Skip to main content
SpringerLink
Log in

Review and Classification of Bio-inspired Algorithms and Their Applications

  • Published:
Journal of Bionic Engineering Aims and scope Submit manuscript

Abstract

Scientists have long looked to nature and biology in order to understand and model solutions for complex real-world problems. The study of bionics bridges the functions, biological structures and functions and organizational principles found in nature with our modern technologies, numerous mathematical and metaheuristic algorithms have been developed along with the knowledge transferring process from the lifeforms to the human technologies. Output of bionics study includes not only physical products, but also various optimization computation methods that can be applied in different areas. Related algorithms can broadly be divided into four groups: evolutionary based bio-inspired algorithms, swarm intelligence-based bio-inspired algorithms, ecology-based bio-inspired algorithms and multi-objective bio-inspired algorithms. Bio-inspired algorithms such as neural network, ant colony algorithms, particle swarm optimization and others have been applied in almost every area of science, engineering and business management with a dramatic increase of number of relevant publications. This paper provides a systematic, pragmatic and comprehensive review of the latest developments in evolutionary based bio-inspired algorithms, swarm intelligence based bio-inspired algorithms, ecology based bio-inspired algorithms and multi-objective bio-inspired algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Zang H N, Zhang S J, Hapeshi K A. Review of nature-inspired algorithms. Journal of Bionic Engineering, 2010, 7, S232–S237.

    Google Scholar 

  2. Yang X S. Nature-inspired Metaheuristic Algorithms, 2nd ed., Luniver Press, Somerset, UK, 2010, 1–5.

    Google Scholar 

  3. Lindfield G, Penny J. Introduction to Nature-Inspired Optimization, Academic Press, London, United Kingdom, 2017, 1, 101–117.

    MATH  Google Scholar 

  4. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521, 436–444.

    Google Scholar 

  5. Mason K, Duggan M, Barrett E, Duggan J, Howley E. Predicting host CPU utilization in the cloud using evolutionary neural networks. Future Generation Computer Systems, 2018, 86, 162–173.

    Google Scholar 

  6. Hassan H A, Mohamed S A, Sheta W M. Scalability and communication performance of HPC on Azure Cloud. Egyptian Informatics Journal, 2016, 17, 175–182.

    Google Scholar 

  7. Abdul Khalid N E, Ariff N, Yahya S, Noor N. A review of bio-inspired algorithms as image processing techniques. Communications in Computer and Information Science, 2011, 179, 660–673.

    Google Scholar 

  8. Binitha S, Sathya S S. A survey of bio inspired optimization algorithms. International Journal of Soft Computing and Engineering, 2012, 2, 137–151.

    Google Scholar 

  9. Chizari H, Lupu E, Thomas P. Randomness of physiological signals in generation cryptographic key for secure communication between implantable medical devices inside the body and the outside world. Living in the Internet of Things: Cybersecurity of the IoT, 2018, 2018, 1–6.

    Google Scholar 

  10. Sayers W. Artificial Intelligence Techniques for Flood Risk Management in Urban Environments. PhD thsis, University of Exeter, Exeter, UK, 2015.

    Google Scholar 

  11. Bayer P, Finkel M. Evolutionary algorithms for the optimization of advective control of contaminated aquifer zones. Water Resources Research, 2004, 40, W06506.

    Google Scholar 

  12. Nicklow J, Reed P, Savić D, Dessalegne T, Harrell L, Chan-Hilton, A, Karamouz M, Minsker B, Ostfeld A, Singh A, Zechman E. State of the art for genetic algorithms and beyond in water resources planning and management. Journal of Water Resources Planning and Management, 2010, 136, 412–432.

    Google Scholar 

  13. Karaboga D. An Idea Based on Honey Bee Swarm for Numerical Optimization. Technical Report - TR06, Erciyes University, Turkey, 2005.

    Google Scholar 

  14. Zainal N, Zain A, Sharif S. Overview of artificial fish swarm Algorithm and its applications in industrial problems. Applied Mechanics and Materials, 2015, 815, 253–257.

    Google Scholar 

  15. Zhou Y Q, Chen H, Zhou G. Invasive weed optimization algorithm for optimization no-idle flow shop scheduling problem. Neurocomputing, 2014, 137, 285–292.

    Google Scholar 

  16. Simon D. Biogeography-based optimization. IEEE Transactions on Evolutionary Computation, 2008, 12, 702–713.

    Google Scholar 

  17. Sayers W, Savic D, Kapelan Z. Performance of LEMMO with artificial neural networks for water systems optimization. Urban Water Journal, 2019, 16, 21–32.

    Google Scholar 

  18. Coello C A C. Twenty years of evolutionary multiobjective optimization: A historical overview of the field. IEEE Computational Intelligence Magazine, 2005, 1, 28–36.

    Google Scholar 

  19. Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 2002, 6, 182–197.

    Google Scholar 

  20. Bishop C M. Neural Networks for Pattern Recognition, Oxford University Press, USA, 1995, 77–161.

    Google Scholar 

  21. Parker D B. Learning Logic, Technical Report TR-47, Cambridge, UK, 1985.

  22. Werbos P. Beyond Regression: New Tools for Predictions and Analysis in the Behavioural Sciences, PhD thesis, Harvard University, USA, 1974.

    Google Scholar 

  23. Cybenko G. Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals and Systems, 1989, 2, 303–314.

    MathSciNet  MATH  Google Scholar 

  24. Hornik K, Stinchcombe M, White H. Multilayer feedfordward networks are universal approximators. Neural Networks, 1989, 2, 359–366.

    MATH  Google Scholar 

  25. Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado G S, Davis A, Dean J, Devin M, Ghemawat S, Goodfellow I, Harp A, Irving G, Isard M, Jia Y, Jozefowicz R, Kaiser L, Kudlur M, Levenberg J, Mané D, Monga R, Moore S, Murray D, Olah C, Schuster M, Shlens J, Steiner B, Sutskever I, Talwar K, Tucker P, VanhouckeV, Vasudevan V, Viégas F, Vinyals O, Warden P, Wattenberg M, Wicke M, Yu Y, Zheng X. TensorFlow: Large-scale machine learning on heterogeneous systems, Computer Science, 2015, Preprint at: https//arXiv:1603.04467.

  26. McCulloch W, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biology, 1943, 5, 115–133.

    MathSciNet  MATH  Google Scholar 

  27. Rosenblatt F. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 1958, 65, 386–408.

    Google Scholar 

  28. Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 2012, 1, 1097–1105.

    Google Scholar 

  29. Elsheikh A H, Sharshir S W, Elaziz M A, Kabeel A E, Guilan W, Haiou Z. Modeling of solar energy systems using artificial neural network: A comprehensive review. Solar Energy, 2019, 180, 622–639.

    Google Scholar 

  30. Bagheri M, Mirbagheri S A, Ehteshami M, Bagheri Z. Modeling of a sequencing batch reactor treating municipal wastewater using multi-layer perceptron and radial basis function artificial neural networks. Process Safety and Environmental Protection, 2015, 93, 111–123.

    Google Scholar 

  31. Yusoff N I M, Alhamali D I, Ibrahim A N H, Rosyidi S A P, Hassan N A. Engineering characteristics of nanosilica/polymer-modified bitumen and predicting their rheological properties using multilayer perceptron neural network model. Construction and Building Materials, 2019, 204, 781–799.

    Google Scholar 

  32. Whittington J C R, Bogacz R. Theories of error back-propagation in the brain. Trends in Cognitive Sciences, 2019, 23, 235–250.

    Google Scholar 

  33. Arulkumaran K, Cully A, Togelius J. AlphaStar: An evolutionary computation perspective. Proceedings of the Genetic and Evolutionary Computation Conference, Prague, Czech, 2019, 314–315.

  34. Tian Y, Zhang K, Li J, Lin X, Yang B. LSTM-based traffic flow prediction with missing data. Neurocomputing, 2018, 318, 297–305.

    Google Scholar 

  35. Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Liu T, Wang X, Wang G, Cai J, Chen T. Recent advances in convolutional neural networks. Pattern Recognition, 2018, 77, 354–377.

    Google Scholar 

  36. Behzadian K, Kapelan Z, Savić D A, Ardeshir A. Stochastic sampling design using multiobjective genetic algorithm and adaptive neural networks. Environmental Modelling & Software, 2009, 24, 530–541.

    Google Scholar 

  37. Juhn Y, Liu H. Artificial intelligence approaches using natural language processing to advance EHR-based clinical research. Journal of Allergy and Clinical Immunology, 2020, 145, 463–469.

    Google Scholar 

  38. Trappey A J C, Trappey C V, Wu J L, Wang J W C. Intelligent compilation of patent summaries using machine learning and natural language processing techniques. Advanced Engineering Informatics, 2020, 43, 101027.

    Google Scholar 

  39. Dmitriev E A, Myasnikov V V. Possibility estimation of 3D scene reconstruction from multiple images. Proceedings of the International Conference on Information Technology and Nanotechnology, Samara, Russia, 2019, 293–296.

  40. Gkioxari G, Malik J, Johnson J. Mesh R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Seoul, Korea, 2019, 9785–9795.

  41. Holland J H. Adaptation in Natural and Artificial Systems, University of Michigan Press, 1975, 1–232.

  42. De Jong K A. An Analysis of the Behaviour of a Class of Genetic Adaptive Systems. PhD thesis, University of Michigan, USA, 1975.

    Google Scholar 

  43. Koza J R. Genetic Programming. MIT Press, Massachusetts, USA, 1992, 73–191.

    Google Scholar 

  44. Paul P V, Moganarangan N, Kumar S S, Raju R, Vengattaraman T, Dhavachelvan P. Performance analyses over population seeding techniques of the permutation-coded genetic algorithm: An empirical study based on traveling salesman problems. Applied Soft Computing, 2015, 32, 383–402.

    Google Scholar 

  45. Islam M L, Shatabda S, Rashid M A, Khan M G M, Rahman M S. Protein structure prediction from inaccurate and sparse NMR data using an enhanced genetic algorithm. Computational Biology and Chemistry, 2019, 79, 6–15.

    Google Scholar 

  46. Akopov A S, Beklaryan L A, Thakur M, Verma B D. Parallel multi-agent real-coded genetic algorithm for large-scale black-box single-objective optimization. Knowledge-Based Systems, 2019, 174, 103–122.

    Google Scholar 

  47. Luo J, Fujimura S, El Baz D, Plazolles B. GPU based parallel genetic algorithm for solving an energy efficient dynamic flexible flow shop scheduling problem. Journal of Parallel and Distributed Computing, 2019, 133, 244–257.

    Google Scholar 

  48. Beyer H G, Schwefel H P. Evolution strategies - A comprehensive introduction. Natural Computing, 2002, 1, 3–52.

    MathSciNet  MATH  Google Scholar 

  49. Rechenberg I. Evolutionsstrategie: Optimierung Technischer Systeme Nach Prinsipien der Biologischen Evolution, Frommann-Holzboog, Stuttgart, Germany, 1973, 1–170. (in German)

    Google Scholar 

  50. Rechenberg I. Cybernetic solution path of an experimental problem. Royal Aircraft Establishment Translation 1122, Farnborough, 1965.

  51. Schwefel H P. Evolutionsstrategie und Numerische Optimierung. PhD thesis, Technische Universität Berlin, Berlin, Germany, 1975. (in German)

    Google Scholar 

  52. Schwefel H P. Kybernetische Evolution als Strategie der Exprimentellen Forschung in der Strömungstechnik. Dissertation, TechnischeUnversitat Berlin, Berlin, Germany, 1965. (in German)

    Google Scholar 

  53. Klockgether J, Schwefel H P. Two-phase nozzle and hollow core jet experiments. Proceedings of the 11 th Symposium on Engineering Aspects of Magnetohydrodynamics, Pasadena, Californa, 1970, 141–148.

  54. Schwefel HP. Projekt MHD-Staustrahlrohr: Experimentelle Optimierung einer Zweiphasendüse, Teil I, 1968. (in German)

  55. Engelbrecht A P. Computational Intelligence, An Introduction. Wiley, 2007, 213–235.

  56. Bäck T, Hoffmeister F, Schwefel H P. A survey of evolution strategies. Proceedings of the Fourth International Conference on Genetic Algorithms, San Diego, USA, 1991, 2–9.

  57. Schwefel H P. Numerical Optimization of Computer Models. Wiley, Chichester, UK, 1981, 1–330.

    MATH  Google Scholar 

  58. Schwefel H P. Numerische Optimierung von Computer-Modellen mittels der Evolutionsstrategie. Birkhaeuser, Basel, Switzerland, 1977, 123–176.

    MATH  Google Scholar 

  59. Lin Y, Yang Q, Guan G. Scantling optimization of FPSO internal turret area structure using RBF model and evolutionary strategy. Ocean Engineering, 2019, 191, 106562.

    Google Scholar 

  60. Liu K, Zhang J. Nonlinear process modelling using echo state networks optimised by covariance matrix adaption evolutionary strategy. Computers & Chemical Engineering, 2020, 135, 106730.

    Google Scholar 

  61. Liu G, Zhao L, Yang F, Bian J, Qin T, Yu N, Liu T Y. Trust Region Evolution Strategies. Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, Honolulu, USA, 2019, 4252–4359.

  62. Salimans T, Ho J, Chen X, Sidor S, Sutskever I. Evolution Strategies as a Scalable Alternative to Reinforcement Learning. Preprint at https://arxiv.org/abs/1703.03864, 2017.

  63. Dorigo M, Stützle T. Ant colony optimization: Overview and recent advances. Handbook of Metaheuristics, 2010, 146, 227–263.

    Google Scholar 

  64. Cordon O, Viana I F de, Herrera F, Moreno L. A New ACO Model Integrating Evolutionary Computation Concepts: The Best-Worst Ant System, Proceedings of the 2nd International Workshop on Ant Algorithms, Brussels, Belgium, 2000, 22–29.

  65. Dorigo M, Stutzle T. Ant Colony Optimization. MIT Press, Massachusetts, USA, 2004, 1–319.

  66. Dorigo M. Optimization, Learning and Natural Algorithms. PhD thesis, Politecno di Milano, Milan, Italy, 1992.

    Google Scholar 

  67. Dorigo M, Maniezzo V, Colorni A. Ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Man, Systems and Cybernetics - Part B, 1997, 26, 29–41.

    Google Scholar 

  68. Mohan B C, Baskaran R. A survey: Ant colony optimization based recent research and implementation on several engineering domain. Expert Systems with Applications, 2012, 39, 4618–4627.

    Google Scholar 

  69. López-Ibáñez M, Stutzle T. Automatic configuration of multi-objective ant colony optimization algorithms. Lecture Notes in Computer Science, 2010, 6234, 95–106.

    Google Scholar 

  70. Pham D, Ghanbarzadeh A, Koç E, Otri S, Rahim S, Zaidi M. The Bees Algorithm Technical Note, Manufacturing Engineering Centre, Cardiff University, UK, 2005, 1–57.

    Google Scholar 

  71. Pham D T, Castellani M. A comparative study of the bees algorithm as a tool for function optimization. Cogent Engineering, 2015, 2, 1–28.

    Google Scholar 

  72. Khan I, Maiti M K. A swap sequence based artificial bee colony algorithm for traveling salesman problem. Swarm and Evolutionary Computation, 2019, 44, 428–438.

    Google Scholar 

  73. Ning J, Zhang C, Zhang B. A novel artificial bee colony algorithm for the QoS based multicast route optimization problem. Optik, 2016, 127, 2771–2779.

    Google Scholar 

  74. Sağ T, Çunkaş, M. Color image segmentation based on multiobjective artificial bee colony optimization. Applied Soft Computing, 2015, 34, 389–401.

    Google Scholar 

  75. Kumar A, Kumar D, Jarial S K. A review on artificial bee colony algorithms and their applications to data clustering. Cybernetics and Information Technologies, 2017, 17, 3–28.

    MathSciNet  Google Scholar 

  76. Zou W, Zhu Y, Chen H, Sui X. A Clustering approach using cooperative artificial bee colony algorithm. Discrete Dynamics in Nature and Society, 2010, 2010, 1–17.

    MATH  Google Scholar 

  77. Li X L, Shao Z J, Qian J X. An optimizing method based on autonomous animats: Fish-swarm algorithm. Systems Engineering - Theory & Practice, 2002, 22, 32–38.

    Google Scholar 

  78. Yu L, Li C. A global artificial fish swarm algorithm for structural damage detection. Advances in Structural Engineering, 2014, 17, 331–346.

    Google Scholar 

  79. Neshat M, Adeli A, Sepidnam G, Sargolzaei M, Toosi A. A Review of artificial fish swarm optimization methods and applications. International Journal on Smart Sensing and Intelligent Systems, 2012, 5, 107–148.

    Google Scholar 

  80. He Q, Hu X T, Ren H, Zhang H Q. A novel artificial fish swarm algorithm for solving large-scale reliability - Redundancy application problem. ISA Transactions, 2015, 59, 105–113.

    Google Scholar 

  81. Basak A, Maity D, Das S. A differential invasive weed optimization algorithm for improved global numerical optimization. Applied Mathematics and Computation, 2013, 219, 6645–6668.

    MathSciNet  MATH  Google Scholar 

  82. Rani D S, Subrahmanyam N, Sydulu M. Multi-objective invasive weed optimization - An application to optimal network reconfiguration in radial distribution systems. International Journal of Electrical Power & Energy Systems, 2015, 73, 932–942.

    Google Scholar 

  83. Barisal A K, Prusty R C. Large scale economic dispatch of power systems using oppositional invasive weed optimization. Applied Soft Computing, 2015, 29, 122–137.

    Google Scholar 

  84. Ghasemi M, Ghavidel S, Akbari E, Vahed A A. Solving non-linear, non-smooth and non-convex optimal power flow problems using chaotic invasive weed optimization algorithms based on chaos. Energy, 2014, 73, 340–353.

    Google Scholar 

  85. Zhao Y W, Leng L L, Qian Z Y, Wang W L. A discrete hybrid ivasive weed optimization algorithm for the capacitated vehicle routing problem. Procedia Computer Science, 2016, 91, 978–987.

    Google Scholar 

  86. Velmurugan T, Khara S, Nandakumar S, Saravanan B. Seamless vertical handoff using Invasive Weed Optimization (IWO) algorithm for heterogeneous wireless networks. Ain Shams Engineering Journal, 2016, 7, 101–111.

    Google Scholar 

  87. Goudos S K, Plets D, Liu N, Martens L, Joseph W. A multi-objective approach to indoor wireless heterogeneous networks planning based on biogeography-based optimization. Computer Networks, 2015, 91, 564–576.

    Google Scholar 

  88. Lin J. A hybrid biogeography-based optimization for the fuzzy flexible job-shop scheduling problem. Knowledge-Based Systems, 2015, 78, 59–74.

    Google Scholar 

  89. Kim S S, Byeon J H, Yu H, Liu H. Biogeography-based optimization for optimal job scheduling in cloud computing. Applied Mathematics and Computation, 2014, 247, 266–280.

    MathSciNet  MATH  Google Scholar 

  90. Rajasomashekar S, Aravindhababu P. Biogeography based optimization technique for best compromise solution of economic emission dispatch. Swarm and Evolutionary Computation, 2012, 7, 47–57.

    Google Scholar 

  91. Wang L, Xu Y. An effective hybrid biogeography-based optimization algorithm for parameter estimation of chaotic systems. Expert Systems with Applications, 2011, 38, 15103–15109.

    MathSciNet  Google Scholar 

  92. Niu Q, Zhang L T, Li K. A biogeography-based optimization algorithm with mutation strategies for model parameter estimation of solar and fuel cells. Energy Conversion and Management, 2014, 86, 1173–1185.

    Google Scholar 

  93. Jourdan L, Corne D, Savic D, Walters G. Evolutionary Multi-Criterion Optimization, Springer, Berlin, Germany, 2005, 841–855.

    Google Scholar 

  94. Jourdan L, Corne D, Savić D, Walters G. Hybridising rule induction and multi-objective evolutionary search for optimising water distribution systems. Proceedings of the Fourth International Conference on Hybrid Intelligent Systems, Kitakyushu, Japan, 2004, 434, 439.

  95. Woodward M, Kapelan Z, Gouldby B. Adaptive flood risk management under climate change uncertainty using real options and optimization. Risk Analysis, 2013, 1, 75–92.

    Google Scholar 

  96. Woodward M, Gouldby B, Kapelan Z, Hames, D. Multiobjective optimization for improved management of flood risk. Journal of Water Resources Planning and Management (ASCE), 2013, 2, 201–215.

    Google Scholar 

  97. di Pierro F, Khu ST, Savić D, Berardi L. Efficient multi-objective optimal design of water distribution networks on a budget of simulations using hybrid algorithms. Environmental Modelling & Software, 2009, 24, 202–213.

    Google Scholar 

  98. Woodward M. The use of real options and multi-objective optimization in flood risk management. PhD thesis, University of Exeter, Exeter, UK, 2012.

    Google Scholar 

  99. Pareto V. Cours D’Economie Politique Vol. I & II. Lausanne, Swizerland, 1896.

  100. Deb K, Jain H. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints. IEEE Transactions on Evolutionary Computation, 2014, 18, 577–601.

    Google Scholar 

  101. Doerner K J, Gutjahr W, Hartl R, Strauss C, Stummer C. Ant colony optimization in multiobjective portfolio selection. Proceedings of the 4th Metaheuristics International Conference, Porto, Portugal, 2001, 243–248.

  102. Li J, Zhang Z Q, Zhang L L, Shao K J. Multi-objective ant colony optimization algorithm based on discrete variables. IOP Conference Series: Earth and Environmental Science, 2018, 189, 042031.

    Google Scholar 

  103. Oliveira S M, Hussin M S, Stuetzle T, Roli A, Dorigo M. A detailed analysis of the population-based ant colony optimization algorithm for the TSP and the QAP. Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation, Dublin, Ireland, 2011, 13–14.

  104. Zaharia M, Xin R S, Wendell P, Das T, Armbrust M, Dave A, Meng X, Rosen J, Venkataraman S, Franklin M J, Ghodsi A, Gonzalez J, Shenker S, Stoica I. Apache spark: A unified engine for big data processing. Communications of the ACM, 2016, 59, 56–65.

    Google Scholar 

  105. Dong G F, Fu X L, Li H H, Xie P F. Cooperative ant colony-genetic algorithm based on spark. Computers & Electrical Engineering, 2017, 60, 66–75.

    Google Scholar 

  106. Poole D J, Allen C B. Constrained niching using differential evolution. Swarm and Evolutionary Computation, 2019, 44, 74–100.

    Google Scholar 

  107. Tian M, Gao X. Differential evolution with neighborhood-based adaptive evolution mechanism for numerical optimization. Information Sciences, 2019, 478, 422–448.

    Google Scholar 

  108. Kong X, Chen Y L, Xie W, Wu X Y. A novel paddy field algorithm based on pattern search method. Proceedings of the IEEE International Conference on Information and Automation, Chengdu, China, 2012, 686–690.

  109. Askarzadeh A. Bird mating optimizer: An optimization algorithm inspired by bird mating strategies. Communications in Nonlinear Science and Numerical Simulation, 2014, 19, 1213–1228.

    MathSciNet  MATH  Google Scholar 

  110. Alijla B O, Wong L P, Lim C P, Khader A T, Al-Betar M A. A modified intelligent water drops algorithm and its application to optimization problems. Expert Systems with Applications, 2014, 41, 6555–6569.

    Google Scholar 

  111. Niu S H, Ong S K, Nee A Y C. An improved intelligent water drops algorithm for solving multi-objective job shop scheduling. Engineering Applications of Artificial Intelligence, 2013, 26, 2431–2442.

    Google Scholar 

  112. Patle B K, Pandey A, Jagadeesh A, Parhi D R. Path planning in uncertain environment by using firefly algorithm. Defence Technology, 2018, 14, 691–701.

    Google Scholar 

  113. Tighzert L, Fonlupt C, Mendil B. A set of new compact firefly algorithms. Swarm and Evolutionary Computation, 2018, 40, 92–115.

    Google Scholar 

  114. Fernandes D A B, Freire M M, Fazendeiro P A P, Inácio P R M. Applications of artificial immune systems to computer security: A survey. Journal of Information Security and Applications, 2017, 35, 138–159.

    Google Scholar 

  115. Ming L, Zhao J S. Feature selection for chemical process fault diagnosis by artificial immune systems. Chinese Journal of Chemical Engineering, 2018, 26, 1599–1604.

    Google Scholar 

  116. He S, Wu Q H, Saunders J. Group search optimizer: An optimization algorithm inspired by animal searching behavior. IEEE Transactions on Evolutionary Computation, 2009, 13, 973–990.

    Google Scholar 

  117. Kang Q, Lan T, Yan Y, Wang L, Wu Q D. Group search optimizer based optimal location and capacity of distributed generations. Neurocomputing, 2012, 78, 55–63.

    Google Scholar 

  118. Dash R, Dash R, Rautray R. An evolutionary framework based microarray gene selection and classification approach using binary shuffled frog leaping algorithm. Journal of King Saud University - Computer and Information Sciences, 2019. (in press)

  119. Eusuff M, Lansey K, Pasha F. Shuffled frog-leaping algorithm: A memetic meta-heuristic for discrete optimization. Engineering Optimization, 2006, 38, 129–154.

    MathSciNet  Google Scholar 

  120. Lin A P, Sun W, Yu H S, Wu G H, Tang H W. Adaptive comprehensive learning particle swarm optimization with cooperative archive. Applied Soft Computing, 2019, 77, 533–546.

    Google Scholar 

  121. Lin A P, Sun W, Yu H S, Wu G H, Tang H W. Global genetic learning particle swarm optimization with diversity enhancement by ring topology. Swarm and Evolutionary Computation, 2019, 44, 571–583.

    Google Scholar 

  122. Chen H N, Zhu Y L. Optimization based on symbiotic multi-species coevolution. Applied Mathematics and Computation, 2008, 205, 47–60.

    MathSciNet  MATH  Google Scholar 

  123. Zitzler E, Laumanns M, Thiele L. Proceedings of the Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, Athens, Greece, 2002, 95–100.

  124. Gharari R, Poursalehi N, Abbasi M, Aghaie M. Implementation of Strength Pareto Evolutionary algorithm II in the multiobjective burnable poison placement optimization of KWU pressurized water reactor. Nuclear Engineering and Technology, 2016, 48, 1126–1139.

    Google Scholar 

  125. Schaffer J D. Some Experiments in Machine Learning Using Vector Evaluated Genetic Algorithms. PhD thesis, Vanderbilt University, Nashville, USA, 1984.

    Google Scholar 

  126. Schaffer J D. Multiple objective optimization with vector evaluated genetic algorithms. Proceedings of the 1st International Conference on Genetic Algorithms, 1985, 93–100.

  127. Knowles J D, Corne D W. Approximating the nondominated front using the pareto archived evolution strategy. Evolutionary Computation, 2000, 8, 149–172.

    Google Scholar 

  128. Knowles J, Corne D. Properties of an adaptive archiving algorithm for storing nondominated vectors. IEEE Transactions on Evolutionary Computation, 2003, 7, 100–116.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Management, Jilin University, Changchun, 130022, China

    Xumei Fan

  2. School of Computing and Technology, The University of Gloucestershire, Cheltenham, GL50 2RH, UK

    William Sayers, Shujun Zhang & Hassan Chizari

  3. Key Laboratory of Bionics Engineering of Ministry of Education, Jilin University, Changchun, 130022, China

    Zhiwu Han & Luquan Ren

Authors
  1. Xumei Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. William Sayers
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shujun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhiwu Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Luquan Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hassan Chizari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Xumei Fan or Shujun Zhang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fan, X., Sayers, W., Zhang, S. et al. Review and Classification of Bio-inspired Algorithms and Their Applications. J Bionic Eng 17, 611–631 (2020). https://doi.org/10.1007/s42235-020-0049-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42235-020-0049-9

Keywords

Search

Navigation