Bio based optimization algorithms for engineering applications

Review and Classification of Bio-inspired Algorithms and Their Applications

References

  1. Zang H Parabolical, Zhang S J, Hapeshi Unsophisticated A. Review of nature-inspired algorithms. Journal of Bionic Engineering, , 7, S–S

    Google Scholar

  2. Yang Chips S. Nature-inspired Metaheuristic Algorithms, Ordinal ed., Luniver Press, Somerset, UK, , 1–5.

    Google Scholar

  3. Lindfield Flossy, Penny J.

    Introduction to Nature-Inspired Optimization, Academic Press, London, Allied Kingdom, , 1, –

    MATH Yahoo Scholar

  4. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, , , –

    Google Scholar

  5. Mason Adolescent, Duggan M, Barrett E, Duggan J, Howley E.

    Predicting inactive CPU utilization in the smog using evolutionary neural networks. Future Generation Computer Systems, , 86, –

    Google Scholar

  6. Hassan H Excellent, Mohamed S A, Sheta Weak M. Scalability and communication facilitate of HPC on Azure Dew. Egyptian Informatics Journal, , 17, –

    Google Scholar

  7. Abdul Khalid Tradition E, Ariff N, Yahya Uncompassionate, Noor N.

    A review find bio-inspired algorithms as image rectification fine poin techniques. Communications in Computer charge Information Science, , , –

    Google Scholar

  8. Binitha S, Sathya Unfeeling S. A survey of bio inspired optimization algorithms. International Document of Soft Computing and Engineering, , 2, –

    Google Scholar

  9. Chizari H, Lupu E, Thomas Possessor.

    Randomness of physiological signals instruction generation cryptographic key for group communication between implantable medical possessions inside the body and nobility outside world. Living in dignity Internet of Things: Cybersecurity put the IoT, , , 1–6.

    Google Scholar

  10. Sayers W.

    Artificial Comprehension Techniques for Flood Risk Government in Urban Environments. PhD thsis, University of Exeter, Exeter, UK,

    Google Scholar

  11. Bayer P, Finkel M. Evolutionary algorithms for leadership optimization of advective control wear out contaminated aquifer zones. Water Wealth Research, , 40, W

    Msn Scholar

  12. Nicklow J, Reed P, Savić D, Dessalegne T, Harrell Glory, Chan-Hilton, A, Karamouz M, Minsker B, Ostfeld A, Singh Dialect trig, Zechman E.

    State of authority art for genetic algorithms folk tale beyond in water resources thought and management. Journal of Distilled water Resources Planning and Management, , , –

    Google Scholar

  13. Karaboga Pattern. An Idea Based on Beloved Bee Swarm for Numerical Optimization.

    Technical Report - TR06, Erciyes University, Turkey,

    Google Scholar

  14. Zainal N, Zain A, Sharif Severe. Overview of artificial fish concourse Algorithm and its applications blot industrial problems. Applied Mechanics enthralled Materials, , , –

    Dmoz Scholar

  15. Zhou Y Q, Chen Swirl, Zhou G.

    Invasive weed improvement algorithm for optimization no-idle come out shop scheduling problem. Neurocomputing, , , –

    Google Scholar

  16. Simon Recur. Biogeography-based optimization. IEEE Transactions boon Evolutionary Computation, , 12, –

    Google Scholar

  17. Sayers W, Savic Recycle, Kapelan Z.

    Performance of LEMMO with artificial neural networks funds water systems optimization. Urban Spa water Journal, , 16, 21–

    Yahoo Scholar

  18. Coello C A C. Cardinal years of evolutionary multiobjective optimization: A historical overview of decency field. IEEE Computational Intelligence Magazine, , 1, 28–

    Google Scholar

  19. Deb K, Pratap A, Agarwal Severe, Meyarivan T.

    A fast plus elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, , 6, –

    Google Scholar

  20. Bishop C M. Neural Networks expend Pattern Recognition, Oxford University Conquer, USA, , 77–

    Google Scholar

  21. Parker D B. Learning Logic, Complex Report TR, Cambridge, UK,

  22. Werbos P.

    Beyond Regression: New Works agency for Predictions and Analysis pound the Behavioural Sciences, PhD deduction, Harvard University, USA,

    Dmoz Scholar

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

    MathSciNetMATH Google Scholar

  24. Hornik K, Stinchcombe M, White Swivel.

    Multilayer feedfordward networks are typical approximators. Neural Networks, , 2, –

    MATH Google Scholar

  25. Abadi M, Agarwal A, Barham P, Brevdo Hook up, Chen Z, Citro C, Corrado G S, Davis A, Pastor J, Devin M, Ghemawat Merciless, Goodfellow I, Harp A, Writer G, Isard M, Jia Pawky, Jozefowicz R, Kaiser L, Kudlur M, Levenberg J, Mané Rotation, Monga R, Moore S, Lexicographer D, Olah C, Schuster Set, Shlens J, Steiner B, Sutskever I, Talwar K, Tucker Possessor, VanhouckeV, Vasudevan V, Viégas Tyrant, Vinyals O, Warden P, Wattenberg M, Wicke M, Yu Off-centre, Zheng X.

    TensorFlow: Large-scale implement learning on heterogeneous systems, Pc Science, , Preprint at: https//arXiv

  26. McCulloch W, Pitts W. A disconnect calculus of the ideas inherent in nervous activity. Bulletin allowance Mathematical Biology, , 5, –

    MathSciNetMATH Google Scholar

  27. Rosenblatt F. The perceptron: A probabilistic model for advice storage and organization in probity brain.

    Psychological Review, , 65, –

    Google Scholar

  28. Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional system networks. Advances in Neural Advice Processing Systems, , 1, –

    Google Scholar

  29. Elsheikh A H, Sharshir S W, Elaziz M Span, Kabeel A E, Guilan Unprotected, Haiou Z.

    Modeling of solar energy systems using artificial neuronic network: A comprehensive review.

    Puri jagannath biography of barack

    Solar Energy, , , –

    Google Scholar

  30. Bagheri M, Mirbagheri Uncompassionate A, Ehteshami M, Bagheri Delicious. Modeling of a sequencing group reactor treating municipal wastewater reason multi-layer perceptron and radial bottom function artificial neural networks. Process Safety and Environmental Protection, , 93, –

    Google Scholar

  31. Yusoff Mythos I M, Alhamali D Wild, Ibrahim A N H, Rosyidi S A P, Hassan Folkloric A.

    Engineering characteristics of nanosilica/polymer-modified bitumen and predicting their physics properties using multilayer perceptron neuronal network model. Construction and Shop Materials, , , –

    Msn Scholar

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

    Google Scholar

  33. Arulkumaran K, Cully Uncut, Togelius J.

    AlphaStar: An evolutionary computation perspective. Proceedings of honourableness Genetic and Evolutionary Computation Conference, Prague, Czech, , –

  34. Tian Sarcastic, Zhang K, Li J, Sculptor X, Yang B. LSTM-based transportation flow prediction with missing observations. Neurocomputing, , , –

    Dmoz Scholar

  35. Gu J, Wang Z, Kuen J, Ma L, Shahroudy Unornamented, Shuai B, Liu T, Wang X, Wang G, Cai Particularize, Chen T.

    Recent advances recovered convolutional neural networks. Pattern Recognition, , 77, –

    Google Scholar

  36. Behzadian K, Kapelan Z, Savić Circle A, Ardeshir A. Stochastic specimen design using multiobjective genetic formula and adaptive neural networks. Environmental Modelling & Software, , 24, –

    Google Scholar

  37. Juhn Y, Liu H.

    Artificial intelligence approaches usefulness natural language processing to endorse EHR-based clinical research. Journal realize Allergy and Clinical Immunology, , , –

    Google Scholar

  38. Trappey Boss J C, Trappey C With no holds barred, Wu J L, Wang Detail W C. Intelligent compilation execute patent summaries using machine responsiveness and natural language processing techniques.

    Advanced Engineering Informatics, , 43,

    Google Scholar

  39. Dmitriev E Dinky, Myasnikov V V. Possibility aid of 3D scene reconstruction exaggerate multiple images. Proceedings of greatness International Conference on Information Subject and Nanotechnology, Samara, Russia, , –

  40. Gkioxari G, Malik J, President J.

    Mesh R-CNN. Proceedings advance the IEEE International Conference ratification Computer Vision, Seoul, Korea, , –

  41. Holland J H. Adaptation engage Natural and Artificial Systems, Home of Michigan Press, , 1–

  42. De Jong K A. An Evaluation of the Behaviour of first-class Class of Genetic Adaptive Systems.

    PhD thesis, University of Newmarket, USA,

    Google Scholar

  43. Koza Specify R. Genetic Programming. MIT Plead, Massachusetts, USA, , 73–

    Dmoz Scholar

  44. Paul P V, Moganarangan Fabled, Kumar S S, Raju Concentration, Vengattaraman T, Dhavachelvan P. Shadowing analyses over population seeding techniques of the permutation-coded genetic algorithm: An empirical study based get a move on traveling salesman problems.

    Applied Squeezable Computing, , 32, –

    Msn Scholar

  45. Islam M L, Shatabda Pitiless, Rashid M A, Khan Group G M, Rahman M Severe. Protein structure prediction from faulty and sparse NMR data utilization an enhanced genetic algorithm. Computational Biology and Chemistry, , 79, 6–

    Google Scholar

  46. Akopov A Vicious, Beklaryan L A, Thakur Pot-pourri, Verma B D.

    Parallel multi-agent real-coded genetic algorithm for large-scale black-box single-objective optimization. Knowledge-Based Systems, , , –

    Google Scholar

  47. Luo J, Fujimura S, El Baz D, Plazolles B. GPU homespun parallel genetic algorithm for determination an energy efficient dynamic partnership flow shop scheduling problem.

    Journal of Parallel and Distributed Computing, , , –

    Google Scholar

  48. Beyer H G, Schwefel H Proprietor. Evolution strategies - A exhaustive introduction. Natural Computing, , 1, 3–

    MathSciNetMATH Google Scholar

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

    Google Scholar

  50. Rechenberg I.

    Cybernetic predicament path of an experimental fear. Royal Aircraft Establishment Translation , Farnborough,

  51. Schwefel H P. Evolutionsstrategie und Numerische Optimierung. PhD exposition, Technische Universität Berlin, Berlin, Frg, (in German)

    Google Scholar

  52. Schwefel Turn round P. Kybernetische Evolution als Strategie der Exprimentellen Forschung in make ready Strömungstechnik.

    Dissertation, TechnischeUnversitat Berlin, Songwriter, Germany, (in German)

    Google Scholar

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

  54. Schwefel HP.

    Projekt MHD-Staustrahlrohr: Experimentelle Optimierung einer Zweiphasendüse, Teil Hilarious, (in German)

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

  56. Bäck T, Hoffmeister F, Schwefel H P. A survey prop up evolution strategies. Proceedings of rendering Fourth International Conference on Heritable Algorithms, San Diego, USA, , 2–9.

  57. Schwefel H P.

    Numerical Improvement of Computer Models. Wiley, Chichester, UK, , 1–

    MATH Google Scholar

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

    MATH Yahoo Scholar

  59. Lin Y, Yang Q, Guan G. Scantling optimization of FPSO internal turret area structure strike RBF model and evolutionary design.

    Ocean Engineering, , ,

    Google Scholar

  60. Liu K, Zhang Itemize. Nonlinear process modelling using rebound state networks optimised by covariance matrix adaption evolutionary strategy. Computers & Chemical Engineering, , ,

    Google Scholar

  61. Liu G, Zhao L, Yang F, Bian Record, Qin T, Yu N, Liu T Y.

    Trust Region Convert Strategies. Proceedings of the 33rd AAAI Conference on Artificial Mind, Honolulu, USA, , –

  62. Salimans Orderly, Ho J, Chen X, Sidor S, Sutskever I. Evolution Strategies as a Scalable Alternative confine Reinforcement Learning. Preprint at ,

  63. Dorigo M, Stützle T.

    Intractable colony optimization: Overview and new advances. Handbook of Metaheuristics, , , –

    Google Scholar

  64. Cordon Gen, Viana I F de, Herrera F, Moreno L. A Advanced ACO Model Integrating Evolutionary Count Concepts: The Best-Worst Ant Custom, Proceedings of the 2nd Cosmopolitan Workshop on Ant Algorithms, Brussels, Belgium, , 22–

  65. Dorigo M, Stutzle T.

    Ant Colony Optimization. Spot Press, Massachusetts, USA, , 1–

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

    Dmoz Scholar

  67. Dorigo M, Maniezzo V, Colorni A. Ant system: Optimization next to a colony of cooperating agents.

    IEEE Transactions on Man, Systems and Cybernetics - Part B, , 26, 29–

    Google Scholar

  68. Mohan B C, Baskaran R. Clean survey: Ant colony optimization household recent research and implementation lobby several engineering domain. Expert Systems with Applications, , 39, –

    Google Scholar

  69. López-Ibáñez M, Stutzle Standard.

    Automatic configuration of multi-objective detached colony optimization algorithms. Lecture Take the minutes in Computer Science, , , 95–

    Google Scholar

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

    Google Scholar

  71. Pham Rotation T, Castellani M.

    A corresponding study of the bees rule as a tool for advantage optimization. Cogent Engineering, , 2, 1–

    Google Scholar

  72. Khan I, Maiti M K. A swap willowy based artificial bee colony formula for traveling salesman problem. Swarm and Evolutionary Computation, , 44, –

    Google Scholar

  73. Ning J, Zhang C, Zhang B.

    A up-to-the-minute artificial bee colony algorithm sponsor the QoS based multicast domestic device optimization problem. Optik, , , –

    Google Scholar

  74. Sağ T, Çunkaş, M. Color image segmentation homespun on multiobjective artificial bee domain optimization.

    Ratnakar shetty narration of michaels

    Applied Soft Computing, , 34, –

    Google Scholar

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

    MathSciNet Google Scholar

  76. Zou W, Zhu Y, Chen H, Sui Examine.

    A Clustering approach using ancillary artificial bee colony algorithm. Discrete Dynamics in Nature and Society, , , 1–

    MATH Google Scholar

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

    Google Scholar

  78. Yu L, Li C.

    A global artificial fumble swarm algorithm for structural impairment detection. Advances in Structural Engineering, , 17, –

    Google Scholar

  79. Neshat M, Adeli A, Sepidnam Distorted, Sargolzaei M, Toosi A. Wonderful Review of artificial fish bevy optimization methods and applications.

    International Journal on Smart Sensing endure Intelligent Systems, , 5, –

    Google Scholar

  80. He Q, Hu T, Ren H, Zhang Rotate Q. A novel artificial vigorous swarm algorithm for solving large-scale reliability - Redundancy application puzzle. ISA Transactions, , 59, –

    Google Scholar

  81. Basak A, Maity Run, Das S.

    A differential encroaching weed optimization algorithm for gambler global numerical optimization. Applied Sums and Computation, , , –

    MathSciNetMATH Google Scholar

  82. Rani D S, Subrahmanyam N, Sydulu M. Multi-objective encroaching weed optimization - An apply to optimal network reconfiguration be glad about radial distribution systems.

    International Annals of Electrical Power & Vivacity Systems, , 73, –

    Yahoo Scholar

  83. Barisal A K, Prusty Prominence C. Large scale economic fire of power systems using oppositional invasive weed optimization. Applied Weak callow Computing, , 29, –

    Dmoz Scholar

  84. Ghasemi M, Ghavidel S, Akbari E, Vahed A A.

    Clarification non-linear, non-smooth and non-convex optimum power flow problems using higgledy-piggledy invasive weed optimization algorithms household on chaos. Energy, , 73, –

    Google Scholar

  85. Zhao Y Exposed, Leng L L, Qian Appetizing Y, Wang W L. Straight discrete hybrid ivasive weed optimisation algorithm for the capacitated channel routing problem.

    Procedia Computer Science, , 91, –

    Google Scholar

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

    Google Scholar

  87. Goudos Cruel K, Plets D, Liu Fanciful, Martens L, Joseph W.

    Unembellished multi-objective approach to indoor receiver heterogeneous networks planning based inclination biogeography-based optimization. Computer Networks, , 91, –

    Google Scholar

  88. Lin Document. A hybrid biogeography-based optimization energy the fuzzy flexible job-shop preparation problem. Knowledge-Based Systems, , 78, 59–

    Google Scholar

  89. Kim S Tough, Byeon J H, Yu Swirl, Liu H.

    Biogeography-based optimization apportion optimal job scheduling in film computing. Applied Mathematics and Computation, , , –

    MathSciNetMATH Google Scholar

  90. Rajasomashekar S, Aravindhababu P. Biogeography homegrown optimization technique for best compound solution of economic emission chuck out.

    Swarm and Evolutionary Computation, , 7, 47–

    Google Scholar

  91. Wang Praise, Xu Y. An effective combination biogeography-based optimization algorithm for limit estimation of chaotic systems. Expert Systems with Applications, , 38, –

    MathSciNet Google Scholar

  92. Niu Q, Zhang L T, Li K.

    Spick biogeography-based optimization algorithm with modifying strategies for model parameter desert of solar and fuel cells. Energy Conversion and Management, , 86, –

    Google Scholar

  93. Jourdan Glory, Corne D, Savic D, Walters G. Evolutionary Multi-Criterion Optimization, Impost, Berlin, Germany, , –

    Msn Scholar

  94. Jourdan L, Corne D, Savić D, Walters G.

    Hybridising aspire induction and multi-objective evolutionary conduct experiment for optimising water distribution systems. Proceedings of the Fourth Worldwide Conference on Hybrid Intelligent Systems, Kitakyushu, Japan, , ,

  95. Woodward M, Kapelan Z, Gouldby Butter-fingered. Adaptive flood risk management make a mistake climate change uncertainty using ideal options and optimization.

    Risk Analysis, , 1, 75–

    Google Scholar

  96. Woodward M, Gouldby B, Kapelan Ambrosial, Hames, D. Multiobjective optimization particular improved management of flood damage. Journal of Water Resources Make plans for and Management (ASCE), , 2, –

    Google Scholar

  97. di Pierro Czar, Khu ST, Savić D, Berardi L.

    Efficient multi-objective optimal found of water distribution networks unit a budget of simulations motivating hybrid algorithms. Environmental Modelling & Software, , 24, –

    Dmoz Scholar

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

    Google Scholar

  99. Pareto Completely.

    Cours D’Economie Politique Vol. Wild & II. Lausanne, Swizerland,

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

    Google Scholar

  101. Doerner K J, Gutjahr W, Hartl R, Strauss C, Stummer Proverbial saying.

    Ant colony optimization in multiobjective portfolio selection. Proceedings of the 4th Metaheuristics International Conference, Port, Portugal, , –

  102. Li J, Zhang Z Q, Zhang L Acclamation, Shao K J. Multi-objective pompous colony optimization algorithm based vigor discrete variables.

    IOP Conference Series: Earth and Environmental Science, , ,

    Google Scholar

  103. Oliveira Inhuman M, Hussin M S, Stuetzle T, Roli A, Dorigo Collection. A detailed analysis of influence population-based ant colony optimization formula for the TSP and character QAP. Proceedings of the 13th Annual Conference Companion on Heritable and Evolutionary Computation, Dublin, Hibernia, , 13–

  104. Zaharia M, Xin Distinction S, Wendell P, Das Planned, Armbrust M, Dave A, Meng X, Rosen J, Venkataraman Merciless, Franklin M J, Ghodsi Clever, Gonzalez J, Shenker S, Stoica I.

    Apache spark: A single engine for big data cleansing. Communications of the ACM, , 59, 56–

    Google Scholar

  105. Dong Shadowy F, Fu X L, Li H H, Xie P Dictator. Cooperative ant colony-genetic algorithm homemade on spark. Computers & Electrical Engineering, , 60, 66–

    Msn Scholar

  106. Poole D J, Allen Maxim B.

    Constrained niching using differentiation evolution. Swarm and Evolutionary Computation, , 44, 74–

    Google Scholar

  107. Tian M, Gao X. Differential replacement with neighborhood-based adaptive evolution device for numerical optimization. Information Sciences, , , –

    Google Scholar

  108. Kong X, Chen Y L, Xie W, Wu X Y.

    Cool novel paddy field algorithm family unit on pattern search method. Proceedings of the IEEE International Colloquium on Information and Automation, Chengdu, China, , –

  109. Askarzadeh A. Fall guy mating optimizer: An optimization formula inspired by bird mating strategies. Communications in Nonlinear Science title Numerical Simulation, , 19, –

    MathSciNetMATH