main| new issue| archive| editorial board| for the authors| publishing house|
Ðóññêèé
Main page
New issue
Archive of articles
Editorial board
For the authors
Publishing house

 

 


ABSTRACTS OF ARTICLES OF THE JOURNAL "INFORMATION TECHNOLOGIES".
No. 11. Vol. 29. 2023

DOI: 10.17587/it.29.559-573

V. V. Kureychik, Dr. Sc., Professor, S. I. Rodzin, PhD., Professor,
Southern Federal University, Taganrog, Russian Federation

Bio-Heuristics Inspired by Fauna (Review)

In recent years, there has been a noticeable increase in publications devoted to new metaheuristics for the effective solution of optimization problems for which classical methods do not provide acceptable results with reasonable time and resources. The review focuses on bio-heuristics inspired by fauna, which account for about half of the publications of meta-heuristics in the last ten years, and which model patterns of behavior of individuals of the animal world. The review is organized taking into account the biological classification of fauna and the simulated mechanisms of animal behavior. The analysis of the mechanisms used to ensure a balance between the convergence rate of the algorithm and the diversification of the solution search space is also given.
Keywords: bio-heuristics, algorithm convergence, search space diversification, population, fitness function, optimization, fauna

P. 559-573

References

  1. Salcedo-Sanz S. Modern meta-heuristics based on nonlinear physics processes: A review of models and design procedures, Phys. Rep., 2016, vol. 655, ðð. 1—70.
  2. Kureychik V. V., Rodzin S. I. Computational models of evolutionary and swarm bio heuristics (review), Informacionnye Tehnologii, 2021, vol. 27, no. 10, pp. 507—520 (in Russian).
  3. Wolpert D. H., Macready W. G. No free lunch theorems for optimization, IEEE Trans. Evol. Comput., 1997, no. 1, pp. 67—82.
  4. Adam S. P. et al. No Free Lunch Theorem: A Review. In Approximation and Optimization: Algorithms, Complexity and Applications, Springer Int. Publ.: Cham, Switzerland, 2019, pp. 57—82.
  5. Kureychik V. V., Rodzin S. I. Computational models of bio heuristics based on physical and cognitive processes (review), Informacionnye Tehnologii, 2021, vol. 27, no. 11, pp. 563—574 (in Russian).
  6. Rodzin S. I. Smart dispatching and metaheuristic swarm flow algorithm, Jour. of Computer and Systems Sciences Int., 2014, no. 53(1), pp. 109—115.
  7. Bassin A. O., Buzdalov M. V., Shalyto A. A. The "One-fifth Rule" with Rollbacks for Self-adjustment of the Population Size in the (1 + (X, X)) Genetic Algorithm, Automatic Control and Computer Sciences, 2021, vol. 55, no. 7, pp. 885—902.
  8. Stanovov V., Akhmedova S., Vakhnin A., Sopov E., Se-menkin E., Affenzeller M. Improving the Quantum Multi-Swarm Optimization with Adaptive Differential Evolution for Dynamic Environments, Algorithms, 2022, vol. 15(5), pp. 154.
  9. Yang X. S. A New Metaheuristic Bat-Inspired Algorithm, Nat. Inspired Coop. Strateg. Optim. (NICSO), 2010, vol. 284, pp. 65—74.
  10. Hodashinsky I. A. Methods for Improving the Efficiency of Swarm Optimization Algorithms. a Survey, Automation and Remote Control, 2021, vol. 82, no. 6, pp. 935—967.
  11. Zheng Y.-J. Water wave optimization: A new nature-inspired metaheuristic, Comput. Oper. Res., 2015, vol. 55, pp. 1—11.
  12. Molina D. et al. Comprehensive taxonomies of nature-and bio-inspired optimization: Inspiration versus algorithmic behavior, critical analysis recommendations, Cogn. Comput., 2020, vol. 12, pp. 897—939.
  13. Stegherr H., Heider M., Hahner J. Classifying Meta-heuristics: Towards a unified multi-level classification system, Nat. Comput., 2020, vol. 21(5), pp. 1—17.
  14. Dragoi E. N., Dafinescu V. Review of Metaheuristics In­spired from the Animal Kingdom, Mathematics, 2021, vol. 9(19), pp. 2335.
  15. Brabazon A., McGarraghy S. Introduction to Foraging-Inspired Algorithms, In Foraging-Inspired Optimization Algorithms, Springer Int. Publ., 2018, pp. 87—101.
  16. Askarzadeh A. Bird mating optimizer: An optimization algorithm inspired by bird mating strategies, Commun. Nonlinear Sci. Numer. Simul., 2014, vol. 19, pp. 1213—1228.
  17. Yang X.-S., Deb S. Cuckoo search via L@vy flights, IEEE World Congress on Nature & Biologically Inspired Computing (NaBIC), 2009.
  18. Joshi A., Kulkarni O., Kakandikar G., Nandedkar V. Cuckoo Search Optimization — A Review, Mater. Today Proc., 2017, no. 4, pp. 7262—7269.
  19. Moosavi S. H. S., Bardsiri V. K. Satin bowerbird optimizer: A new optimization algorithm to optimize ANFIS for software development effort estimation, Eng. Appl. Artif. Intell., 2017, vol. 60, pp. 1—15.
  20. Yang X. S., Deb S. Eagle strategy using Levy walk and firefly algorithms for stochastic optimization, In Nature Inspired Cooperative Strategies for Optimization (NICSO), Springer: Ger­many, 2010, pp. 101—111.
  21. Meng X., Liu, Y., Gao X., Zhang H. A New Bio-inspired Algorithm: Chicken Swarm Optimization, In Advances in Swarm Intelligence. Springer, 2014, pp. 86—94.
  22. Askarzadeh A. A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm, Comput. Struct., 2016, vol. 169, pp. 1—12.
  23. Hodashinsky I. A., Sarin K. S. Feature Selection for Classification through Population Random Search with Memory, Automation and Remote Control, 2019, vol. 80, no. 2, pp. 324—333.
  24. Jain M. et al. Owl search algorithm: A novel nature-inspired heuristic paradigm for global optimization, J. Intell. Fuzzy Syst., 2018, vol. 34, pp. 1573—1582.
  25. Zhuoran Z. et al. An optimization method: Hummingbirds op­timization algorithm, J. Syst. Eng. Electron, 2018, vol. 29, pp. 386—404.
  26. Brabazon A., Cui W., O'Neill M. The raven roosting op­timization algorithm, Soft Comput., 2015, vol. 20, pp. 525—545.
  27. Heidari A. A. et al. Harris hawks optimization: Algorithm and applications, Futur. Gener. Comput. Syst., 2019, vol. 97, pp. 849—87.
  28. Abualigah L. et al. Aquila Optimizer: A novel meta-heuristic optimization algorithm, Comput. Ind. Eng., 2021, vol. 157, pp. 10725.
  29. Mohammadi-Balani A. et al. Golden eagle optimizer: A nature-inspired metaheuristic algorithm, Comput. Ind. Eng., 2020, vol. 152, pp. 107050.
  30. Sun J., Lei X. Geese-inspired hybrid particle swarm optimization algorithm for traveling salesman problem, IEEE Int. Conf. on AI and Computational Intelligence, 2009.
  31. Duan H., Qiao P. Pigeon-inspired optimization: A new swarm intelligence optimizer for air robot path planning, Int. J. Intell. Comput. Cybern., 2014, no. 7, pp. 24—37.
  32. Dhiman G., Kumar V. Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems, Knowledge-Based Syst., 2018, vol. 165, pp. 169—196.
  33. Dhiman G., Kumar V. Emperor penguin optimizer: A bio-inspired algorithm for engineering problems, Knowledge-Based Syst., 2018, vol. 159, pp. 20—50.
  34. Abdollahzadeh B., Gharehchopogh F. S., Mirjalili S. African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems, Comput. Ind. Eng., 2021, vol. 158, pp. 107408.
  35. Kaveh A., Farhoudi N. A new optimization method: Dolphin echolocation, Adv. Eng. Softw., 2013, vol. 59, pp. 53—70.
  36. Ebrahimi A., Khamehchi E. Sperm whale algorithm: An effective metaheuristic algorithm for production optimization problems, J. Nat. Gas Sci. Eng., 2016, vol. 29, pp. 211—222.
  37. Gharehchopogh F. S., Gholizadeh H. A comprehensive survey: Whale Optimization Algorithm and its applications, Swarm Evol. Comput., 2019, vol. 48, pp. 1—24.
  38. Mirjalili S., Mirjalili S. M., Lewis A. Grey Wolf Optimizer, Adv. Eng. Softw., 2014, vol. 69, pp. 46—61.
  39. Bansal J. C., Sharma H., Jadon S. S., Clerc M. Spider Monkey Optimization algorithm for numerical optimization, Me-metic Comput., 2014, no. 6, pp. 31—47.
  40. Khishe M., Mosavi M. R. Chimp optimization algorithm, Expert Syst. Appl., 2020, vol. 149, pp. 113338.
  41. Abdollahzadeh B., Gharehchopogh F. S., Mirjalili S. Artificial gorilla troops optimizer: A new nature-inspired metaheuris-tic algorithm for global optimization problems, Int. J. Intell. Syst., 2021, vol. 36, pp. 5887—5958.
  42. Dhiman G., Kumar V. Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering appli­cations, Adv. Eng. Softw., 2017, vol. 114, pp. 48—70.
  43. Jain M., Singh V., Rani A. A novel nature-inspired algorithm for optimization: Squirrel search algorithm, Swarm Evol. Comput., 2019, vol. 44, pp. 148—175.
  44. Rajakumar B. R. The Lion's Algorithm: A New Nature-Inspired Search Algorithm, Procedia Technol., 2012, no. 6, pp. 126—135.
  45. Mohammad T. M., Mohammad H. B., Shirzadi M., Bag-heri M. A novel meta-heuristic algorithm for numerical function optimization: Blind, naked mole-rats (BNMR) algorithm, Sci. Res. Essays., 2012, no. 7, pp. 3566—3583.
  46. Wang G. G., Deb S., Gao X. Z., Coelho L. D. A new me-taheuristic optimization algorithm motivated by elephant herding behaviour, Int. J. Bio-Inspired Comput., 2016, no. 8, pp. 394.
  47. Shadravan S., Naji H., Bardsiri V. The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems, Eng. Appl. Artif. Intell., 2019, vol. 80, pp. 20—34.
  48. Kumar N., Singh N., Vidyarthi D. P. Artificial lizard search optimization (ALSO): A novel nature-inspired meta-heu-ristic algorithm, Soft Comput., 2021, vol. 25, pp. 6179—6201.
  49. Braik M. S. Chameleon Swarm Algorithm: A bio-inspired optimizer for solving engineering design problems, Expert Syst. Appl., 2021, vol. 174, pp. 114685.
  50. Bulanova N. S., Buzdalova A. S., Shalyto A. A. Method of adaptive selection of mutation operators of artificial immune systems and local search, Scientific and Technical Bulletin of information technologies, Mechanics and Optics, 2017, vol. 17, no. 6. pp. 1100—1106 (in Russian).
  51. Oftadeh R., Mahjoob M., Shariatpanahi M. A novel meta-heuristic optimization algorithm inspired by group hunting of animals: Hunting search, Comput. Math. Appl., 2010, vol. 60, pp. 2087—2098.
  52. Faramarzi A., Heidarinejad M., Mirjalili S., Gandomi A. H. Marine Predators Algorithm: A nature-inspired metaheuristic, Expert Syst. Appl., 2020, vol. 152, pp. 113377.
  53. Mohseni S., Gholami R., Zarei N., Zadeh A. R. Com­petition over resources: A new optimization algorithm based on animals behavioral ecology, Int. Conf. on Intelligent Networking and Collaborative Systems (INCoS), Italy, 2014, DOI: 10.1109/INCoS.2014.55.
  54. Sharafi Y., Khanesar M. A., Teshnehlab M. COOA: Competitive optimization algorithm, Swarm Evol. Comput., 2016, vol. 30, pp. 39—63.
  55. Rodzin S., Rodzina O. Metaheuristics memes and bio-geography for trans computational combinatorial optimization problems, Proc. of the 6th Int. Conf. Cloud System and Big Data Engineering, 2016, pp. 1—5.
  56. Duman E., Uysal M., Alkaya A. F. Migrating Birds Optimization: A new metaheuristic approach and its performance on quadratic assignment problem, Inf. Sci., 2012, vol. 217, pp. 65—77.
  57. Rodzin S. I., Skobtsov Yu. A., El-Khatib S. A. Bio heuristics: theory algorithms and applications: monograph, Cheboksary, ID "Wednesday", 2019, 224 p. DOI: 10.31483/a-54 (in Russian).
  58. Rajasekhar A., Lynn N., Das S., Suganthan P. Computing with the collective intelligence of honey bees — A survey, Swarm Evol. Comput., 2017, vol. 32, pp. 25—48.
  59. Dorigo M., Maniezzo V., Colorni A. The Ant System: An Autocatalytic Optimizing Process, Milan, Italy, Politecnico di Milano, 1991, pp. 1—21.
  60. El-Khatib S., Rodzin S., Skobtsov Yu. Investigation of optimal heuristical parameters for mixed ACO-k-means segmen­tation algorithm for MRI images, Proc. 3rd Int. Sc. Conf. on In­formation Technologies in Science, Management, Social Sphere and Medicine (ITSMSSM), 2016, vol. 51, ðð . 216—221.
  61. Semenkina O. E., Popov E. A., Semenkin E. S. Cooperative Self-Configuring Nature-Inspired Algorithm for a Scheduling Problem, IOP Conf. Ser.: Materials Science and Engineering. Kras­noyarsk Science and Technology City Hall, 2021, pp. 12080.
  62. Liang K., Karpenko A. P. A Modified Particle Swarm Algorithm for Solving Group Robotics Problem, Advances in Intelligent Systems and Computing, 2020, vol. 1127 AISC, pp. 205—217.
  63. Das K. N., Singh T. K. Drosophila Food-Search Optimization, Appl. Math. Comput., 2014, vol. 231, pp. 566—580.
  64. Hu J. H. et al. The fruit fly optimization algorithms for patient-centered care based on interval trapezoidal type-2 fuzzy numbers, Int. J. Fuzzy Syst., 2019, no. 5, pp. 1270—1287.
  65. Feng X., Lau F. C., Yu H. A novel bio-inspired approach based on the behavior of mosquitoes, Inf. Sci., 2013, vol. 233, pp. 87—108.
  66. Wang G.-G., Deb S., Cui Z. Monarch butterfly optimization, Neural Computing and Applications, 2015, vol. 31, pp. 1995—2014.
  67. Bhattacharjee K. K., Sarmah S. P. Monarch Migration Algorithm for optimization problems, IEEE Int. Conf. on Indus­trial Engineering and Engineering Management, Bali, Indonesia, 2016, DOI: 10.1109/IEEM.2015.7385648.
  68. Kumar A., Misra R. K., Singh D. Butterfly optimizer, IEEE Workshop on Computational Intelligence: Theories, Applications and Future Directions, India, 2015, DOI: 10.1109/wci.2015.7495523.
  69. Mirjalili S. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm, Knowl.-Based Syst., 2015, vol. 89, pp. 228—249.
  70. Mohamed A.-A. et al. Optimal power flow using moth swarm algorithm, Electr. Power Syst. Res., 2017, vol. 142, pp. 190—206.
  71. Chen S. Locust Swarms-A new multi-optima search technique, IEEE Congress on Evolutionary Computation, Trondheim, Norway, 2009, DOI: 10.1109/CEC.2009.4983152.
  72. Cuevas E., Diaz Cortes M., Oliva Navarro D. Advances of Evolutionary Computation: Methods and Operators, Springer International Publishing, 2016, 214 ð .
  73. Saremi S., Mirjalili S., Lewis A. Grasshopper Optimiza­tion Algorithm: Theory and application, Adv. Eng. Softw., 2017, vol. 105, pp. 30—47.
  74. Mirjalili S. The Ant Lion Optimizer, Adv. Eng. Softw., 2015, vol. 83, pp. 80—98.
  75. Wu S.-J., Wu C.-T. A bio-inspired optimization for inferring interactive networks: Cockroach swarm evolution, Expert Syst. Appl., 2015, vol. 42, pp. 3253—3267.
  76. Mirjalili S. Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi objective problems, Neural Comput. Appl., 2015, vol. 27, pp. 1053—1073.
  77. Kallioras N. A. et al. Pity beetle algorithm — A new meta-heuristic inspired by the behavior of bark beetles, Adv. Eng. Softw., 2018, vol. 121, pp. 147—166.
  78. Cuevas E. et al. A swarm optimization algorithm inspired in the behavior of the social spider, Expert Syst. Appl., 2013, vol. 40, pp. 6374—6384.
  79. Wang G.-G. et al. A comprehensive review of krill herd algorithm: Variants, hybrids and applications, Artif. Intell. Rev., 2017, vol. 51, pp. 119—148.
  80. Wang G. G., Deb S., Coelho L. D. Earthworm optimiza­tion algorithm: A bio-inspired metaheuristic algorithm for global optimization problems, Int. J. Bio-Inspired Comput., 2015, vol. 1, DOI: 10.1504/IJBIC.2015.10004283.
  81. Kaur S. et al. Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization, Eng. Appl. Artif. Intell., 2020, vol. 90, pp. 103541.
  82. Crepinsek M., Liu S. H., Mernik M. Exploration and exploitation in evolutionary algorithms: A survey, ACM Comput. Surv., 2013, vol. 43, pp. 1—33.
  83. Kureychik V. V., Kureychik V. M., Rodzin S. I. Theory of evolutionary computing, Moscow, Fizmatlit, 2012, 260 p. (in Russian).
  84. Kobak V. G., Zolotykh O. A., Zolotykh I. A., Poliev A. V. The list algorithms accuracy characteristics improvement based on Krohn's algorithm and its modifications, Jour. of Physics: Conf. Ser., 2021, vol. 2131(2), pp. 022122.
  85. Karpenko A., Agasiev T., Sakharov M. Intellectualization Methods of Population Algorithms of Global Optimization, Stu­dies in Systems, Decision and Control, 2020, vol. 259, pp. 137—151.
  86. LaTorre A. et al. Fairness in bio-inspired optimization research: A prescription of methodological guidelines for comparing meta-heuristics, arXiv Preprint 2020.
  87. Montague M., Aslam J. A. Condorcet fusion for improved retrieval, Proc. of the Eleventh Int. Conf. on Information and Know. Manag., 2002, DOI: 10.1145/584792.584881.
  88. Tzanetos A., Dounias G. Nature inspired optimization algorithms or simply variations of metaheuristics? Artif. Intell. Rev., 2021, vol. 54, pp. 1841—1862.

 

To the contents