EDP Sciences logo
Subscriber Authentication Point
Search
Menu
All issues Volume 59 / No 6 (November-December 2025) RAIRO-Oper. Res., 59 6 (2025) 3455-3486 References
Open Access
Issue
RAIRO-Oper. Res.
Volume 59, Number 6, November-December 2025
Page(s) 3455 - 3486
DOI https://doi.org/10.1051/ro/2025138
Published online 17 November 2025
  • X.-S. Yang, A.H. Gandomi, S. Talatahari and A.H. Alavi, Metaheuristics in Water, Geotechnical and Transport Engineering. Newnes (2012). [Google Scholar]
  • K. Hussain, M.N.M. Salleh, S. Cheng and Y. Shi, On the exploration and exploitation in popular swarm-based metaheuristic algorithms. Neural Comput. App. 31 (2019) 7665–7683. [Google Scholar]
  • X. Zhao, F. Yang, Y. Han and Y. Cui, An opposition-based chaotic salp swarm algorithm for global optimization. IEEE Access 8 (2020) 36485–36501. [Google Scholar]
  • X.-S. Yang, Nature-inspired optimization algorithms: challenges and open problems. J. Comput. Sci. 46 (2020) 101104. [Google Scholar]
  • X.-S. Yang and M. Karamanoglu, Swarm intelligence and bio-inspired computation: an overview, in Swarm Intelligence and Bio-Inspired Computation. Elsevier (2013) 3–23. [Google Scholar]
  • S. Mirjalili, S.M. Mirjalili and A. Lewis, Grey wolf optimizer. Adv. Eng. Softw. 69 (2014) 46–61. [CrossRef] [Google Scholar]
  • S. Mirjalili and A. Lewis, The whale optimization algorithm. Adv. Eng. Softw. 95 (2016) 51–67. [CrossRef] [Google Scholar]
  • W. Zhao, Z. Zhang and L. Wang, Manta ray foraging optimization: an effective bio-inspired optimizer for engineering applications. Eng. App. Artif. Intell. 87 (2020) 103300. [Google Scholar]
  • L. Abualigah, D. Yousri, M. Abd Elaziz, A.A. Ewees, M.A. Al-Qaness and A.H. Gandomi, Aquila optimizer: a novel meta-heuristic optimization algorithm. Comput. Ind. Eng. 157 (2021) 107250. [CrossRef] [Google Scholar]
  • L. Wang, Q. Cao, Z. Zhang, S. Mirjalili and W. Zhao, Artificial rabbits optimization: a new bio-inspired metaheuristic algorithm for solving engineering optimization problems. Eng. App. Artif. Intell. 114 (2022) 105082. [Google Scholar]
  • M. Dehghani, Z. Montazeri, E. Trojovská and P. Trojovsk`y, Coati optimization algorithm: a new bio-inspired metaheuristic algorithm for solving optimization problems. Knowl.-Based Syst. 259 (2023) 110011. [Google Scholar]
  • A. Seyyedabbasi and F. Kiani, Sand cat swarm optimization: a nature-inspired algorithm to solve global optimization problems. Eng. Comput. 39 (2023) 2627–2651. [Google Scholar]
  • E.-S.M. El-kenawy, N. Khodadadi, S. Mirjalili, A.A. Abdelhamid, M.M. Eid and A. Ibrahim, Greylag goose optimization: nature-inspired optimization algorithm. Expert Syst. App. 238 (2024) 122147. [Google Scholar]
  • S. Mirjalili, Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl.-Based Syst. 89 (2015) 228–249. [Google Scholar]
  • H. Zamani, M.H. Nadimi-Shahraki, S. Mirjalili, F. Soleimanian Gharehchopogh and D. Oliva, A critical review of moth-flame optimization algorithm and its variants: structural reviewing, performance evaluation, and statistical analysis. Arch. Comput. Methods Eng. 31 (2024) 2177–2225. [Google Scholar]
  • J. Lian, G. Hui, L. Ma, T. Zhu, X. Wu, A.A. Heidari, Y. Chen and H. Chen, Parrot optimizer: algorithm and applications to medical problems. Comput. Biol. Med. 172 (2024) 108064. [Google Scholar]
  • C. Yuan, D. Zhao, A.A. Heidari, L. Liu, Y. Chen, Z. Wu and H. Chen, Artemisinin optimization based on malaria therapy: algorithm and applications to medical image segmentation. Displays 84 (2024) 102740. [Google Scholar]
  • C. Yuan, D. Zhao, A.A. Heidari, L. Liu, Y. Chen and H. Chen, Polar lights optimizer: algorithm and applications in image segmentation and feature selection. Neurocomputing 607 (2024) 128427. [Google Scholar]
  • H. Su, D. Zhao, A.A. Heidari, L. Liu, X. Zhang, M. Mafarja and H. Chen, RIME: a physics-based optimization. Neurocomputing 532 (2023) 183–214. [Google Scholar]
  • D. Zouache, A. Got and H. Drias, An external archive guided Harris Hawks optimization using strengthened dominance relation for multi-objective optimization problems. Artif. Intell. Rev. 56 (2023) 2607–2638. [Google Scholar]
  • A. Got, D. Zouache and A. Moussaoui, MOMRFO: multi-objective manta ray foraging optimizer for handling engineering design problems. Knowledge-Based Systems 237 (2022) 107880. [Google Scholar]
  • L. Allou, D. Zouache, K. Amroun and A. Got, A novel epsilon-dominance Harris Hawks optimizer for multi-objective optimization in engineering design problems. Neural Comput. App. 34 (2022) 17007–17036. [Google Scholar]
  • F. Berrah, M. Chebila, F. Innal and A. Got, Cost effective analysis of the design of safety instrumented systems using manta-ray foraging optimization algorithm. Int. J. Saf. Secur. Eng. 13 (2023) 975–986. [Google Scholar]
  • D. Zouache, A. Got, D. Alarabiat, L. Abualigah and E.-G. Talbi, A novel multi-objective wrapper-based feature selection method using quantum-inspired and swarm intelligence techniques. Multimedia Tools App. 83 (2024) 22811–22835. [Google Scholar]
  • A. Got, D. Zouache, A. Moussaoui, L. Abualigah and A. Alsayat, Improved manta ray foraging optimizer-based SVM for feature selection problems: a medical case study. J. Bionic Eng. 21 (2024) 409–425. [Google Scholar]
  • A. Got, N.A. Houacine, D. Zouache and H. Drias, An optimized svm with feature selection using swarm intelligence technique, in 2024 International Conference of the African Federation of Operational Research Societies (AFROS). IEEE (2024) 1–5. [Google Scholar]
  • P. Visu, T.S. Praba, N. Sivakumar, R. Srinivasan and T. Sethukarasi, Bio-inspired dual cluster heads optimized routing algorithm for wireless sensor networks. J. Ambient Intell. Human. Comput. 12 (2021) 3753–3761. [Google Scholar]
  • J. Ma, Z. Bi, T.O. Ting, S. Hao and W. Hao, Comparative performance on photovoltaic model parameter identification via bio-inspired algorithms. Sol. Energy 132 (2016) 606–616. [Google Scholar]
  • M. Hemici, D. Zouache, B. Brahmi, A. Got and H. Drias, A decomposition-based multiobjective evolutionary algorithm using simulated annealing for the ambulance dispatching and relocation problem during COVID-19. Appl. Soft Comput. 141 (2023) 110282. [Google Scholar]
  • C. Khelfa and I. Khennak, A survey on recent optimization strategies in ambulance dispatching and relocation problems, in Artificial Intelligence Doctoral Symposium, edited by H. Drias, F. Yalaoui and A. Hadjali. Springer Nature Singapore, Singapore (2023) 192–203. [Google Scholar]
  • L.S. Bendimerad, N.A. Houacine and H. Drias, Swarm intelligent approaches for ambulance dispatching and emergency calls covering: application to COVID-19 spread in Saudi Arabia, in Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021), edited by A. Abraham, A. Engelbrecht, F. Scotti, N. Gandhi, P. Manghirmalani Mishra, G. Fortino, V. Sakalauskas and S. Pllana. Springer International Publishing, Cham (2022) 617–626. [Google Scholar]
  • X. Jin, T. He and Y. Lin, Multi-objective model selection algorithm for online sequential ultimate learning machine. EURASIP J. Wireless Commun. Networking 2019 (2019) 1–7. [Google Scholar]
  • H. Yu, K. Yuan, W. Li, N. Zhao, W. Chen, C. Huang, H. Chen and M. Wang, Improved butterfly optimizer-configured extreme learning machine for fault diagnosis. Complexity 2021 (2021) 6315010. [Google Scholar]
  • X. Liu, H. Huang and J. Xiang, A personalized diagnosis method to detect faults in gears using numerical simulation and extreme learning machine. Knowl.-Based Syst. 195 (2020) 105653. [Google Scholar]
  • S. Sharma, N. Khodadadi, A.K. Saha, F.S. Gharehchopogh and S. Mirjalili, Non-dominated sorting advanced butterfly optimization algorithm for multi-objective problems. J. Bionic Eng. 20 (2023) 819–843. [Google Scholar]
  • F.S. Gharehchopogh, B. Abdollahzadeh and B. Arasteh, An improved farmland fertility algorithm with hyperheuristic approach for solving travelling salesman problem. CMES-Comput. Model. Eng. Sci. 135 (2023) 1981. [Google Scholar]
  • M. Ayar, A. Isazadeh, F. S. Gharehchopogh, and M. Seyedi, Nsica: Multi-objective imperialist competitive algorithm for feature selection in arrhythmia diagnosis, Computers in Biology and Medicine, 161 (2023) 107025. [Google Scholar]
  • F.A. Özbay, E. Özbay and F.S. Gharehchopogh, An improved artificial rabbits optimization algorithm with chaotic local search and opposition-based learning for engineering problems and its applications in breast cancer problem. CMES-Comput. Model. Eng. Sci. 141 (2024) 1067. [Google Scholar]
  • R. Caponetto, L. Fortuna, S. Fazzino and M.G. Xibilia, Chaotic sequences to improve the performance of evolutionary algorithms. IEEE Trans. Evol. Comput. 7 (2003) 289–304. [Google Scholar]
  • D. Yang, G. Li and G. Cheng, On the efficiency of chaos optimization algorithms for global optimization. Chaos Solitons Fractals 34 (2007) 1366–1375. [Google Scholar]
  • M.S. Tavazoei and M. Haeri, Comparison of different one-dimensional maps as chaotic search pattern in chaos optimization algorithms. Appl. Math. Comput. 187 (2007) 1076–1085. [Google Scholar]
  • G. Kaur and S. Arora, Chaotic whale optimization algorithm. J. Comput. Design Eng. 5 (2018) 275–284. [Google Scholar]
  • M. Kohli and S. Arora, Chaotic grey wolf optimization algorithm for constrained optimization problems. J. Comput. Design Eng. 5 (2018) 458–472. [Google Scholar]
  • S. Arora and P. Anand, Chaotic grasshopper optimization algorithm for global optimization. Neural Comput. App. 31 (2019) 4385–4405. [Google Scholar]
  • Y. Wang, X. Zhang, D.-J. Yu, Y.-J. Bai, J.-P. Du and Z.-T. Tian, Tent chaotic map and population classification evolution strategy-based dragonfly algorithm for global optimization. Math. Prob. Eng. 2022 (2022) 2508414. [Google Scholar]
  • Y. Xu, H. Chen, A.A. Heidari, J. Luo, Q. Zhang, X. Zhao and C. Li, An efficient chaotic mutative moth-flameinspired optimizer for global optimization tasks. Expert Syst. App. 129 (2019) 135–155. [Google Scholar]
  • S. Saha and V. Mukherjee, A novel quasi-oppositional chaotic antlion optimizer for global optimization. Appl. Intell. 48 (2018) 2628–2660. [Google Scholar]
  • G.-G. Wang, L. Guo, A.H. Gandomi, G.-S. Hao and H. Wang, Chaotic krill herd algorithm. Inf. Sci. 274 (2014) 17–34. [Google Scholar]
  • G.I. Sayed, G. Khoriba and M.H. Haggag, A novel chaotic salp swarm algorithm for global optimization and feature selection. Appl. Intell. 48 (2018) 3462–3481. [Google Scholar]
  • F. Daqaq, R. Ellaia, M. Ouassaid, H.M. Zawbaa and S. Kamel, Enhanced chaotic manta ray foraging algorithm for function optimization and optimal wind farm layout problem. IEEE Access 10 (2022) 78345–78369. [Google Scholar]
  • L.-Y. Chuang, C.-J. Hsiao and C.-H. Yang, Chaotic particle swarm optimization for data clustering. Expert Syst. App. 38 (2011) 14555–14563. [Google Scholar]
  • A. Iraji, J. Karimi, S. Keawsawasvong and M.L. Nehdi, Minimum safety factor evaluation of slopes using hybrid chaotic sand cat and pattern search approach. Sustainability 14 (2022) 8097. [Google Scholar]
  • W.-F. Gao, S.-Y. Liu and L.-L. Huang, Particle swarm optimization with chaotic opposition-based population initialization and stochastic search technique. Commun. Nonlinear Sci. Numer. Simul. 17 (2012) 4316–4327. [Google Scholar]
  • L. Wang, L. Zhang, W. Zhao and X. Liu, Parameter identification of a governing system in a pumped storage unit based on an improved artificial hummingbird algorithm. Energies 15 (2022) 6966. [Google Scholar]
  • J. Basha, N. Bacanin, N. Vukobrat, M. Zivkovic, K. Venkatachalam, S. Hubálovsk`y and P. Trojovsk`y, Chaotic Harris Hawks optimization with quasi-reflection-based learning: an application to enhance CNN design. Sensors 21 (2021) 6654. [Google Scholar]
  • Y. Wang, H. Liu, G. Ding and L. Tu, Adaptive chimp optimization algorithm with chaotic map for global numerical optimization problems. J. Supercomput. 79 (2023) 6507–6537. [CrossRef] [Google Scholar]
  • Z. Elgamal, A.Q.M. Sabri, M. Tubishat, D. Tbaishat, S.N. Makhadmeh and O.A. Alomari, Improved reptile search optimization algorithm using chaotic map and simulated annealing for feature selection in medical field. IEEE Access 10 (2022) 51428–51446. [Google Scholar]
  • Y. Zhang and Y. Mo, Chaotic adaptive sailfish optimizer with genetic characteristics for global optimization. J. Supercomput. 78 (2022) 10950–10996. [Google Scholar]
  • O. Chengtian, L. Yujia and Z. Donglin, An adaptive chaotic sparrow search optimization algorithm, in 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE). IEEE (2021) 76–82. [Google Scholar]
  • S. Gao, Y. Yu, Y. Wang, J. Wang, J. Cheng and M. Zhou, Chaotic local search-based differential evolution algorithms for optimization. IEEE Trans. Syst. Man Cybern.: Syst. 51 (2019) 3954–3967. [Google Scholar]
  • S. Gupta and K. Deep, An opposition-based chaotic grey wolf optimizer for global optimisation tasks. J. Exper. Theor. Artif. Intell. 31 (2019) 751–779. [Google Scholar]
  • D. Jia, G. Zheng and M.K. Khan, An effective memetic differential evolution algorithm based on chaotic local search. Inf. Sci. 181 (2011) 3175–3187. [Google Scholar]
  • A. Chhabra, A.G. Hussien and F.A. Hashim, Improved bald eagle search algorithm for global optimization and feature selection. Alexandria Eng. J. 68 (2023) 141–180. [Google Scholar]
  • S. Li, H. Chen, M. Wang, A.A. Heidari and S. Mirjalili, Slime mould algorithm: a new method for stochastic optimization. Future Generation Comput. Syst. 111 (2020) 300–323. [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]
  • A.A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja and H. Chen, Harris Hawks optimization: algorithm and applications. Future Generation Comput. Syst. 97 (2019) 849–872. [Google Scholar]
  • R.V. Rao, V.J. Savsani and D. Vakharia, Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput.-Aided Design 43 (2011) 303–315. [Google Scholar]
  • K. Price, N. Awad, M. Ali and P. Suganthan, Problem definitions and evaluation criteria for the 100-digit challenge special session and competition on single objective numerical optimization. Technical report, Nanyang Technological University Singapore (2018). [Google Scholar]
  • G. Wu, R. Mallipeddi and P.N. Suganthan, Problem definitions and evaluation criteria for the CEC 2017 competition on constrained real-parameter optimization. Technical Report, National University of Defense Technology, Changsha, Hunan, PR China and Kyungpook National University, Daegu, South Korea and Nanyang Technological University, Singapore (2017). [Google Scholar]
  • J. Zhang and A.C. Sanderson, JADE: adaptive differential evolution with optional external archive. IEEE Trans. Evol. Comput. 13 (2009) 945–958. [CrossRef] [Google Scholar]
  • R. Tanabe and A. Fukunaga, Success-history based parameter adaptation for differential evolution, in 2013 IEEE Congress on Evolutionary Computation. IEEE (2013) 71–78. [Google Scholar]
  • J.J. Liang, A.K. Qin, P.N. Suganthan and S. Baskar, Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evol. Comput. 10 (2006) 281–295. [CrossRef] [Google Scholar]
  • H. Mittal, R. Pal, A. Kulhari and M. Saraswat, Chaotic kbest gravitational search algorithm (CKGSA), in 2016 Ninth International Conference on Contemporary Computing (IC3). IEEE (2016) 1–6. [Google Scholar]
  • Y.-J. Zhang, Y.-X. Yan, J. Zhao and Z.-M. Gao, CSCAHHO: chaotic hybridization algorithm of the sine cosine with Harris Hawk optimization algorithms for solving global optimization problems. PLoS One 17 (2022) e0263387. [Google Scholar]
  • M. Zhang, D. Long, T. Qin and J. Yang, A chaotic hybrid butterfly optimization algorithm with particle swarm optimization for high-dimensional optimization problems. Symmetry 12 (2020) 1800. [CrossRef] [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.