routing problems with traffic congestion. Swarm and Evolutionary Computation, 44, 1018–1027. https://doi.org/10.1016/j.swevo.2018.10.015
   Web of Science ®Google Scholar
• Sabar, N. R., et al. (2020). An adaptive memetic approach for heterogeneous vehicle routing problems with two-dimensional loading constraints. Swarm and Evolutionary Computation, 58, 100730. https://doi.org/10.1016/j.swevo.2020.100730
   Web of Science ®Google Scholar
• Sahu, P. C., et al. (2018). Improved-salp swarm optimized type-II fuzzy controller in load frequency control of multi area islanded AC microgrid. Sustainable Energy, Grids and Networks, 16, 380–392. https://doi.org/10.1016/j.segan.2018.10.003
   Web of Science ®Google Scholar
• Sayed, G. I., Khoriba, G., & Haggag, M. H. (2018). A novel chaotic salp swarm algorithm for global optimization and feature selection. Applied Intelligence, 48(10), 3462–3481. https://doi.org/10.1007/s10489-018-1158-6
   Web of Science ®Google Scholar
• Sayed, G. I., Soliman, M. M., & Hassanien, A. E. (2021). A novel melanoma prediction model for imbalanced data using optimized SqueezeNet by bald eagle search optimization. Computers in Biology and Medicine, 136, 104712. https://doi.org/10.1016/j.compbiomed.2021.104712
   PubMed Web of Science ®Google Scholar
• Shan, Q., & Wang, J. (2013). Solve capacitated vehicle routing problem using hybrid chaotic particle swarm optimization. In 2013 Sixth International Symposium on Computational Intelligence and Design (pp. 422–427). IEEE. https://doi.org/10.1109/ISCID.2013.218
   Google Scholar
• Shi, Z., et al. (2017). Improved crow search algorithm with inertia weight factor and roulette wheel selection scheme. In 2017 10th international symposium on computational intelligence and design (ISCID) (pp. 205–209). IEEE. https://doi.org/10.1109/ISCID.2017.140
   Google Scholar
• Singh, N., et al. (2020). A new fusion of salp swarm with sine cosine for optimization of non-linear functions. Engineering with Computers, 36(1), 185–212. https://doi.org/10.1007/s00366-018-00696-8
   Web of Science ®Google Scholar
• Smucker, M. D., Allan, J., & Carterette, B. (2007). A comparison of statistical significance tests for information retrieval evaluation. In Proceedings of the sixteenth ACM conference on conference on information and knowledge management (pp. 623–632). https://doi.org/10.1145/1321440.1321528
   Google Scholar
• Son, P. V. H., & Dang, N. T. N. (2024). A modified sine cosine algorithm for time-cost trade-Off problem. Springer Nature Singapore.
   Google Scholar
• Srinivasan, K., et al. (1999). Mixed-integer programming model for reservoir performance optimization. Journal of Water Resources Planning and Management, 125(5), 298–301. https://doi.org/10.1061/(ASCE)0733-9496(1999)125:5(298)
   Web of Science ®Google Scholar
• Su, H., et al. (2023). RIME: A physics-based optimization. Neurocomputing, 532, 183–214. https://doi.org/10.1016/j.neucom.2023.02.010
   Web of Science ®Google Scholar
• Sultana, S., & Roy, P. K. (2016). Oppositional krill herd algorithm for optimal location of capacitor with reconfiguration in radial distribution system. International Journal of Electrical Power & Energy Systems, 74, 78–90. https://doi.org/10.1016/j.ijepes.2015.07.008
   Web of Science ®Google Scholar
• Sun, Z.-X., et al. (2018). Salp swarm algorithm based on blocks on critical path for reentrant job shop scheduling problems. In Huang DS., Bevilacqua V., Premaratne P., & Gupta P. (Eds.), Intelligent computing theories and application: 14th international conference, ICIC 2018, Wuhan, China, August 15-18, 2018, proceedings, part I 14 (pp. 638–648). Springer.
   Google Scholar
• Tan, K. C., et al. (2001). Heuristic methods for vehicle routing problem with time windows. Artificial Intelligence in Engineering, 15(3), 281–295. https://doi.org/10.1016/S0954-1810(01)00005-X
   Google Scholar
• Tayab, U. B., et al. (2021). Energy management system for microgrids using weighted salp swarm algorithm and hybrid forecasting approach. Renewable Energy, 180, 467–481. https://doi.org/10.1016/j.renene.2021.08.070
   Web of Science ®Google Scholar
• Tizhoosh, H. R. (2005). Opposition-based learning: A new scheme for machine intelligence. In International conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce (CIMCA-IAWTIC'06) (pp. 695–701). IEEE.
   Google Scholar
• Toth, P., & Vigo, D. (2002). The vehicle routing problem. SIAM.
   Google Scholar
• Tu, J., et al. (2021). The colony predation algorithm. Journal of Bionic Engineering, 18(3), 674–710. https://doi.org/10.1007/s42235-021-0050-y
   Web of Science ®Google Scholar
• Upadhyay, P., et al. (2014). A novel design method for optimal IIR system identification using opposition based harmony search algorithm. Journal of the Franklin Institute, 351(5), 2454–2488. https://doi.org/10.1016/j.jfranklin.2014.01.001
   Web of Science ®Google Scholar
• Wang, C.-H., & Lu, J.-Z. (2009). A hybrid genetic algorithm that optimizes capacitated vehicle routing problems. Expert Systems with Applications, 36(2), 2921–2936. https://doi.org/10.1016/j.eswa.2008.01.072
   Web of Science ®Google Scholar
• Wang, D., et al. (2018). A simplex method-based salp swarm algorithm for numerical and engineering optimization. In Shi Z., Mercier-Laurent E., & Li J. (Eds.), Intelligent information processing IX: 10th IFIP TC 12 international conference, IIP 2018, Nanning, China, October 19-22, 2018, proceedings 10 (pp. 150–159). Springer.
   Google Scholar
• Wang, G.-G. (2018). Moth search algorithm: A bio-inspired metaheuristic algorithm for global optimization problems. Memetic Computing, 10(2), 151–164. https://doi.org/10.1007/s12293-016-0212-3
   Web of Science ®Google Scholar
• Wang, G.-G., Deb, S., & Cui, Z. (2019). Monarch butterfly optimization. Neural Computing and Applications, 31(7), 1995–2014. https://doi.org/10.1007/s00521-015-1923-y
   Web of Science ®Google Scholar
• Wang, W., et al. (2006). Particle swarm optimization for open vehicle routing problem. In Huang DS., Li K., & Irwin G.W. (Eds.), Computational intelligence: International conference on intelligent computing, ICIC 2006 Kunming, China, August 16-19, 2006 proceedings, part II 2 (pp. 999–1007). Springer.
   Google Scholar
• Wang, Y., et al. (2019). Multi-depot green vehicle routing problem with shared transportation resource: Integration of time-dependent speed and piecewise penalty cost. Journal of Cleaner Production, 232, 12–29. https://doi.org/10.1016/j.jclepro.2019.05.344
   Web of Science ®Google Scholar
• Xing, J., et al. (2023). Boosting whale optimizer with quasi-oppositional learning and Gaussian barebone for feature selection and COVID-19 image segmentation. Journal of Bionic Engineering, 20(2), 797–818. https://doi.org/10.1007/s42235-022-00297-8
   PubMed Web of Science ®Google Scholar
• Yang, Y., et al. (2021). Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Systems with Applications, 177, 114864. https://doi.org/10.1016/j.eswa.2021.114864
   Web of Science ®Google Scholar
• Yu, B., Yang, Z.-Z., & Yao, B. (2009). An improved ant colony optimization for vehicle routing problem. European Journal of Operational Research, 196(1), 171–176. https://doi.org/10.1016/j.ejor.2008.02.028
   Web of Science ®Google Scholar
• Zhang, Z., Wei, L., & Lim, A. (2015). An evolutionary local search for the capacitated vehicle routing problem minimizing fuel consumption under three-dimensional loading constraints. Transportation Research Part B: Methodological, 82, 20–35. https://doi.org/10.1016/j.trb.2015.10.001
   Web of Science ®Google Scholar
• Zhengchu, W., et al. (2009). Research in capacitated vehicle routing problem based on modified hybrid particle swarm optimization. In 2009 IEEE international conference on intelligent computing and intelligent systems (pp. 289–293). https://doi.org/10.1109/ICICISYS.2009.5358182
   Google Scholar