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Erschienen in:
Introduction to Biomimetics, Modelling and Analysis Kapitel lesen Erstes Kapitel lesen

2024 | OriginalPaper | Buchkapitel

Bio-inspired Computing and Associated Algorithms

verfasst von : Balbir Singh, Manikandan Murugaiah

Erschienen in: High Performance Computing in Biomimetics

Verlag: Springer Nature Singapore

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Abstract

This chapter explores the fascinating intersection of biology and computer science, where nature’s design principles are harnessed to solve complex computational problems. This chapter provides an overview of bio-inspired computing techniques, including genetic algorithms, neural networks, swarm intelligence, and cellular automata. It goes into the core concepts of each approach, highlighting their biological counterparts and demonstrating their applications across various domains. Furthermore, this chapter discusses the evolution of bio-inspired algorithms, emphasizing their adaptation to contemporary computing paradigms such as machine learning and artificial intelligence. It examines how these algorithms have been employed to address real-world challenges, ranging from optimization problems and pattern recognition to robotics and autonomous systems. In addition to theoretical insights, the chapter offers practical guidance on implementing bio-inspired algorithms, including algorithmic design considerations and the integration of bio-inspired approaches with traditional computing methods. It also discusses the ethical and societal implications of bio-inspired computing, touching upon topics like algorithm bias and data privacy.

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Vorheriges Kapitel High Performance Computing and Its Application in Computational Biomimetics
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Literatur
1.
Nemade, N., Rane, R.D.: A review on bio-inspired computing algorithms and application. IOSR J. Comput. Eng. (IOSR-JCE), 12–19 (2016)
2.
3.
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley (1989)
4.
Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall (1998)
5.
6.
Wolfram, S.: Cellular Automata as Simple Self-Organizing Systems. California Institute of Technology (1983)
7.
Russel, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach. Pearson (2018)
8.
Arkin, R.C.: Behavior-Based Robotics. MIT Press (1998)
9.
Pinker, S.: The Stuff of Thought: Language as a Window into Human Nature. Penguin Books (2007)
10.
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRef
11.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I.: Attention is all you need. Advances in neural information processing systems (2017)
12.
Deb, K.: Multi-objective Optimization Using Evolutionary Algorithms. Wiley (2001)
13.
Blum, C., Merkle, D.: Swarm intelligence: introduction and applications. Nat. Comput. 7(3), 267–278 (2008)
14.
Adleman, L.M.: Molecular computation of solutions to combinatorial problems. Science 266(5187), 1021–1024 (1994)CrossRef
15.
Seelig, G., Soloveichik, D., Zhang, D.Y., Winfree, E.: Enzyme-free nucleic acid logic circuits. Science 314(5805), 1585–1588 (2006)CrossRef
16.
Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y.: Deep Learning (Vol. 1). MIT Press Cambridge (2016)
17.
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press (1995)CrossRef
18.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
19.
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1985)CrossRef
20.
21.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (2012)
22.
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRef
23.
24.
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., ... & Hassabis, D.: Mastering chess and shogi by self-play with a general reinforcement learning algorithm. Nature 529(7587), 484–489 (2016)
25.
Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., ... & Langlotz, C.P.: CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning (2017). arXiv preprint arXiv:​1711.​05225
26.
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer (2015)CrossRef
27.
Yang, X.S.: Nature-Inspired Optimization Algorithms. Elsevier (2014)
28.
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press (1992)
29.
Beyer, H.G., Schwefel, H.P.: Evolution strategies–A comprehensive introduction. Nat. Comput. 1(1), 3–52 (2002)MathSciNetCrossRef
30.
31.
Back, T.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press (1996)CrossRef
32.
Deb, K., et al.: A review on multi-objective optimization in manufacturing industries. Eng. Optim. 48(6), 841–871 (2016)
33.
34.
Vasant, P., Weber, G.-W., Dieu, V.N. (Eds.): Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics; IGI Global: Hershey, PA, USA (2016)
35.
Fávero, L.P., Belfiore, P.: Data Science for Business and Decision Making. Academic Press, Cambridge, MA, USA (2018)
36.
Montoya, O.D., Molina-Cabrera, A., Gil-González, W.: A Possible Classification for Metaheuristic Optimization Algorithms in Engineering and Science. Ingeniería 27, 1 (2022)
37.
Ma, Z., Wu, G., Suganthan, P.N., Song, A., Luo, Q.: Performance assessment and exhaustive listing of 500+ nature-inspired metaheuristic algorithms. Swarm Evol. Comput. 77, 101248 (2023)
38.
Del Ser, J., Osaba, E., Molina, D., Yang, X.-S., Salcedo-Sanz, S., Camacho, D., Das, S., Suganthan, P.N., Coello Coello, C.A., Herrera, F.: Bio-inspired computation: where we stand and what’s next. Swarm Evol. Comput. 48, 220–250 (2019)
39.
Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI, USA (1975)
40.
Wilson, A.J., Pallavi, D.R., Ramachandran, M., Chinnasamy, S., Sowmiya, S.: A review on memetic algorithms and its developments. Electr. Autom. Eng. 1, 7–12 (2022)
41.
Bilal; Pant, M., Zaheer, H., Garcia-Hernandez, L., Abraham, A.: Differential evolution: a review of more than two decades of research. Eng. Appl. Artif. Intell. 90, 103479 (2020)
42.
Sivanandam, S.N., Deepa, S.N., Sivanandam, S.N., Deepa, S.N.: Genetic Algorithms; Springer, Berlin/Heidelberg, Germany (2008)
43.
Dawkins, R.: The Selfish Gene. Oxford University Press, Oxford, UK (1976)
44.
Sengupta, S., Basak, S., Peters, R.A.: Particle swarm optimization: a survey of historical and recent developments with hybridization perspectives. Mach. Learn. Knowl. Extr. 1, 157–191 (2019)
45.
Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)
46.
Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)
47.
Karaboga, D., Gorkemli, B., Ozturk, C., Karaboga, N.: A comprehensive survey: Artificial bee colony (ABC) algorithm and applications. Artif. Intell. Rev. 42, 21–57 (2014)
48.
Dorigo, M., Stützle, T.: Ant colony optimization: overview and recent advances. In: Gendreau, M., Potvin, J.-Y., (Eds.), Handbook of Metaheuristics, pp. 311–351. Springer International Publishing, Cham, Switzerland (2019)
49.
Neshat, M., Sepidnam, G., Sargolzaei, M., Toosi, A.N.: Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artif. Intell. Rev. 42, 965–997 (2014)
50.
Fister, I., Fister, I., Yang, X.-S., Brest, J.: A comprehensive review of firefly algorithms. Swarm Evol. Comput. 13, 34–46 (2013)
51.
Ranjan, R.K., Kumar, V.: A systematic review on fruit fly optimization algorithm and its applications. Artif. Intell. Rev. (2023)
52.
Yang, X.-S., Deb, S.: Cuckoo search: recent advances and applications. Neural Comput. Appl. 24, 169–174 (2014)
53.
Agarwal, T., Kumar, V.: A systematic review on bat algorithm: theoretical foundation, variants, and applications. Arch. Comput. Methods Eng. 29, 2707–2736 (2022)
54.
Selva Rani, B., Aswani Kumar, C.: A comprehensive review on bacteria foraging optimization technique. In: Dehuri, S., Jagadev, A.K., Panda, M. (Eds.) Multi-objective Swarm Intelligence: Theoretical Advances and Applications, pp. 1–25. Springer, Berlin/Heidelberg, Germany (2015)
55.
Luque-Chang, A., Cuevas, E., Fausto, F., Zaldívar, D., Pérez, M.: Social spider optimization algorithm: modifications, applications, and perspectives. Math. Probl. Eng. 2018, 6843923 (2018)
56.
Cuevas, E., Fausto, F., González, A.: Locust search algorithm applied to multi-threshold segmentation. In: Cuevas, E., Fausto, F., González, A., (Eds.) New Advancements in Swarm Algorithms: Operators and Applications, pp. 211–240. Springer International Publishing: Cham, Switzerland (2020)
57.
Ezugwu, A.E., Prayogo, D.: Symbiotic organisms search algorithm: theory, recent advances and applications. Expert Syst. Appl. 119, 184–209 (2019)
58.
Shehab, M., Abualigah, L., Al Hamad, H., Alabool, H., Alshinwan, M., Khasawneh, A.M.: Moth–flame optimization algorithm: Variants and applications. Neural Comput. Appl. 32, 9859–9884 (2020)
59.
Hashim, F.A., Houssein, E.H., Hussain, K., Mabrouk, M.S., Al-Atabany, W.: Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems. Math. Comput. Simul. 192, 84–110 (2022)
60.
Li, J., Lei, H., Alavi, A.H., Wang, G.-G.: Elephant herding optimization: variants, hybrids, and applications. Mathematics 8, 1415 (2020)
61.
Abualigah, L., Diabat, A.: A comprehensive survey of the Grasshopper optimization algorithm: results, variants, and applications. Neural Comput. Appl. 32, 15533–15556 (2020)
62.
Alabool, H.M., Alarabiat, D., Abualigah, L., Heidari, A.A.: Harris hawks optimization: a comprehensive review of recent variants and applications. Neural Comput. Appl. 33, 8939–8980 (2021)
63.
Jiang, Y., Wu, Q., Zhu, S., Zhang, L.: Orca predation algorithm: a novel bio-inspired algorithm for global optimization problems. Expert Syst. Appl. 188, 116026 (2022)
64.
Zamani, H., Nadimi-Shahraki, M.H., Gandomi, A.H.: Starling murmuration optimizer: A novel bio-inspired algorithm for global and engineering optimization. Comput. Methods Appl. Mech. Eng. 392, 114616 (2022)
65.
Dehghani, M., Trojovský, P.: Serval optimization algorithm: a new bio-inspired approach for solving optimization problems. Biomimetics 7, 204 (2022)[PubMed]
66.
Salcedo-Sanz, S.: A review on the coral reefs optimization algorithm: new development lines and current applications. Prog. Artif. Intell. 6, 1–15 (2017)
67.
Wang, G.-G., Gandomi, A.H., Alavi, A.H., Gong, D.: A comprehensive review of krill herd algorithm: Variants, hybrids and applications. Artif. Intell. Rev. 51, 119–148 (2019)
68.
Agushaka, J.O., Ezugwu, A.E., Abualigah, L.: Gazelle optimization algorithm: A novel nature-inspired metaheuristic optimizer. Neural Comput. Appl. 35, 4099–4131 (2023)
69.
Laskar, N.M., Guha, K., Chatterjee, I., Chanda, S., Baishnab, K.L., Paul, P.K.: HWPSO: a new hybrid whale-particle swarm optimization algorithm and its application in electronic design optimization problems. Appl. Intell. 49, 265–291 (2019)
70.
71.
72.
Yang, X.-S.: A new metaheuristic bat-inspired algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (Eds.) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), pp. 65–74. Springer, Berlin/Heidelberg, Germany (2010)
73.
Ahmmad, S.N.Z., Muchtar, F.: A review on applications of optimization using bat algorithm. Int. J. Adv. Trends Comput. Sci. Eng. 9, 212–219 (2020)
74.
Yang, X.-S., He, X.: Bat algorithm: literature review and applications. Int. J. Bio-Inspired Comput. 5, 141–149 (2013)
75.
Golilarz, N.A., Gao, H., Addeh, A., Pirasteh, S.: ORCA optimization algorithm: a new meta-heuristic tool for complex optimization problems. In: Proceedings of the 17th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), Chengdu, China, 18–20 December 2020; pp. 198–204
76.
Drias, H., Bendimerad, L.S., Drias, Y.: A three-phase artificial orcas algorithm for continuous and discrete problems. Int. J. Appl. Metaheuristic Comput. 13, 1–20 (2022)
77.
Cavagna, A., Cimarelli, A., Giardina, I., Parisi, G., Santagati, R., Stefanini, F., Viale, M.: Scale-free correlations in starling flocks. Proc. Natl. Acad. Sci. USA 107, 11865–11870 (2010)
78.
Talbi, E.-G.: Combining metaheuristics with mathematical programming, constraint programming and machine learning. Ann. Oper. Res. 240, 171–215 (2016)
79.
Abdel-Basset, M., Shawky, L.A.: Flower pollination algorithm: a comprehensive review. Artif. Intell. Rev. 52, 2533–2557 (2019)
80.
Yang, X.-S.: Flower pollination algorithm for global optimization. In: Durand-Lose, J., Jonoska, N. (Eds.), Unconventional Computation and Natural Computation. pp. 240–249. Springer, Berlin/Heidelberg, Germany (2012)
81.
Yang, X.-S., Karamanoglu, M., He, X.: Flower pollination algorithm: a novel approach for multiobjective optimization. Eng. Optim. 46, 1222–1237 (2014)
82.
Valenzuela, L., Valdez, F., Melin, P.: Flower pollination algorithm with fuzzy approach for solving optimization problems. In: Melin, P., Castillo, O., Kacprzyk, J. (Eds.), Nature-Inspired Design of Hybrid Intelligent Systems, pp. 357–369. Springer International Publishing, Cham, Switzerland (2017)
83.
Dasgupta, D., Yu, S., Nino, F.: Recent advances in artificial immune systems: models and applications. Appl. Soft Comput. 11, 1574–1587 (2011)
84.
Bernardino, H.S., Barbosa, H.J.C.: Artificial immune systems for optimization. In: Chiong, R. (Ed.) Nature-Inspired Algorithms for Optimisation, pp. 389–411. Springer, Berlin/Heidelberg, Germany (2009)
85.
Tang, C., Todo, Y., Ji, J., Lin, Q., Tang, Z.: Artificial immune system training algorithm for a dendritic neuron model. Knowl. Based Syst. 233, 107509 (2021)
86.
Ray, T., Liew, K.M.: Society and civilization: an optimization algorithm based on the simulation of social behavior. IEEE Trans. Evol. Comput. 7, 386–396 (2003)
87.
Husseinzadeh Kashan, A.: League championship algorithm (LCA): an algorithm for global optimization inspired by sport championships. Appl. Soft Comput. 16, 171–200 (2014)
88.
Laguna, M.: Tabu search. In: Martí, R., Pardalos, P.M., Resende, M.G.C. (Eds.), Handbook of Heuristics, pp. 741–758. Springer International Publishing, Cham, Switzerland (2018)
89.
Sadollah, A., Sayyaadi, H., Yadav, A.: A dynamic metaheuristic optimization model inspired by biological nervous systems: neural network algorithm. Appl. Soft Comput. 71, 747–782 (2018)
90.
Mousavirad, S.J., Ebrahimpour-Komleh, H.: Human mental search: a new population-based metaheuristic optimization algorithm. Appl. Intell. 47, 850–887 (2017)
91.
Bozorgi, A., Bozorg-Haddad, O., Chu, X.: Anarchic society optimization (ASO) algorithm. In: Bozorg-Haddad, O. (Ed.), Advanced Optimization by Nature-Inspired Algorithms, pp. 31–38. Springer, Singapore (2018)
92.
Abdel-Basset, M., Mohamed, R., Chakrabortty, R.K., Sallam, K., Ryan, M.J.: An efficient teaching-learning-based optimization algorithm for parameters identification of photovoltaic models: Analysis and validations. Energy Convers. Manag. 227, 113614 (2021)
93.
Askari, Q., Younas, I., Saeed, M.: Political optimizer: a novel socio-inspired meta-heuristic for global optimization. Knowl. Based Syst. 195, 105709 (2020)
94.
Ibrahim, A., Anayi, F., Packianather, M., Alomari, O.A.: New hybrid invasive weed optimization and machine learning approach for fault detection. Energies 15, 1488 (2022)
95.
Gupta, D., Sharma, P., Choudhary, K., Gupta, K., Chawla, R., Khanna, A., Albuquerque, V.H.C.D.: Artificial plant optimization algorithm to detect infected leaves using machine learning. Expert Syst. 38, e12501 (2021)
96.
Mohamed, H., Korany, R.M., Mohamed Farhat, O.H., Salah, S.A.O.: Optimal design of vertical silicon nanowires solar cell using hybrid optimization algorithm. J. Photonics Energy 8, 022502 (2017)
97.
Cruz-Duarte, J.M., Amaya, I., Ortiz-Bayliss, J.C., Conant-Pablos, S.E., Terashima-Marín, H., Shi, Y.: Hyper-Heuristics to customise metaheuristics for continuous optimisation. Swarm Evol. Comput 66, 100935 (2021)
98.
Hizarci, H., Demirel, O., Turkay, B.E.: Distribution network reconfiguration using time-varying acceleration coefficient assisted binary particle swarm optimization. Eng. Sci. Technol. Int. J. 35, 101230 (2022)
99.
100.
Metadaten
Titel
Bio-inspired Computing and Associated Algorithms
verfasst von
Balbir Singh
Manikandan Murugaiah
Copyright-Jahr
2024
Verlag
Springer Nature Singapore
Buch
High Performance Computing in Biomimetics

Print ISBN: 978-981-9710-16-4

Electronic ISBN: 978-981-9710-17-1

Copyright-Jahr: 2024

DOI
https://doi.org/10.1007/978-981-97-1017-1_3

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