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All issues Volume 328 (2025) EPJ Web Conf., 328 (2025) 01004 References
Open Access
Issue
EPJ Web Conf.
Volume 328, 2025
First International Conference on Engineering and Technology for a Sustainable Future (ICETSF-2025)
Article Number 01004
Number of page(s) 36
DOI https://doi.org/10.1051/epjconf/202532801004
Published online 18 June 2025
  1. V.P. Prakash, S. Pandey, and D. Singh, "A Perspective View of Bio-Inspire Approaches Employing in Wireless Sensor Networks," 2024, pp. 18–31. doi: 10.2174/9789815049480124060004. [Google Scholar]
  2. B. Singh and M. Murugaiah, "Bio-inspired Computing and Associated Algorithms," in Series in bioengineering, 2024, pp. 47–87. doi: 10.1007/978-981-97-1017-1_3. [CrossRef] [Google Scholar]
  3. R. Ramamoorthy, R.C.A. Naidu, and M. Ashwin, "Performance Investigation of Hybrid Bio-Inspired Zone Routing Protocol Over Proactive and Reactive Routing Protocols," 2023, pp. 1–6. doi: 10.1109/icscna58489.2023.10370667. [Google Scholar]
  4. G. Devika, D. Ramesh, and A.G. Karegowda, "Analysis of binary and grey wolf optimization algorithms applied for enhancing performance of energy efficient," in M S Raimaiah Institute of Technology;, P. Ieee, Ed., 2019. [Google Scholar]
  5. X. Fan, W. Sayers, S. Zhang, and others, "Review and classification of bio-inspired algorithms and their applications," J Bionic Eng, vol. 17, pp. 611–631, 2020. [CrossRef] [Google Scholar]
  6. T. Eftimov, P. Koroec, and B. Koroui Seljak, "A novel approach to statistical comparison of meta-heuristic stochastic optimization algorithms using deep statistics," Inf Sci (N Y), vol. 417, pp. 186–215, 2017. [CrossRef] [Google Scholar]
  7. S. Das and P.N. Suganthan, "Differential evolution: A survey of the state-of-the-art," IEEE Transactions on Evolutionary Computation, vol. 15, no. 1 pp. 4–31, 2011. [CrossRef] [Google Scholar]
  8. W.A. Hammod, K. Zamil, and A. Ali, "A review of bio-inspired algorithms," in A Conference: (SOFTEC Asia 2017). Malaysia: Kuala Lumpur Convention Centre, 2017. [Google Scholar]
  9. C. Kang, S. Wang, W. Ren, Y. Lu, and B. Wang, "Optimization design and application of active disturbance rejection controller based on intelligent algorithm," IEEE Access, vol. 7, pp. 59862–59870, 2019. [CrossRef] [Google Scholar]
  10. A.K. Kar, "Bio-inspired computing?A review of algorithms and scope of applications," Expert Syst Appl, vol. 5, pp. 20–32, 2016. [CrossRef] [Google Scholar]
  11. S. Swayamsiddha, "Bio-inspired algorithms: principles, implementation, and applications to wireless communication," 2020, pp. 49–63. doi: 10.1016/B978-0-12-819714-1.00013-0. [Google Scholar]
  12. A. Raychaudhuri and D. De, "Bio-inspired Algorithm for Multi-objective Optimization in Wireless Sensor Network," 2020, pp. 279–301. doi: 10.1007/978981-15-2125-6 12. [Google Scholar]
  13. E. Hernândez-Huerta et al., "Communication of Mobile Devices with Bioinspired Algorithms.," vol. 11, no. 3, pp. 37–48, 2020. [Google Scholar]
  14. C. Rajan, K. Geetha, C.R. Priya, and R. Sasikala, "Investigation on Bio-Inspired Population Based Metaheuristic Algorithms for Optimization Problems in Ad Hoc Networks," World Academy of Science, Engineering and Technology, International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering, vol. 9, no. 3 pp. 163–170, 2015. [Google Scholar]
  15. P. Game, V. Vaze, and M. Emmanuel, "Bio-inspired Optimization: metaheuristic algorithms for optimization.," 2020. [Google Scholar]
  16. E.L. Priya, C.S. Sreekari, and G. Jeyakumar, "A Comparative Study on the Performance of Bio-inspired Algorithms on Benchmarking and Real-World Optimization Problems," 2021, pp. 411–417. doi: 10.1007/978-981-33-4543-044. [Google Scholar]
  17. Y. Azzoug and A. Boukra, "Bio-inspired VANET routing optimization: an overview," Artif Intell Rev, vol. 54, no. 2, pp. 1005–1062, 2021, doi: 10.1007/S10462-020-09868-9. [CrossRef] [Google Scholar]
  18. C. Clarccle and J. Kennedy, "A particle swarm-explosive, stability and convergence in multi-dimensional complex space," IEEE Transactions on Evolutionary Computation, vol. 2002, no. 6 pp. 58–73, 2010. [Google Scholar]
  19. D. Bozhinoski, "Swarm intelligence-based bio-inspired algorithms," 2024, doi: 10.1145/3643915.3644086. [Google Scholar]
  20. M. Hajihassani, D. Jahed Armaghani, and R. Kalatehjari, "Applications of particle swarm optimization in geotechnical engineering: A comprehensive review," Geotechnical and Geological Engineering, vol. 36, pp. 705–722, 2018. [CrossRef] [Google Scholar]
  21. F. Dressler and O.B. Akan, "Bio-inspired networking: from theory to practice," IEEE Communications Magazine, vol. 48, no. 11 pp. 176–183, 2010, doi: 10.1109/MCOM.2010.5621985. [CrossRef] [Google Scholar]
  22. I.N. Trivedi, P. Jangir, A. Kumar, N. Jangir, and R. Totlani, "A novel hybrid PSO?WOA algorithm for global numerical functions optimization," in Advances in Computer and Computational Sciences, Cham, Switzerland: Springer, 2018. [Google Scholar]
  23. J. Weiand Wang, "A dynamical particle swarm optimization with dimensional mutation," IJCSNS International journal of computer Science and Network Security, vol. 6, pp. 221–224, 2006. [Google Scholar]
  24. D. Rai and K. Tyagi, "Bio-inspired optimization techniques: a critical comparative study," ACMSigsoft Software Engineering Notes, vol. 38, no. 4 pp. 1–7, 2013, doi: 10.1145/2492248.2492271. [CrossRef] [Google Scholar]
  25. R.-J. Ma, N.-Y. Yu, and J.-Y. Hu, "Application of particle swarm optimization algorithm in the heating system planning problem," Scientific World Journal, vol. 2013, p. 718345, 2013. [CrossRef] [PubMed] [Google Scholar]
  26. D. Karaboga, B. Akay, and C. Ozturk, "Artificial bee colony (ABC) optimization algorithm for training feed-forward neural networks," MDAI, vol. 7, pp. 318–319, 2007. [Google Scholar]
  27. H.M. Alshamlan, G.H. Badr, and Y.A. Alohali, "Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification," Comput Biol Chem, vol. 56, pp. 49–60, 2015. [CrossRef] [PubMed] [Google Scholar]
  28. M. Mao, Q. Duan, P. Duan, and B. Hu, "Comprehensive improvement of artificial fish swarm algorithm for global mppt in pv system under partial shading conditions," Transactions of the Institute of Measurement and Control, vol. 40, no. 7 pp. 2178–2199, 2018. [CrossRef] [Google Scholar]
  29. Y. Feng, S. Zhao, and H. Liu, "Analysis of network coverage optimization based on feedback k-means clustering and swarm algorithm," IEEE Access, vol. 8, pp. 42864–42876, 2020. [CrossRef] [Google Scholar]
  30. X. Li, B. Keegan, and F. Mtenzi, "Energy efficient hybrid routing protocol based on the artificial fish swarm algorithm and ant colony optimisation for wsns," Sensors, vol. 18, no. 10 pp. 3351, 2018, [CrossRef] [PubMed] [Google Scholar]
  31. A.B. Serapiao, G.S. Correa, F.B. Goncalves, and V.O. Carvalho, "Combining K-means and K-harmonic with fish school search algorithm for data clustering task on graphics processing units," Appl Soft Comput, vol. 41, pp. 290–304, 2016. [CrossRef] [Google Scholar]
  32. L. Yan, Y. He, and Z. Huangfu, "A fish swarm inspired holes recovery algorithm for wireless sensor networks," Int J Wirel Inf Netw, vol. 27, no. 1 pp. 89–101, 2020. [CrossRef] [Google Scholar]
  33. T. Du, Y. Hu, and X. Ke, "Improved quantum artificial fish algorithm application to distributed network considering distributed generation," Comput Intell Neurosci, vol. 2015, p. 91, 2015. [Google Scholar]
  34. N. He, A. Belacel, H. Chan, and Y. Hamam, "A hybrid artificial fish swarm simulated annealing optimization algorithm for automatic identification of clusters," Int J Inf Technol Decis Mak, vol. 15, no. 05 pp. 949–974, 2016. [CrossRef] [Google Scholar]
  35. D.J. Sathya and K. Geetha, "Hybrid ANN optimized artificial fish swarm algorithm based classifier for classification of suspicious lesions in breast DCE-MRI," Polish Journal of Medical Physics and Engineering, vol. 23, no. 4 pp. 81–88, 2017. [CrossRef] [Google Scholar]
  36. Y. Zhu, W. Xu, G. Luo, H. Wang, J. Yang, and W. Lu, "Random forest enhancement using improved artificial fish swarm for the medial knee contact force prediction," Artif Intell Med, vol. 103, pp. 101811, 2020. [CrossRef] [PubMed] [Google Scholar]
  37. W. Yan, M. Li, X. Pan, G. Wu, and L. Liu, "Application of support vector regression cooperated with modified artificial fish swarm algorithm for wind tunnel performance prediction of automotive radiators," Appl Therm Eng, vol. 164, pp. 114543, 2020. [CrossRef] [Google Scholar]
  38. Y. Liu, X. Wu, and Y. Shen, "Cat swarm optimizing clustering: A cat swarm optimization in advances in swarm intelligence," Lecture Notes in Computer Science (LNCS, vol. 6782), pp. 321–328, 2011. [Google Scholar]
  39. S.K. Saha, S.P. Ghoshal, R. Kar, and D. Mandal, Cat Swarm Optimization Algorithm for Optimal Linear Phase FIR Filter Design. Elsevier Ltd; ISA Transactions in Press, 2013. [Google Scholar]
  40. C.K. Faseela and H. Vennila, Economic and Emission Dispatch using Whale optimization Algorithm (WOA). In: in IJECE, 2018. [Google Scholar]
  41. I. Aljarah, H. Faris, and S. Mirjalili, "Optimizing connection weights in neural networks using the whale optimization algorithm," Soft comput, vol. 22, no. 1 pp. 1–15, 2018. [CrossRef] [Google Scholar]
  42. Z. Yan, J. Sha, B. Liu, W. Tian, and J. Lu, "An ameliorative whale optimization algorithm for multi-objective optimal allocation of water resources in Handan, China," Water (Basel), vol. 10, pp. 87, 2018. [Google Scholar]
  43. S.T. Revathi, N. Ramaraj, and S. Chithra, "Brain storm-based Whale Optimization Algorithm for privacy-protected data publishing in cloud computing," Cluster Comput, vol. 21, pp. 1–10, 2018. [CrossRef] [Google Scholar]
  44. A.N. Jadhav and N. Gomathi, "WGC: Hybridization of exponential grey wolf optimizer with whale optimization for data clustering," Alexandria Engineering Journal, vol. 57, no. 3 pp. 1569–1584, 2017. [Google Scholar]
  45. Z. Xu, Y. Yu, H. Yachi, J. Ji, Y. Todo, and S. Gao, "A novel memetic whale optimization algorithm for optimization," in International Conference on Swarm Intelligence. Cham, Switzerland, Springer, 2018. [Google Scholar]
  46. M.M. Mafarja and S. Mirjalili, "Hybrid Whale Optimization Algorithm with simulated annealing for feature selection," Neurocomputing, vol. 260, pp. 302–312, 2017. [CrossRef] [Google Scholar]
  47. A. Kaveh and M.I. Ghazaan, "Enhanced whale optimization algorithm for sizing optimization of skeletal structures," Mechanics Based Design of Structures and Machines, vol. 45, no. 3 pp. 345–362, 2017. [CrossRef] [Google Scholar]
  48. D. Oliva, M.A. El Aziz, and A.E. Hassanien, "Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm," Appl Energy, vol. 200, pp. 141–154, 2017. [CrossRef] [Google Scholar]
  49. A. Mostafa, A.E. Hassanien, M. Houseni, and H. Hefny, "Liver segmentation in MRI images based on whale optimization algorithm," Multimed Tools Appl, vol. 76, no. 23 pp. 24931–24954, 2017. [CrossRef] [Google Scholar]
  50. M. Fong, "A multi-objective optimal vehicle fuel consumption based on whale optimization algorithm," in Advances in Intelligent Information Hiding and Multimedia Signal Processing, 2016. [Google Scholar]
  51. D. Prakash and C. Lakshminarayana, "Optimal siting of capacitors in radial distribution network using Whale Optimization Algorithm," Alexandria Engineering Journal, vol. 56, no. 4 pp. 499–509, 2017. [CrossRef] [Google Scholar]
  52. M. Abdel-Basset, D. El-Shahat, I. Elhenawy, A.K. Sangaiah, and S.H. Ahmed, "A novel whale optimization algorithm for cryptanalysis in merkle-hellman cryptosystem," Mobile Networks and Applications, vol. 23, no. 4 pp. 723–733, 2018. [CrossRef] [Google Scholar]
  53. S.A. Uymaz, G. Tezel, and E. Yel, "Artificial algae algorithm (AAA) for nonlinear global optimization," Appl Soft Comput, vol. 2015, pp. 153, 2015. [CrossRef] [Google Scholar]
  54. C.P. Liu and C.M. Ye, "Solving permutation flow shop scheduling problem by firefly algorithm," Industrial Engineering Management, vol. 17, pp. 56–59, 2012. [Google Scholar]
  55. V. Fernandez-Viagas and J.M. Framinan, "A bounded-search iterated greedy algorithm for the distributed permutation flowshop scheduling problem," Int J Prod Res, vol. 53, pp. 1111–1123, 2015. [CrossRef] [Google Scholar]
  56. O. Thiare, A.A.A. Ari, A.M. Gueroui, S. Khemiri-Kallel, and J. Hwang, "Bio-inspired Solution for Cluster-Tree Based Data Collection Protocol in Wireless Sensors Networks," 2023, pp. 1–6. doi: 10.1109/NOMS56928.2023.10154279. [Google Scholar]
  57. X.S. Yang and S. Deb, "Cuckoo search: Recent advances and application," Neural Comput Appl, vol. 24, pp. 169–174, 2014. [CrossRef] [Google Scholar]
  58. M.K. Naik and R. Panda, "A novel adaptive cuckoo search algorithm for intrinsic discriminant analysis based face recognition," Appl Soft Comput, vol. 38, pp. 661–675, 2016. [CrossRef] [Google Scholar]
  59. P. Ong, "Adaptive cuckoo search algorithm for unconstrained optimization," Scientific World Journal, vol. 2014, pp. 1–8, 2014. [CrossRef] [Google Scholar]
  60. V.R. Chifu, C.B. Pop, I. Salomie, D.S. Suia, and A.N. Niculici, "Optimizing the semantic web service composition process using cuckoo search," Comput Intell, vol. 382, pp. 93–102, 2012. [Google Scholar]
  61. A.R. Yildiz, "Cuckoo search algorithm for the selection of optimal machine parameters in milling operations," International Journal of Advanced Manufacturing Technology, vol. 64, pp. 55–61, 2012. [Google Scholar]
  62. M.K. Naik, R.N. Maheshwari, A. Wunnava, and others, "A new adaptive cuckoo search algorithm," in IEEE International Conference on Recent Trends in Information Systems. Kolkata; 2015, 2015, pp. 1–5. [Google Scholar]
  63. T. Ahmed and S. Obaidi, "Improved Scatter Search Using Cuckoo Search," International Journal of Advanced Research in Artificial Intelligence, vol. 2, pp. 61–67, 2013. [Google Scholar]
  64. S. Burnwal and S. Deb, "Scheduling optimization of flexible manufacturing system using cuckoo search-based approach," International Journal of Advanced and Manufacturing Technology, vol. 64, pp. 954–959, 2012. [Google Scholar]
  65. H. Faris, I. Aljarah, and S. Mirjalili, "Evolving radial basis function networks using moth?flame optimizer," in Handbook of Neural Computation, Vol. 28.; Elsevier, 2017, pp. 537–550. [CrossRef] [Google Scholar]
  66. A.E. Hassanien, T. Gaber, U. Mokhtar, and H. Hefny, "An improved moth flame optimization algorithm based on rough sets for tomato diseases detection," Comput Electron Agric, vol. 136, pp. 86–96, 2017. [CrossRef] [Google Scholar]
  67. P. Singh and S. Prakash, "Optical network unit placement in fiber-wireless (fiwi) access network by moth-flame optimization algorithm," Optical Fiber Technology, vol. 36, pp. 403–411, 2017. [CrossRef] [Google Scholar]
  68. M. Wang et al., "Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses," Neurocomputing, vol. 267, pp. 69–84, 2017. [CrossRef] [Google Scholar]

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