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Yıl 2024, Cilt: 42 Sayı: 3, 919 - 944, 12.06.2024

Öz

Kaynakça

  • REFERENCES
  • [1] Tan R, Liu W, Cao G, Shi Y. Creative design inspired by biological knowledge: Technologies and methods. Front Mech Eng 2019;14:1–14.
  • [2] Rovalo E, McCardle J. Performance based abstraction of biomimicry design principles using prototyping. Designs 2019;3:38.
  • [3] Graeff E, Maranzana N, Aoussat A. Biological practices and fields, missing pieces of the biomimetics' methodological puzzle. Biomimetics (Basel) 2020;5:62.
  • [4] Hoornaert SG, Lassabe N, Finzinger C, Penalva M, Chirazi J, et al. Nature can inspire solutions for aeronautics and space sciences. Aeronaut Aerosp Open Access J 2020;4:69–79.
  • [5] Brodrick D. Natural genius: Approaches and challenges to applying biomimetic design principles in architecture (doctoral thesis). Colorado: Colorado Boulder Univ; 2020.
  • [6] Wang J, Chen W, Xiao X, Xu Y, Li C, Jia X, et al. A survey of the development of biomimetic intelligence and robotics. Biomimetic Intell Robot 2021;1:100001.
  • [7] Chiesa M, Maioli G, Colombo GI, Piacentini L. GARS: Genetic Algorithm for the identification of a Robust Subset of features in high-dimensional datasets. BMC Bioinformatics 2020;21:54.
  • [8] Drachal K, Pawlowski M. A review of the applications of Genetic Algorithms to forecasting prices of commodities. Economies 2021;9:6.
  • [9] Albadr MA, Tiun S, Ayob M, AL-Dhief F. Genetic Algorithm based on Natural Selection Theory for optimization problems. Symmetry 2020;12:1758.
  • [10] Georgioudakis M, Plevris V. A comparative study of Differential Evolution Variants in constrained Structural Optimization. Front Built Environ 2020;6:102.
  • [11] Du Y, Fan Y, Liu X, Luo Y, Tang J, Liu P. Multiscale cooperative differential evolution algorithm. Comput Intell Neurosci 2019;2019:5259129.
  • [12] Nabil E, Sayed SAF, Hameed HA. An efficient binary clonal selection algorithm with optimum path forest for feature selection. Int J Adv Comput Sci Appl 2020;11.
  • [13] Yu J, Li R, Feng Z, Zhao A. A novel parallel ant colony optimization algorithm for warehouse path planning. J Control Sci Eng 2020;2020:5287189.
  • [14] Kruekaew B, Kimpan W. Enhancing of artificial bee colony algorithm for virtual machine scheduling and load balancing problem in cloud computing. Int J Comput Intell Syst 2020;13:496– 510.
  • [15] Hantash N, Khatib T, Khammash M. An improved particle swarm optimization algorithm for optimal allocation of distributed generation units in radial power systems. Appl Comput Intell Soft Comput 2020;2020:8824988.
  • [16] Khan A, Khan A, Bangash JI, Subhan F, Khan A, Khan A, et al. Cuckoo Search-based SVM (CS-SVM) model for real time indoor position estimation in IoT networks. Secur Commun Netw 2021;2021:6654926.
  • [17] Zebari AY, Almufti SM, Abdulrahman CM. Bat algorithm: Review applications and modifications. Int J Sci World 2020;8:1–7.
  • [18] Odili JB, Noraziah A, Babalola AE. Flower pollination algorithm for data generation and analytics - A diagnostic analysis. Sci Afr 2020;8:e00440.
  • [19] Yang W, Sun Z. GPS position prediction method based on chaotic map-based flower pollination algorithm. Complexity 2021;2021:9972701.
  • [20] Altherwi A. Application of Firefly algorithm for optimal production and demand forecasting at selected industrial plant. Open J Bus Manag 2020;8:2451–2459.
  • [21] Nayak J, Naik B, Dinesh P, Vakula K, Dash PB. Firefly Algorithm in biomedical and health care: Advances, issues and challenges. SN Comput Sci 2020;1:311.
  • [22] EESA AS, Sadiq S, Hassan MM, Orman Z. Rule generation based on modified cuttlefish algorithm for intrusion detection system. Uludağ Univ J Fac Eng 2021;26:253–268.
  • [23] Ma J, Liu Y, Zang S, Wang L. Robot path planning based on Genetic Algorithm fused with continuous Bezier Optimization. Comput Intell Neurosci 2020;2020:9813040.
  • [24] Mashwani WK, Rehman ZU, Bakar MA, Kocak I. A customized differential evolutionary algorithm for bounded constrained optimization problems. Complexity 2021;2021;5515701.
  • [25] Telleria MC, Zulueta E, Gamiz UF, Betoño DTF, Betoño ATF. Differential evolution optimal parameters tuning with artificial neural network. Mathematics 2021;9:427.
  • [26] Yang C, Chen BQ, Jia L, Wen HY. Improved clonal selection algorithm based on biological forgetting mechanism. Complexity 2020;2020:2807056.
  • [27] Zhang W, Gao K, Zhang W, Wang X, Zhang Q, Wang H. A hybrid clonal selection algorithm with modified combinatorial recombination and success history based adaptive mutation for numerical optimization. Artif Intell 2019;49:819–836.
  • [28] Purbasari A, Hidayanto AN, Zulianto A. Parallelization clonal selection algorithm with MPI.NET for optimization problem. Int J Innov Technol Explor Eng 2019;8:184–191.
  • [29] Zhang W, Zhang W, Yen G, Jing H. A cluster-based clonal selection algorithm for Optimisation in dynamic environment. Swarm Evol Comput 2019;50:100454.
  • [30] Barisik T, Guneri AF. BLEVE risk effect estimation using the Levenberg-Marquardt algorithm in an artificial neural network model. Sigma J Eng Nat Sci 2022;40:877–893.
  • [31] Gutiérrez-Muñoz M, González-Salazar A, Coto-Jiménez M. Evaluation of mixed deep neural networks for reverberant speech enhancement. Biomimetics (Basel) 2019;5:1.
  • [32] Kaya N, Erdem F. Modeling of copper removal from aqueous solutions by using carbon-based adsorbents derived from hazelnut and walnut shells by artificial neural network. Sigma J Eng Nat Sci 2022;40:695–704.
  • [33] Fahad LG, Tahir SF, Shahzad W, Hassan M. Ant colony optimization-based streaming feature selection: An application to the medical image diagnosis. Sci Program 2020;9:1064934.
  • [34] Li X, Wang L. Application of improved ant colony optimization in mobile robot trajectory planning. Math Biosci Eng 2020;17:6756–6774.
  • [35] Wang H, Wang W, Zhou X, Zhao J, Wang Y, Xiao S, et al. Artificial bee colony algorithm based on knowledge fusion. Complex Intell Syst 2021;7:1139–1152.
  • [36] Li X, Wu D, He J, Bashir M, Liping M. An improved method of particle swarm optimization for path planning of mobile robot. J Control Sci Eng 2020;2020:3857894.
  • [37] Digehsara AP, Chegini NS, Bagheri A, Roknsaraei PM. An improved particle swarm optimization based on the reinforcement of the population initialization phase by scrambled halt on sequence. Cogent Eng 2020;7:1737283.
  • [38] Yu H, Gao Y, Wang L, Meng J. A hybrid particle swarm optimization algorithm enhanced with nonlinear inertial weight and guassian mutation for job shop scheduling problems. Mathematics 2020;8:1355.
  • [39] Guerrero-Luis M, Valdez F, Castillo O. A Review on the Cuckoo Search Algorithm. In: Castillo O, Melin P, editors. Fuzzy logic hybrid extensions of neural and optimization algorithms: Theory and applications, studies in computational intelligence. New York: Springer; 2021. p. 113–124.
  • [40] Wu Y, Wang Z. Improved Cuckoo search algorithm for multiple odor sources localization. In the Proceedings of the 13th International Conference on Agents and Artificial Intelligence; 2021 Feb 4–6; Vienna, Austria: ICAART; 2021. p. 708–715.
  • [41] Manar A, Al-Abaji MA. Cuckoo search algorithm: Review and its applications. Tikrit J Pure Sci 2021;26:137–144.
  • [42] Pan JS, Song PC, Chu SC, Peng YJ. Improved compact Cuckoo search algorithm applied to location of drone Logistics hub. Mathematics 2020;8:333.
  • [43] Huang J, Ma Y. Bat Algorithm based on an Integration Strategy and Gaussian distribution. Math Probl Eng 2020;2020:9495281.
  • [44] Mashwani WK, Mehmood I, Bakar MA, Koçcak I. A modified Bat Algorithm for Solving large-scale bound constrained global optimization problems. Math Probl Eng 2021;2021:6636918.
  • [45] Karakonstantis I, Vlachos A. Bat algorithm applied to continuous constrained Optimization problems. J Inf Optim Sci 2021;42:57–75.
  • [46] Khursheed M, Nadeem MF, Khalil A, Sajjad IA, Raza A, Iqbal MQ, et al. Review of flower pollination algorithm: Applications and Variants. In Proceedings of the 2020 International Conference on Engineering and Emerging Technologies; 2020 Feb 22–23; Lahore, Pakistan: ICEET; 2020.
  • [47] Suwannarongsri S. Application of modified flower pollination algorithm to multiple vehicle routing problems with time constraints. WSEAS Trans Syst Control 2020;15:459–467.
  • [48] Wang K, Li X, Gao L. A multi-objective discrete flower pollination algorithm for stochastic two-sided partial disassembly line balancing problem. Comput Ind Eng 2019;130:634–649.
  • [49] Wu J, Wang Y, Burrage K, Tian Y, Lawson BA, Ding Z. An improved firefly algorithm for global continuous optimization problems. Expert Syst Appl 2020;149:113340.
  • [50] Dai W, Liang L, Zhang B. Firefly optimization algorithm for the prediction of uplift due to high-pressure jet grouting. Adv Civ Eng 2020;2020:8833784.
  • [51] Marie-Sainte SL, Alalyani N. Firefly algorithm based feature selection for Arabic text classification. J King Saud Univ Comput Inf Sci 2020;32:320–328. [52] Daweri MSA, Abdullah S, Ariffin KAZ. A migration based cuttlefish algorithm with short term memory for optimization problems. IEEE Access 2020;8:70270–70292.
  • [53] Hussien AM, Mekhamer SF, Hasanien HM. Cuttlefish Optimisation algorithm based optimal PI controller for performance enhancement of an autonomous operation of a DG system. In Proceedings of the 2nd International Conference on Smart Power and Internet Energy Systems; 2020 Sept 15–18; Bangkok, Thailand; 2020. p. 293–298.
  • [54] Eesa AS, Orman Z. A new clustering method based on the bio-inspired cuttlefish optimization algorithm. Expert Syst 2020;37:e12478.
  • [55] Liang S, Song B, Xue D. Landing route planning method for micro drones based on hybrid optimization algorithm. Biomimetic Intell Robot 2021;1:100003.
  • [56] Aracri S, Giorgio-Serchi F, Suaria G, Sayed ME, Nemitz MP, Mahon S, et al. Soft robots for ocean exploration and offshore operations: A perspective. Soft Robot 2021;8:625–639.
  • [57] Bhowmik P, Pantho MJH, Bobda C. Bio-inspired smart vision sensor: Toward a reconfigurable hardware modeling of the hierarchical processing in the brain. J Real Time Image Proc 2021;18:157–174.
  • [58] Drotman D, Jadhav S, Sharp D, Chan C, Tolley MT. Electronics-free pneumatic circuits for controlling soft-legged robots. Sci Robot 2021;6:eaay2627.
  • [59] Ivanova N. Biomimetic optics: Liquid-based optical elements imitating the eye functionality. Philos Trans A Math Phys Eng Sci 2020;378:20190442.
  • [60] Ren Z, Hu W, Dong X, Sitti M. Multi-functional soft-bodied jellyfish-like swimming. Nat Commun 2019;10:2703.
  • [61] Le LM, Ly HB, Pham BT, Le VM, Pham TA, Nguyen DH, et al. Hybrid artificial ıntelligence approaches for predicting buckling damage of steel columns under axial compression. Materials (Basel) 2019;12:1670.
  • [62] Orozco-Rosas U, Picos K, Montiel O. Hybrid path planning algorithm based on membrane pseudo-bacterial potential field for autonomous mobile robots. IEEE Access 2019;7:156787– 156803.
  • [63] Lin T, Lin H, Lin C, Chen C. Mobile robot wall-following control using a behavior-based fuzzy controller in unknown environments. Iran J Fuzzy Syst 2019;16:113–124.
  • [64] Martin-Palma JR, Kolle M. Biomimetic photonic structures for optical sensing. Opt Laser Technol 2019;109:270–277.
  • [65] Hancock M, Sood S, Higgins T, Sproul K. Biomimicry and Machine Learning in the Context of Healthcare Digitization. In: Schmorrow DD, Fidopiastis CM, editors. Augment cognition. New York: Springer; 2019. p. 273–283.
  • [66] Lamata L. Quantum machine learning and quantum biomimetics: A perspective. Mach Learn Sci Technol 2020;1:033002.
  • [67] Wanieck K, Ritzinger D, Zollfrank C, Jacobs S. Biomimetics: Teaching the tools of the trade. FEBS Open Bio 2020;10:2250–2267.
  • [68] Di Salvo S. Advances in research for biomimetic materials. Adv Mater Res 2018;1149:28–40.
  • [69] Ababsa T, Djedl N, Duthen Y. Genetic programming-based self-reconfiguration planning for metamorphic robot. Int J Autom Comput 2018;15:431–442.
  • [70] Blanco E, Uchiyama Y, Kohsaka R. Application of biomimetics to architectural and urban design: A review across scales. Sustainability 2020;12:9813.
  • [71] Ha N, Lu G. A review of recent research on bio-inspired structured and materials for energy absorption applications. Compos Part B Eng 2020;181:107496.
  • [72] Sreenivasulu R, Chaitanya G. Self adaptive penalty method coupled with metaheuristic algorithms to optimization of varying geometrical parameters in drilling for multi hole parts. Sigma J Eng Nat Sci 2022;40:855–867.
  • [73] Izci D. A novel modified arithmetic optimization algorithm for power system stabilizer design. Sigma J Eng Nat Sci 2022;40:529–541.
  • [74]Ahmad AA, Sari M. Parameter estimation to an anemia model using the Particle Swarm Optimization. Sigma J Eng Nat Sci 2019;37:1335–1347

A systematic comparative study of popular biomimetic intelligence techniques

Yıl 2024, Cilt: 42 Sayı: 3, 919 - 944, 12.06.2024

Öz

Biomimetics is an emerging field that allows mimicry of living organisms in nature to develop different techniques so as to solve hard and complex problems related to optimization. The different techniques developed in this field takes inspiration from biology or nature. Biolo-gy acts as a powerful tool for imitating, copying, learning, understanding and inspiring the development of new systems and models. The different techniques discussed in this paper include techniques based on evolutionary algorithms, neural network and swarm intelligence. All these techniques are biologically inspired and provide good accuracy. The accuracy of all these algorithms can be increased by using them in hybrid form with other techniques and us-ing different datasets. The comparative analysis of these techniques is done using advantages, disadvantages and applications of these techniques.

Kaynakça

  • REFERENCES
  • [1] Tan R, Liu W, Cao G, Shi Y. Creative design inspired by biological knowledge: Technologies and methods. Front Mech Eng 2019;14:1–14.
  • [2] Rovalo E, McCardle J. Performance based abstraction of biomimicry design principles using prototyping. Designs 2019;3:38.
  • [3] Graeff E, Maranzana N, Aoussat A. Biological practices and fields, missing pieces of the biomimetics' methodological puzzle. Biomimetics (Basel) 2020;5:62.
  • [4] Hoornaert SG, Lassabe N, Finzinger C, Penalva M, Chirazi J, et al. Nature can inspire solutions for aeronautics and space sciences. Aeronaut Aerosp Open Access J 2020;4:69–79.
  • [5] Brodrick D. Natural genius: Approaches and challenges to applying biomimetic design principles in architecture (doctoral thesis). Colorado: Colorado Boulder Univ; 2020.
  • [6] Wang J, Chen W, Xiao X, Xu Y, Li C, Jia X, et al. A survey of the development of biomimetic intelligence and robotics. Biomimetic Intell Robot 2021;1:100001.
  • [7] Chiesa M, Maioli G, Colombo GI, Piacentini L. GARS: Genetic Algorithm for the identification of a Robust Subset of features in high-dimensional datasets. BMC Bioinformatics 2020;21:54.
  • [8] Drachal K, Pawlowski M. A review of the applications of Genetic Algorithms to forecasting prices of commodities. Economies 2021;9:6.
  • [9] Albadr MA, Tiun S, Ayob M, AL-Dhief F. Genetic Algorithm based on Natural Selection Theory for optimization problems. Symmetry 2020;12:1758.
  • [10] Georgioudakis M, Plevris V. A comparative study of Differential Evolution Variants in constrained Structural Optimization. Front Built Environ 2020;6:102.
  • [11] Du Y, Fan Y, Liu X, Luo Y, Tang J, Liu P. Multiscale cooperative differential evolution algorithm. Comput Intell Neurosci 2019;2019:5259129.
  • [12] Nabil E, Sayed SAF, Hameed HA. An efficient binary clonal selection algorithm with optimum path forest for feature selection. Int J Adv Comput Sci Appl 2020;11.
  • [13] Yu J, Li R, Feng Z, Zhao A. A novel parallel ant colony optimization algorithm for warehouse path planning. J Control Sci Eng 2020;2020:5287189.
  • [14] Kruekaew B, Kimpan W. Enhancing of artificial bee colony algorithm for virtual machine scheduling and load balancing problem in cloud computing. Int J Comput Intell Syst 2020;13:496– 510.
  • [15] Hantash N, Khatib T, Khammash M. An improved particle swarm optimization algorithm for optimal allocation of distributed generation units in radial power systems. Appl Comput Intell Soft Comput 2020;2020:8824988.
  • [16] Khan A, Khan A, Bangash JI, Subhan F, Khan A, Khan A, et al. Cuckoo Search-based SVM (CS-SVM) model for real time indoor position estimation in IoT networks. Secur Commun Netw 2021;2021:6654926.
  • [17] Zebari AY, Almufti SM, Abdulrahman CM. Bat algorithm: Review applications and modifications. Int J Sci World 2020;8:1–7.
  • [18] Odili JB, Noraziah A, Babalola AE. Flower pollination algorithm for data generation and analytics - A diagnostic analysis. Sci Afr 2020;8:e00440.
  • [19] Yang W, Sun Z. GPS position prediction method based on chaotic map-based flower pollination algorithm. Complexity 2021;2021:9972701.
  • [20] Altherwi A. Application of Firefly algorithm for optimal production and demand forecasting at selected industrial plant. Open J Bus Manag 2020;8:2451–2459.
  • [21] Nayak J, Naik B, Dinesh P, Vakula K, Dash PB. Firefly Algorithm in biomedical and health care: Advances, issues and challenges. SN Comput Sci 2020;1:311.
  • [22] EESA AS, Sadiq S, Hassan MM, Orman Z. Rule generation based on modified cuttlefish algorithm for intrusion detection system. Uludağ Univ J Fac Eng 2021;26:253–268.
  • [23] Ma J, Liu Y, Zang S, Wang L. Robot path planning based on Genetic Algorithm fused with continuous Bezier Optimization. Comput Intell Neurosci 2020;2020:9813040.
  • [24] Mashwani WK, Rehman ZU, Bakar MA, Kocak I. A customized differential evolutionary algorithm for bounded constrained optimization problems. Complexity 2021;2021;5515701.
  • [25] Telleria MC, Zulueta E, Gamiz UF, Betoño DTF, Betoño ATF. Differential evolution optimal parameters tuning with artificial neural network. Mathematics 2021;9:427.
  • [26] Yang C, Chen BQ, Jia L, Wen HY. Improved clonal selection algorithm based on biological forgetting mechanism. Complexity 2020;2020:2807056.
  • [27] Zhang W, Gao K, Zhang W, Wang X, Zhang Q, Wang H. A hybrid clonal selection algorithm with modified combinatorial recombination and success history based adaptive mutation for numerical optimization. Artif Intell 2019;49:819–836.
  • [28] Purbasari A, Hidayanto AN, Zulianto A. Parallelization clonal selection algorithm with MPI.NET for optimization problem. Int J Innov Technol Explor Eng 2019;8:184–191.
  • [29] Zhang W, Zhang W, Yen G, Jing H. A cluster-based clonal selection algorithm for Optimisation in dynamic environment. Swarm Evol Comput 2019;50:100454.
  • [30] Barisik T, Guneri AF. BLEVE risk effect estimation using the Levenberg-Marquardt algorithm in an artificial neural network model. Sigma J Eng Nat Sci 2022;40:877–893.
  • [31] Gutiérrez-Muñoz M, González-Salazar A, Coto-Jiménez M. Evaluation of mixed deep neural networks for reverberant speech enhancement. Biomimetics (Basel) 2019;5:1.
  • [32] Kaya N, Erdem F. Modeling of copper removal from aqueous solutions by using carbon-based adsorbents derived from hazelnut and walnut shells by artificial neural network. Sigma J Eng Nat Sci 2022;40:695–704.
  • [33] Fahad LG, Tahir SF, Shahzad W, Hassan M. Ant colony optimization-based streaming feature selection: An application to the medical image diagnosis. Sci Program 2020;9:1064934.
  • [34] Li X, Wang L. Application of improved ant colony optimization in mobile robot trajectory planning. Math Biosci Eng 2020;17:6756–6774.
  • [35] Wang H, Wang W, Zhou X, Zhao J, Wang Y, Xiao S, et al. Artificial bee colony algorithm based on knowledge fusion. Complex Intell Syst 2021;7:1139–1152.
  • [36] Li X, Wu D, He J, Bashir M, Liping M. An improved method of particle swarm optimization for path planning of mobile robot. J Control Sci Eng 2020;2020:3857894.
  • [37] Digehsara AP, Chegini NS, Bagheri A, Roknsaraei PM. An improved particle swarm optimization based on the reinforcement of the population initialization phase by scrambled halt on sequence. Cogent Eng 2020;7:1737283.
  • [38] Yu H, Gao Y, Wang L, Meng J. A hybrid particle swarm optimization algorithm enhanced with nonlinear inertial weight and guassian mutation for job shop scheduling problems. Mathematics 2020;8:1355.
  • [39] Guerrero-Luis M, Valdez F, Castillo O. A Review on the Cuckoo Search Algorithm. In: Castillo O, Melin P, editors. Fuzzy logic hybrid extensions of neural and optimization algorithms: Theory and applications, studies in computational intelligence. New York: Springer; 2021. p. 113–124.
  • [40] Wu Y, Wang Z. Improved Cuckoo search algorithm for multiple odor sources localization. In the Proceedings of the 13th International Conference on Agents and Artificial Intelligence; 2021 Feb 4–6; Vienna, Austria: ICAART; 2021. p. 708–715.
  • [41] Manar A, Al-Abaji MA. Cuckoo search algorithm: Review and its applications. Tikrit J Pure Sci 2021;26:137–144.
  • [42] Pan JS, Song PC, Chu SC, Peng YJ. Improved compact Cuckoo search algorithm applied to location of drone Logistics hub. Mathematics 2020;8:333.
  • [43] Huang J, Ma Y. Bat Algorithm based on an Integration Strategy and Gaussian distribution. Math Probl Eng 2020;2020:9495281.
  • [44] Mashwani WK, Mehmood I, Bakar MA, Koçcak I. A modified Bat Algorithm for Solving large-scale bound constrained global optimization problems. Math Probl Eng 2021;2021:6636918.
  • [45] Karakonstantis I, Vlachos A. Bat algorithm applied to continuous constrained Optimization problems. J Inf Optim Sci 2021;42:57–75.
  • [46] Khursheed M, Nadeem MF, Khalil A, Sajjad IA, Raza A, Iqbal MQ, et al. Review of flower pollination algorithm: Applications and Variants. In Proceedings of the 2020 International Conference on Engineering and Emerging Technologies; 2020 Feb 22–23; Lahore, Pakistan: ICEET; 2020.
  • [47] Suwannarongsri S. Application of modified flower pollination algorithm to multiple vehicle routing problems with time constraints. WSEAS Trans Syst Control 2020;15:459–467.
  • [48] Wang K, Li X, Gao L. A multi-objective discrete flower pollination algorithm for stochastic two-sided partial disassembly line balancing problem. Comput Ind Eng 2019;130:634–649.
  • [49] Wu J, Wang Y, Burrage K, Tian Y, Lawson BA, Ding Z. An improved firefly algorithm for global continuous optimization problems. Expert Syst Appl 2020;149:113340.
  • [50] Dai W, Liang L, Zhang B. Firefly optimization algorithm for the prediction of uplift due to high-pressure jet grouting. Adv Civ Eng 2020;2020:8833784.
  • [51] Marie-Sainte SL, Alalyani N. Firefly algorithm based feature selection for Arabic text classification. J King Saud Univ Comput Inf Sci 2020;32:320–328. [52] Daweri MSA, Abdullah S, Ariffin KAZ. A migration based cuttlefish algorithm with short term memory for optimization problems. IEEE Access 2020;8:70270–70292.
  • [53] Hussien AM, Mekhamer SF, Hasanien HM. Cuttlefish Optimisation algorithm based optimal PI controller for performance enhancement of an autonomous operation of a DG system. In Proceedings of the 2nd International Conference on Smart Power and Internet Energy Systems; 2020 Sept 15–18; Bangkok, Thailand; 2020. p. 293–298.
  • [54] Eesa AS, Orman Z. A new clustering method based on the bio-inspired cuttlefish optimization algorithm. Expert Syst 2020;37:e12478.
  • [55] Liang S, Song B, Xue D. Landing route planning method for micro drones based on hybrid optimization algorithm. Biomimetic Intell Robot 2021;1:100003.
  • [56] Aracri S, Giorgio-Serchi F, Suaria G, Sayed ME, Nemitz MP, Mahon S, et al. Soft robots for ocean exploration and offshore operations: A perspective. Soft Robot 2021;8:625–639.
  • [57] Bhowmik P, Pantho MJH, Bobda C. Bio-inspired smart vision sensor: Toward a reconfigurable hardware modeling of the hierarchical processing in the brain. J Real Time Image Proc 2021;18:157–174.
  • [58] Drotman D, Jadhav S, Sharp D, Chan C, Tolley MT. Electronics-free pneumatic circuits for controlling soft-legged robots. Sci Robot 2021;6:eaay2627.
  • [59] Ivanova N. Biomimetic optics: Liquid-based optical elements imitating the eye functionality. Philos Trans A Math Phys Eng Sci 2020;378:20190442.
  • [60] Ren Z, Hu W, Dong X, Sitti M. Multi-functional soft-bodied jellyfish-like swimming. Nat Commun 2019;10:2703.
  • [61] Le LM, Ly HB, Pham BT, Le VM, Pham TA, Nguyen DH, et al. Hybrid artificial ıntelligence approaches for predicting buckling damage of steel columns under axial compression. Materials (Basel) 2019;12:1670.
  • [62] Orozco-Rosas U, Picos K, Montiel O. Hybrid path planning algorithm based on membrane pseudo-bacterial potential field for autonomous mobile robots. IEEE Access 2019;7:156787– 156803.
  • [63] Lin T, Lin H, Lin C, Chen C. Mobile robot wall-following control using a behavior-based fuzzy controller in unknown environments. Iran J Fuzzy Syst 2019;16:113–124.
  • [64] Martin-Palma JR, Kolle M. Biomimetic photonic structures for optical sensing. Opt Laser Technol 2019;109:270–277.
  • [65] Hancock M, Sood S, Higgins T, Sproul K. Biomimicry and Machine Learning in the Context of Healthcare Digitization. In: Schmorrow DD, Fidopiastis CM, editors. Augment cognition. New York: Springer; 2019. p. 273–283.
  • [66] Lamata L. Quantum machine learning and quantum biomimetics: A perspective. Mach Learn Sci Technol 2020;1:033002.
  • [67] Wanieck K, Ritzinger D, Zollfrank C, Jacobs S. Biomimetics: Teaching the tools of the trade. FEBS Open Bio 2020;10:2250–2267.
  • [68] Di Salvo S. Advances in research for biomimetic materials. Adv Mater Res 2018;1149:28–40.
  • [69] Ababsa T, Djedl N, Duthen Y. Genetic programming-based self-reconfiguration planning for metamorphic robot. Int J Autom Comput 2018;15:431–442.
  • [70] Blanco E, Uchiyama Y, Kohsaka R. Application of biomimetics to architectural and urban design: A review across scales. Sustainability 2020;12:9813.
  • [71] Ha N, Lu G. A review of recent research on bio-inspired structured and materials for energy absorption applications. Compos Part B Eng 2020;181:107496.
  • [72] Sreenivasulu R, Chaitanya G. Self adaptive penalty method coupled with metaheuristic algorithms to optimization of varying geometrical parameters in drilling for multi hole parts. Sigma J Eng Nat Sci 2022;40:855–867.
  • [73] Izci D. A novel modified arithmetic optimization algorithm for power system stabilizer design. Sigma J Eng Nat Sci 2022;40:529–541.
  • [74]Ahmad AA, Sari M. Parameter estimation to an anemia model using the Particle Swarm Optimization. Sigma J Eng Nat Sci 2019;37:1335–1347
Toplam 74 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yazılım Mimarisi
Bölüm Reviews
Yazarlar

Shivani Shivani Bu kişi benim 0000-0001-5648-265X

Satinder Bal Gupta Bu kişi benim 0000-0002-6056-1489

Yayımlanma Tarihi 12 Haziran 2024
Gönderilme Tarihi 21 Ekim 2022
Yayımlandığı Sayı Yıl 2024 Cilt: 42 Sayı: 3

Kaynak Göster

Vancouver Shivani S, Gupta SB. A systematic comparative study of popular biomimetic intelligence techniques. SIGMA. 2024;42(3):919-44.

IMPORTANT NOTE: JOURNAL SUBMISSION LINK https://eds.yildiz.edu.tr/sigma/