Open Access
RAIRO-Oper. Res.
Volume 58, Number 1, January-February 2024
Page(s) 253 - 280
Published online 08 February 2024
  • M. Abdelghany, Z. Yahia and A.B. Eltawil, A new two-stage variable neighborhood search algorithm for the nurse rostering problem. RAIRO Oper. Res. 55 (2021) 2804–7303. [Google Scholar]
  • L. Abualigah and A. Diabat, Advances in sine cosine algorithm: a comprehensive survey. Artif. Intell. Rev. 54 (2021) 1–42. [Google Scholar]
  • L. Abualigah, A. Diabat, S. Mirjalili, M. Abd Elaziz and A.H. Gandomi, The arithmetic optimization algorithm. Comput. Methods Appl. Mech. Eng. 376 (2021) 113609. [Google Scholar]
  • N. Adil and H. Lakhbab, A new modified bat algorithm for global optimization. RAIRO: Oper. Res. 57 (2023) 2659–2685. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
  • M. Azizi, Atomic orbital search: a novel metaheuristic algorithm. Appl. Math. Model. 93 (2021) 657–683. [Google Scholar]
  • A. Baskar, New simple trigonometric algorithms for solving optimization problems. J. Appl. Sci. Eng. 25 (2022) 1105–1120. [Google Scholar]
  • A. Baskar, Sine (B): a single randomized population-based algorithm for solving optimization problems. Mater. Today: Proc. 62 (2022) 4745–4751. [Google Scholar]
  • A. Baskar and M. Anthony Xavior, A four-point direction search heuristic algorithm applied to facility location on plane, sphere, and ellipsoid surfaces. J. Oper. Res. Soc. 73 (2021) 2385–2394. [Google Scholar]
  • A. Baykasoğlu and F.B. Ozsoydan, Adaptive firefly algorithm with chaos for mechanical design optimization problems. Appl. Soft Comput. 36 (2015) 152–164. [Google Scholar]
  • H. Bayzidi, S. Talatahari, M. Saraee and C.P. Lamarche, Social network search for solving engineering optimization problems. Comput. Intell. Neurosci. 2021 (2021) 1–32. [Google Scholar]
  • M.A. Bozorgirad and R. Logendran, A comparison of local search algorithms with population-based algorithms in hybrid flow shop scheduling problems with realistic characteristics. Int. J. Adv. Manuf. Technol. 83 (2016) 1135–1151. [Google Scholar]
  • E.K. Burke, M. Gendreau, M. Hyde, G. Kendall, G. Ochoa, E. Özcan and R. Qu, Hyper-heuristics: a survey of the state of the art. J. Oper. Res. Soc. 64 (2013) 1695–1724. [Google Scholar]
  • Y. Cao, Hypervolume Indicator. MATLAB Central File Exchange. (2022). [Google Scholar]
  • A. Chakraborty, S. Mitra, D. Das, D. Battacharyya, D. De, S.P. Mondal and A.J. Pal, Active learning-based estimation of COVID-19 pandemic: a synergetic case study in selective regions population, in Healthcare Informatics for Fighting COVID-19 and Future Epidemics. EAI/Springer Innovations in Communication and Computing. Springer, Cham (2022) 31–65. [CrossRef] [Google Scholar]
  • S.K. Das and S.K. Roy, Effect of variable carbon emission in a multi-objective transportation-p-facility location problem under neutrosophic environment. Comput. Ind. Eng. 132 (2019) 311–324. [Google Scholar]
  • S.K. Das, S.K. Roy and G.W. Weber, The impact of carbon tax policy in a multi-objective green solid logistics modelling under sustainable development, in Computational Modelling in Industry 4.0: A Sustainable Resource Management Perspective. Springer Nature Singapore (2022) 49–66. [CrossRef] [Google Scholar]
  • S.K. Das, M. Pervin, S.K. Roy and G.W. Weber, Multi-objective solid transportation-location problem with variable carbon emission in inventory management: a hybrid approach. Ann. Oper. Res. 324 (2023) 283–309. [Google Scholar]
  • S.K. Das, F.Y. Vincent, S.K. Roy and G.W. Weber, Location–allocation problem for green efficient two-stage vehicle-based logistics system: a type-2 neutrosophic multi-objective modeling approach. Expert Syst. Appl. 238 (2024) 122174. [Google Scholar]
  • K. Deb, An efficient constraint handling method for genetic algorithms. Comput. Methods Appl. Mech. Eng. 186 (2000) 311–338. [Google Scholar]
  • K. Deb and M. Goyal, Optimizing engineering designs using a combined genetic search, in ICGA (1997) 521–528. [Google Scholar]
  • S. Droste, T. Jansen and I. Wegener, Upper and lower bounds for randomized search heuristics in black-box optimization. Theory Comput. Syst. 39 (2006) 525–544. [Google Scholar]
  • D. Fouskakis and D. Draper, Stochastic optimization: a review. Int Stat Rev. 70 (2002) 315–349. [Google Scholar]
  • A.H. Gandomi, X.S. Yang and A.H. Alavi, Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng. Comput. 29 (2013) 17–35. [CrossRef] [Google Scholar]
  • N. Ghorui, S.P. Mondal, B. Chatterjee, A. Ghosh, A. Pal, D. De and B.C. Giri, Selection of cloud service providers using MCDM methodology under intuitionistic fuzzy uncertainty. Soft Comput. 27 (2023) 2403–2423. [Google Scholar]
  • F.A. Hashim, K. Hussain, E.H. Houssein, M.S. Mabrouk and W. Al-Atabany, Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems. Appl. Intell. 51 (2021) 1531–1551. [Google Scholar]
  • J.H. Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press (1992). [Google Scholar]
  • C. Jariyatantiwait and G.G. Yen, Multiobjective differential evolution based on fuzzy performance feedback. Int. J. Swarm Intell. Res. 5 (2014) 45–64. [Google Scholar]
  • Kaew, Multiobjective differential evolution based on fuzzy performance feedback. MATLAB Central File Exchange. (2022). [Google Scholar]
  • J. Kennedy and R. Eberhart, Particle swarm optimization, in Proceedings of ICNN’95-International Conference on Neural Networks. Vol. 4 IEEE (1995) 1942–1948. [Google Scholar]
  • R.M. Lewis, V. Torczon and M.W. Trosset, Direct search methods: then and now. J. Comput. Appl. Math. 124 (2000) 191–207. [Google Scholar]
  • A.K. Manna, T. Benerjee, S.P. Mondal, A.A. Shaikh and A.K. Bhunia, Two-plant production model with customers’ demand dependent on warranty period of the product and carbon emission level of the manufacturer via different meta-heuristic algorithms. Neural. Comput. Appl. 33 (2021) 14263–14281. [Google Scholar]
  • A. Mehamdia, Y. Chaib and T. Bechouat, Two modified conjugate gradient methods for solving unconstrained optimization and application. RAIRO: Oper. Res. 57 (2023) 2804–7303. [Google Scholar]
  • S. Mirjalili, SCA: a sine cosine algorithm for solving optimization problems. Knowl. Based Syst. 96 (2016) 120–133. [Google Scholar]
  • S. Mirjalili, S.M. Mirjalili and A. Lewis, Grey wolf optimizer. Adv. Eng. Softw. 69 (2014) 46–61. [CrossRef] [Google Scholar]
  • P.K. Pal, K. Deep and A.K. Nagar, Performance of sine–cosine algorithm on large-scale optimization problems, in Decision Science in Action: Theory and Applications of Modern Decision Analytic Optimisation, Springer, Singapore (2019) 139–154. [CrossRef] [Google Scholar]
  • Y. Peng, S.H. Gao, D. Yu, Y.P. Xiao and Y.J. Luo, Multi-objective optimization for multimodal transportation routing problem with stochastic transportation time based on data-driven approaches. RAIRO: Oper. Res. 57 (2023) 2804–7303. [Google Scholar]
  • M. Rahaman, S.P. Mondal, S. Alam, S.K. De and A. Ahmadian, Study of a fuzzy production inventory model with deterioration under Marxian principle. Int. J. Fuzzy Syst. 24 (2022) 2092–2106. [CrossRef] [Google Scholar]
  • R. Rao, Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int. J. Ind. Eng. Comput. 7 (2016) 19–34. [Google Scholar]
  • J.R. Schott, Fault tolerant design using single and multicriteria genetic algorithm optimization. Massachusetts Institute of Technology (1995). [Google Scholar]
  • D. Simon, Biogeography-based optimization. IEEE Trans. Evol. Comput. 12 (2008) 702–713. [Google Scholar]
  • Solve a Mixed-Integer Engineering Design Problem Using the Genetic Algorithm. (2022). [Google Scholar]
  • S. Talatahari and M. Azizi, Chaos Game Optimization: a novel metaheuristic algorithm. Artif. Intell. Rev. 54 (2020) 917–1004. [Google Scholar]
  • Test functions for global optimization algorithms. GitHub, (2021). [Google Scholar]
  • H.R. Tizhoosh, 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). Vol. 1. IEEE (2005) 695–701. [Google Scholar]
  • Virtual Library of Simulation Experiments: Test Functions and Datasets. (2022). [Google Scholar]
  • H. Wang, Y. Jin and X. Yao, Diversity assessment in many-objective optimization. IEEE Trans. Cybern. 47 (2016) 1510–1522. [Google Scholar]
  • D.H. Wolpert and W.G. Macready, No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1 (1997) 67–82. [Google Scholar]
  • S.J. Wright, Continuous optimization (nonlinear and linear programming). Found. Comput.-Aided Process Des. 7 (1999) 1–14. [Google Scholar]
  • W. Zhao, L. Wang and S. Mirjalili, Artificial hummingbird algorithm: a new bio-inspired optimizer with its engineering applications. Comput. Methods Appl. Mech. Eng. 388 (2022) 114194. [CrossRef] [Google Scholar]
  • E. Zitzler, K. Deb and L. Thiele, Comparison of multiobjective evolutionary algorithms: empirical results. Evol. Comput. 8 (2000) 173–195. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.