О книге
Монография посвящена методам анализа и синтеза популяционных алгоритмов глобальной оптимизации и издается в двух томах. В первом томе систематизированы сущности популяционных алгоритмов и их характеристики, а также эволюционные операции, операторы и процедуры. Представлены методы параметрической оптимизации и параметрического синтеза популяционных алгоритмов. Рассмотрены типовые структуры этих алгоритмов, а также методы их структурного синтеза путем гибридизации. Второй том является своеобразной базой данных для материала первого тома. В нем приведены схемы большого числа известных популяционных алгоритмов, а также их паттерны — лаконичные формализованные описания.
Монография ориентирована на специалистов, использующих в своей работе методы, алгоритмы и программы оптимизации. В то же время она может быть полезна аспирантам и студентам высших учебных заведений, обучающихся по направлению «Информатика и вычислительная техника» и смежным направлениям.
Список литературы
- Карпенко А.П. Современные алгоритмы поисковой оптимизации. Алгоритмы, вдохновленные природой. М.: Изд-во МГТУ им. Н.Э. Баумана, 2014. 446 с.
- Xing B., Gao W.-J. Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms. Springer Cham Heidelberg New York Dordrecht London, 2014. 451 p.
- Taherdangkoo M., Shirzadi M.H., Bagheri M.H. A novel meta-heuristic algorithm for numerical function optimization: Blind, Naked Mole-Rats (BNMR) algorithm // Scientific Research and Essays. 2012. Vol. 7. Рp. 3566–3583.
- Wolpert D.H., Macready W.G. No free lunch theorems for optimization // IEEE Transactions on Evolutionary Computation, 1997, No. 1(1), Рp. 67–82.
- Huyer W., Neumaier A. SNOBFIT Stable noisy optimization by branch and fit, ACM Transactions on Mathematical Software (TOMS). 2008. Vol. 35. No. 2. Рp. 121.
- Гладков Л.А., Курейчик В.В., Курейчик В.М. и др. Биоинспирированные методы в оптимизации: монография. М.: Физматлит, 2009. 384 с.
- Курейчик В.М., Курейчик В.В., Родзин С.И. Основы теории эволюционных вычислений. Ростов-на-Дону: Изд-во ЮФУ, 2010. 222 с.
- Родзин С.И., Скобцов Ю.А., Эль-Хатиб С.А. Биоэвристики: теория, алгоритмы и приложения: монография. Чебоксары: ИД «Среда», 2019. 224 с.
- Пантелеев А.В., Метлицкая Д.В., Алешина Е.А. Методы глобальной оптимизации: Метаэвристические стратегии и алгоритмы. М.: Вузовская книга, 2013. 244 с.
- Anupam B., Mishra K.K., Tiwari S., Misra A.K. Physics-Inspired Optimization Algorithms: A Survey // Hindawi Publishing Corporation. Journal of Optimization, 2013. (http://dx.doi.org/10.1155/2013/438152).
- Brownlee J. Clever Algorithms: Nature-Inspired Programming Recipes, 2011. 441 p. (https://github.com/clever-algorithms/CleverAlgorithms).
- Can U., Alatas B. Physics Based Metaheuristic Algorithms for Global Optimization // American Journal of Information Science and Computer Engineering. 2015. Vol. 1. No. 3. Рp. 94–106.
- Elbeltagi E., Hegazy T., Grierson D. Comparison among five evolutionary-based optimization algorithms // J. Advanced Engineering Informatics, 2005. No. 19. Рp. 43–53.
- Engelbrecht A.P. Computational Intelligence. An Introduction: John Wiley & Sons Ltd, England, 2007. 597 p.
- Evolutionary Computation 1. Basic Algorithms and Operators. Edited by T. Bck, D.B. Fogel, Z. Michalewicz: Institute of physics publishing Bristol and Philadelphia, 2000. 339 p.
- Evolutionary Computation 2. Advanced Algorithms and Operators. Edited by T. Bck, D.B. Fogel, Z. Michalewicz: Institute of physics publishing Bristol and Philadelphia, 2000. 270 p.
- Fogel D.B. Evolutionary computation: toward a new philosophy of machine intelligence / John Wiley & Sons, Inc., Hoboken, New Jersey, 2006. 274 p.
- Glover F., Kochenberger G.A. Handbook of metaheuristics / Springer, 2010. 648 p.
- Kaveh A. Advances in Metaheuristic Algorithms for Optimal Design of Structures / Springer Cham Heidelberg New York Dordrecht London, 2014. 426 p.
- De Jong A.K. Evolutionary computation: a unified approach. / The MIT Press. Cambridge, Massachusetts. London, England, 2006. 258 p.
- Krishnaveni A., Shankar R., Duraisamy S. A Survey on Nature-Inspired Computing (NIC): Algorithms and Challenges // Global Journal of Computer Science and Technology: Neural & Artificial Intelligence. 2019. Vol. 19. Issue 3-D. Online ISSN: 0975-4172 & Print ISSN: 0975-4350.
- Siddique N., Adeli H. Nature Inspired Computing: An Overview and Some Future Directions // Cognitive Computation. 2015. Vol. 7. Рp. 706–714.
- Sehrawat P., Rohil H., Devi Ch. Taxonomy of Swarm Optimization // International Journal of Advanced Research in Computer Science and Software Engineering. 2013. Vol. 3. Issue 8. Рp. 1400–1406.
- Devi R.B. et al. Survey on evolutionary computation tech techniques and its application in different fields // International Journal on Information Theory (IJIT). 2014. Vol. 3. No. 3. Рp. 73–82.
- Roy S., Biswas S., Chaudhuri S.S. Nature-Inspired Swarm Intelligence and Its Applications // International Journal of Modern Education and Computer Science (IJMECS). 2014. Vol. 12. Рp. 55–65.
- Sean Luke. Essentials of Metaheuristics (Second Edition). 2013. Publisher: lulu.com. 242 р.
- Yang X.-S. Nature-Inspired Metaheuristic Algorithms. Second Edition. Luniver Press, United Kingdom, 2010. 148 p.
- Darwish A. Bio-inspired computing: Algorithms review, deep analysis, and the scope of applications // Future Computing and Informatics Journal. 2018. Vol. 3. Рp. 231–246.
- Hansen N., Auger A., Finck S., Ros R. Real-parameter black-box optimization benchmarking 2010: Experimental setup. Diss. INRIA, 2010.
- Decison Tree for Optimization Software, available at: http://plato.asu.edu/guide.html (accessed 25.01.2018).
- Jamil M., Yang X.-S. A literature survey of benchmark functions for global optimization problems, Journal of Mathematical Modelling and Numerical Optimization. 2013. Vol. 4. No. 2. Рp. 150–194.
- Gould N.I.M., Orban D., Toint P.L. CUTEst: a constrained and unconstrained testing environment with safe threads for mathematical optimization, Computational Optimization and Applications, 2014. Vol. 60. No. 3. Рp. 545–557.
- Dolan E.D., Mor J.J., Munson T.S. Benchmarking optimization software with COPS 3.0. Technical memorandum, ANL/MCS-TM-273. Argonne National Laboratory, IL (US). 2004. Рp. 159.
- Floudas C.A. et al. Handbook of test problems in local and global optimization, Springer Science & Business Media. 2013. Vol. 33.
- Beiranvand V., Hare W., Lucet Y. Best practices for comparing optimization algorithms, Optimization and Engineering. 2017. Vol. 18. No. 4. Рp. 815–848.
- Godfrey C. Onwubolu, Babu B.V. New Optimization Techniques in Engineering. Springer-Verlag Berlin Heidelberg. 2004. 712 p.
- Wooldridge M. An Introduction to MultiAgent Systems: Wiley, Chichester, 2009. 488 p.
- De Carvalho V.R., Sichman J.S. Evolutionary Computation Meets Multiagent Systems for Better Solving Optimization Problems / F. Koch et al. (Eds.): GEAR 2018, CCIS 999: Springer Nature Singapore Pte Ltd., 2019. Рp. 27–41.
- Andrei N. 40 conjugate gradient algorithms for unconstrained optimization. A survey on their definition // ICI Technical Report. 2008. No. 13. Рp. 113.
- Spall J.C. Introduction to stochastic search and optimization: estimation, simulation and control: John Wiley and Sons, 2003. 618 p.
- Синюк В.Г., Ермоленко Д.Н. Нечеткое моделирование на основе метода косвенного нечеткого вывода: монография. Белгород: Изд-во БГТУ им. В.Г. Шухова, 2011. 155 с.
- Chattopadhyay S., Murthy C.A., Sankar K.Pal. Fitting truncated geometric distributions in large scale real world networks // Theoretical Computer Science. 2014. Vol. 551. Рp. 22–38.
- Кузнецов С.П. Динамический хаос (курс лекций). М.: Физматлит, 2001. 295 с.
- Kaveh A., Rahami H. Nonlinear analysis and optimal design of structures via force method and genetic algorithm // Computer and Structures. 2006. Vol. 84. Issue 12. Рp. 770–778.
- Флах П. Машинное обучение. Наука и искусство построения алгоритмов, которые извлекают знания из данных / пер. с англ. А.А. Слинкина. М.: ДМК Пресс, 2015. 400 с.
- Сидняев Н.И., Вилисова Н.Т. Введение в теорию планирования эксперимента: учебное пособие. М.: Изд-во МГТУ им. Н. Э. Баумана, 2011. 463 c.
- Кнут Д. Искусство программирования. Т. 3. Сортировка и поиск (The Art of Computer Programming, Vol. 3. Sorting and Searching). 2-е изд.: пер. с англ. М.: ИД «Вильямс», 2007. 832 с.
- Соболь И.М., Статников Р.Б. Выбор оптимальных параметров в задачах со многими критериями. М.: Наука, 1981. 114 с.
- Hutter F., Ramage S. Manual for SMAC version v2. 10.02-master // Vancouver: Department of Computer Science University of British Columbia, 2015. 73 p.
- Blot A. et al. MO-ParamILS: A Multi-Objective Automatic Algorithm Configuration Framework // 10th International Conference on Learning and Intelligent Optimization. Springer International Publishing, 2016. Рp. 32–47.
- Ans tegui C., Sellmann M., Tierney K. A gender-based genetic algorithm for the automatic configuration of algorithms // International Conference on Principles and Practice of Constraint Programming, Springer, Berlin, Heidelberg, 2009. Рp. 142–157.
- L pez-Ib ez M., Dubois-Lacoste J., St tzle T., Birattari M. The irace package: Iterated racing for automatic algorithm configuration // IRIDIA, Universit Libre de Bruxelles, Belgium, Tech. Rep. TR/IRIDIA/2011-004, 2011. Рp. 43–58.
- Fitzgerald T., Malitsky Y., O’Sullivan B., Tierney K. ReACT: Real-Time Algorithm Configuration through Tournaments // Seventh Annual Symposium on Combinatorial Search, 2014. 15 p.
- Eiben A.E., Smith J.E. Introduction to evolutionary computing // Natural Computing. Springer, Berlin Heidelberg New York, 2015. 287 p.
- Агасиев Т.А., Карпенко А.П. Современные техники глобальной оптимизации. Обзор // Информационные технологии, 2018. № 6. С. 370–386.
- Агасиев Т.А. Автоматизация настройки алгоритмов параметрической оптимизации проектных решений для серийных задач высокой вычислительной сложности : дисс. … канд. техн. наук: 05.13.12. М., МГТУ им. Н.Э. Баумана, 2021. 163 с.
- Balaprakash P., Birattari M., St tzle T. Improvement strategies for the F-Race algorithm: Sampling design and iterative refinement // 4th International workshop on hybrid metaheuristics, Springer, Berlin, Heidelberg, 2007. Рp. 108–122.
- Maron O., Moore A.W. The racing algorithm: Model selection for lazy learners // Artificial Intelligence Review 11(1-5), 1997. Рp. 193–225.
- Birattari M., Kacprzyk J. Tuning metaheuristics: a machine learning perspective / Berlin, Springer. 2009. Vol. 197. Рp. 85–114.
- Gutmann H.-M. A radial basis function method for global optimization // Journal of Global Optimization, 2001. No. 19. Рp. 201–227.
- Колодяжный М., Зайцев А. Гетероскедастичные гауссовские процессы и их применение для байесовской оптимизации // ИТиС 2018. М: Институт проблем передачи информации им. А. А. Харкевича РАН, 2018. С. 42–51.
- Coy S.P., Golden B.L., Runger G.C., Wasil E.A. Using experimental design to find effective parameter settings for heuristics // Journal of Heuristics. 2001. Vol. 7. No. 1. Рp. 77–97.
- Bartz-Beielstein T., Parsopoulos K.E., Vrahatis M.N. Analysis of particle swarm optimization using computational statistics // International conference on numerical analysis and applied mathematics ICNAAM, 2004. Рp. 34–37.
- Ugolotti R. Meta-optimization of Bio-inspired Techniques for Object Recognition. PhD thesis. Universit di Parma. Dipartimento di Ingegneria dell’Informazione, 2015. 158 р.
- Ruxton G.D. The unequal variance t-test is an underused alternative to Student’s t-test and the Mann Whitney U test // Behavioral Ecology. 2006. Vol. 17. No. 4. Рp. 688–690.
- Diveev A.I., Sofronova E.A. Numerical method of network operator for multiobjective synthesis of optimal control system // 2009 IEEE International Conference on Control and Automation. Christchurch, New Zealand, December 911, 2009. Рp. 701–708.
- Белоус В.В., Грошев С.В., Карпенко А.П. Веб-ориентированная среда визуализации многомерного фронта Парето // Информационные и математические технологии в науке и управлении. 2017. №1 (5). С. 94–101.
- Dr o J., Siarry P. Hybrid Continuous Interacting Ant Colony aimed at enhanced global optimization // Algorithmic Operations Research. 2007. Vol. 2. Рp. 52–64.
- Bongartz I., Conn A.R., Gould N., Toint Ph. L. CUTE: Constrained and unconstrained testing environment // ACM Transactions on Mathematical Software (TOMS). 1995. Vol. 21. No. 1. Рp. 123–160.
- Mersmann O. et al. Exploratory landscape analysis // 13th Annual conference on Genetic and evolutionary computation. ACM, Dublin, Ireland, 2011. Рp. 829–836.
- Agasiev T. Characteristic feature analysis of continuous optimization problems based on Variability Map of objective function for optimization algorithm configuration // Open Computer Science. 2020. Vol. 10. No. 1. Рp. 97–111.
- Civicioglu P. Backtracking search optimization algorithm for numerical optimization problems // Applied Mathematics and Computation. 2013. Vol. 219. Рp. 8121–8144.
- Hansen N., Ostermeier A. Completely derandomized self-adaptation in evolution strategies // Evolutionary Computation. 2001. Vol. 9(2). Рр. 159–195.
- Hansen N. The CMA evolution strategy: a comparing review / Towards a new evolutionary computation. Advances on estimation of distribution algorithms // Editors: Lozano J.A., Larranaga P., Inza I., Bengoetxea E. Springer, 2006. Рp. 75102.
- Саймон Д. Алгоритмы эволюционной оптимизации / пер. с англ. А.В. Логунова. М.: ДМК Пресс, 2020. 940 с.
- Wei-wu L., Wang H., Zou Z.J. Function optimization method based on bacterial colony chemotaxis // Journal of Circuits and Systems. 2005. Vol. 10. Рp. 58–63.
- Niu B., Wang H. Bacterial colony optimization // Discrete Dynamics in Nature and Society. 2012. Рp. 1–28.
- Passino K.M. Biomimicry of bacterial foraging for distributed optimization and control // IEEE Control System Magazine. 2002. Vol. 22. Рp. 52–67.
- Cuevas E. Social-Spider Algorithm for Constrained Optimization / Advances of Evolutionary Computation: Methods and Operators / Studies in Computational Intelligence 629. Springer International Publishing Switzerland, 2016. Рp. 175–202.
- Bilchev G.A., Parmee I.C. The ant colony metaphor for searching continuous design spaces // Lecture Notes in Computer Science. 1995. Vol. 993. Рp. 25–39.
- Dr o J., Siarry P. Continuous interacting ant colony algorithm based on dense heterarchy // Future Generation Computer Systems. 2004. Vol. 20. No. 5. Рp. 841–856.
- Hu X.-M., Zhang J., Li Y. Orthogonal methods based ant colony search for solving continuous optimization problems // Journal of computer science and technology. 2008. No. 23 (1). Рp. 2–18.
- Dr o J., Siarry P. An ant colony algorithm aimed at dynamic continuous optimization // Applied Mathematics and Computation. 2006. Vol. 181. Issue 1. Рp. 457–467.
- Karaboga D., Basturk B. A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm // Journal of Global Optimization. 2007. Vol. 39. Рp. 459–471.
- Pham D.T., Ghanbarzadeh A., Ko E., Otri S., Rahim S., Zaidi M. The Bees Algorithm / Technical Note, Manufacturing Engineering Centre, Cardiff University, UK, 2005. 40 p.
- Akbari R., Mohammadi A., Ziarati K. A novel bee swarm optimization algorithm for numerical function optimization // Communications in Nonlinear Science and Numerical Simulation. 2010. Vol. 15. Issue 10. Рp. 3142–3155.
- Maia R.D., De Castro L.N., Caminhas W.M. OptBees — A Bee-inspired Algorithm for Solving Continuous Optimization Problems / BRICS Congress on Computational Intelligence & 11th Brazilian Congress on Computational Intelligence, 2013. Рp. 142–151.
- Tseng L.-Y., Chen C. Multiple trajectory search for large scale global optimization / 2008 IEEE Congress on Evolutionary Computation, CEC (IEEE World Congress on Computational Intelligence), 2008. Рp. 3052–3059.
- Tang K. et al. Benchmark functions for the CEC’2008 special session and competition on large scale global optimization / Tech report, Nature Inspired Computation and Applications Laboratory, USTC, China, 2007. 18 p.
- ZhaoHui C., HaiYan T. Cockroach swarm optimization / 2th International Conference on Computer Engineering and Technology (ICCET ’10). 2010. Vol. 6. Рp. 652–655.
- Havens T.C., Spain C.J., Salmon N.G., Keller J.M. Roach infestation optimization / 2008 IEEE Swarm Intelligence Symposium, St. Louis, MO, USA. Рp. 21–23.
- Yang X.-S. Firefly algorithm, L vy flights and global optimization / M. Bramer (Ed.) Research and development in intelligent systems. London, UK: Springer-Verlag. 2010. Vol. 26. Рp. 209–218.
- Krishnanand K.N., Ghose D. Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions // Swarm Intelligence. 2009. No. 3. Рp. 87–124.
- Yang X.-S. A new metaheuristic bat-inspired algorithm / Nature inspired cooperative strategies for optimization (NISCO 2010), Studies in Computational Intelligence, SCI 284. Berlin: Springer, 2010. Рp. 65–74.
- Chu S.-C., Tsai P.-W. Computational intelligence based on the behavior of cats // International Journal of Innovative Computing, Information and Control. 2007. Vol. 3. No. 1. Рp. 163–173.
- Zhao R., Tang W. Monkey Algorithm for Global Numerical Optimization // Journal of Uncertain Systems. 2008. Vol. 2. No. 3. Рp. 165–176.
- Liu C.-Y., Yan X.-H., Wu H. The Wolf Colony Algorithm and Its Application // Chinese Journal of Electronics. 2011. Vol. 20. Issue 2. Рp. 212–216.
- Wu H.-S., Zhang F.-M. Wolf Pack Algorithm for Unconstrained Global Optimization // Mathematical Problems in Engineering. 2014. Vol. 19. Рp. 1–17.
- Yang X.-S., Deb S. Cuckoo search via L vy flights / World Congress on Nature and Biologically Inspired Computing (NaBIC), 2009, India: IEEE. Рp. 210–214.
- Sun J., Lei X. Geese-inspired hybrid particle swarm optimization algorithm for traveling salesman problem / International Conference on Artificial Intelligence and Computational Intelligence, 2009. Рp. 134–138.
- Neshat M., Sepidnam G., Sargolzaei M. Swallow swarm optimization algorithm: a new method to optimization / London, UK: Springer-Verlag. 2012. Vol. 25. Рp. 429–454.
- Aghababa M.P., Akbari M.E., Shotorbani A.M. An Efficient Modified Shuffled Frog Leaping Optimization Algorithm // International Journal of Computer Applications. 2011. Vol. 32. No. 1. Рp. 26–30.
- Li X.-L., Qian J.-X. Studies on artificial fish swarm optimization algorithm based on decomposition and coordination techniques // Journal of Circuits and Systems. 2003. Vol. 8. No. 1. Рp. 1–6.
- Neshat M. et al. A review of artificial fish swarm optimization methods and applications // International Journal on Smart Sensing and Intelligent Systems. 2012. Vol. 5. No. 1. Рp. 107–148.
- Bastos-Filho C.J.A. et al. Fish school search / In: Nature-Inspired Algorithms for Optimization. SCI. Springer, Heidelberg. 2009. Vol. 193. Рp. 261–277.
- Yang X.-S. Flower Pollination Algorithm for Global Optimization / International Conference on Unconventional Computing and Natural Computation (UCNC 2012): Lecture Notes in Computer Science book series. Vol. 7445. Рp. 240–249.
- Mehrabian A.R., Lucas C. A novel numerical optimization algorithm inspired from weed colonization // Ecological informatics. 2006. Nо. 1. Рp. 355–366.
- Premaratne U., Samarabandu J., Sidhu T. A new biologically inspired optimization algorithm / 4th International Conference on Industrial and Information Systems (ICIIS). Sri Lanka, 2009. Рp. 279–284.
- Karci A., Alatas B. Thinking capability of saplings growing up algorithm / Intelligent Data Engineering and Automated Learning (IDEAL 2006): Lecture Notes in Computer Science book series. 2006. Vol. 4224. Рp. 386–393.
- Бар-Ор Р.Л., Берсини У., Ватанабе Ю. Искусственные иммунные системы и их применение / под ред. Д. Дасгупты / пер. с англ. А.А. Романюхи, С.Г. Руднева. М.: Физматлит, 2006. 344 с.
- MacArthur R.H., Wilson E.O. The Theory of Island Biogeography / Princeton University Press, 1967. 224 p.
- Ma H., Fei M., Simon D.J., Chen Z. Biogeography-based optimization in noisy environments // Transactions of the Institute of Measurement and Control. 2014. Vol. 37(2). Рp. 190–204.
- Cuevas E. et al. An Algorithm for Global Optimization Inspired by Collective Animal Behavior // Discrete Dynamics in Nature and Society. 2012. Vol. 2012. 24 p. (http://dx.doi.org/10.1155/2012/638275).
- Cheng J., Zhang G., Zeng X. A Novel Membrane Algorithm Based on Differential Evolution for Numerical Optimization // International Journal of Unconventional Computing. 2011. Vol. 7. Рp. 159–183.
- Paun G. Computing with membranes // Journal of Computer and System Sciences. 2000. Vol. 61. Issue 1. Рp. 108–143.
- He S., Wu Q.H., Saunders J.R. Group Search Optimizer: an Optimization Algorithm Inspired by Animal Searching Behavior // IEEE Transactions on Evolutionary Computation, 2009. Vol. 13. No. 5. Рp. 973–990.
- Oftadeh R., Mahjoob M.J., Shariatpanahi M. A novel meta-heuristic optimization algorithm inspired by group hunting of animals: Hunting search // Computers and Mathematics with Applications. 2010. Vol. 60. Рp. 2087–2098.
- Gandomi A.H., Alavi A.H. An introduction of Krill Herd algorithm for engineering optimization // Journal of Civil Engineering and Management. 2016. Vol. 22 (3). Рp. 302–310.
- https://ru.wikipedia.org/wiki/Стволовые_клетки
- Taherdangkoo M., Yazdi M., Bagheri M.H. Stem cells optimization algorithm / ICIC 2011, Berlin: Springer, 2011. Vol. 6840. Рp. 394–403.
- Salem S.A. BOA: A novel optimization algorithm / International Conference on Engineering and Technology (ICET), Egypt, 2012. Рp. 1–5.
- Yan G.-W., Hao Z.-J. A novel optimization algorithm based on atmosphere clouds model // International Journal of Computational Intelligence and Applications. 2013. Vol. 12. No. 1. Рp. 1–16.
- Kanagasabai L., Reddy B.R., Kalavathi M.S. Atmosphere Clouds Model Algorithm for Solving Optimal Reactive Power Dispatch Problem // Indonesian Journal of Electrical Engineering and Informatics (IJEEI). 2014. Vol. 2. No. 2. Рp. 76–85.
- Xie L.-P., Zeng J.-C. A global optimization based on physicomimetics framework / IEEE First ACM/SIGEVO Summit on Genetic and Evolutionary Computation (GEC), Shanghai, China, 2009. Рp. 609–616.
- Xie L.-P., Zeng J.-C. An extended artificial physics optimization algorithm for global optimization problems / IEEE 4th International Conference on Innovative Computing, Information and Control (ICICIC), 2009, Kaohsiung Taiwan. Рp. 881–884.
- Erol O.K., Eksin I. A new optimization method: Big bang–Big crunch // Advances in Engineering Software. 2006. Vol. 37. Рp. 106–111.
- Kaveh A., Talatahari S. A novel heuristic optimization method: charged system search // Acta Mechanica. 2010. Vol. 213. No. 3-4. Рp. 267–289.
- Birbil I., Fang S.-C. An electromagnetism-like mechanism for global optimization // Journal of Global Optimization. 2003. Vol. 25. Рp. 263–282.
- Abdechiri M., Meybodi M.R., Bahrami H. Gases Brownian Motion Optimization: an Algorithm for Optimization (GBMO) // Applied Soft Computing. 2013. Vol. 13. Issue 5. Рp. 2932–2946.
- Flores J. J., L pez R., Barrera J. Gravitational interactions optimization / Learning and intelligent optimization. Berlin Heidelberg: Springer, 2011. Рp. 226–237.
- Rashedi E., Nezamabadi-pour H., Saryazdi S. GSA: A Gravitational Search Algorithm // Information Sciences. 2009. Vol. 179. Рp. 2232–2248.
- Formato R.A. Central Force Optimization: A new metaheuristic with applications in applied electromagnetics // Progress in Electromagnetics Research. 2007. Vol. 77. Рp. 425–491.
- Chuang C.-L., Jiang J.-A. Integrated radiation optimization: Inspired by the gravitational radiation in the curvature of space-time / IEEE Congress on Evolutionary Computation (CEC), Singapore, 2007. Рp. 3157–3164.
- Tayarani-Najaran M.-H., Akbarzadeh-T. M.-R. Magnetic Optimization Algorithms a New Synthesis / Conference: Evolutionary Computation (CEC 2008), Hong Kong, China, 2008. Рp. 2664–2669.
- Tayarani-Najaran M.-H., Akbarzadeh-T. M.-R. Magnetic-inspired optimization algorithms: Operators and structures // Swarm and Evolutionary Computation 2014. Vol. 19. Рp. 82–101.
- Kaveh A., Ghazaan M.I., Bakhshpoori T. An improved ray optimization algorithm for design of truss structures // Periodica Polytechnica Civil Engineering. 2013. Vol. 57. No 2. Рp. 97–112.
- Eskandar H., Sadollah A., Bahreinejad A., Hamdi M. Water cycle algorithm – A novel metaheuristic optimization method for solving constrained engineering optimization problems // Computers and Structures. 2012. Vol. 110–111. Рp. 151–166.
- Lam Albert Y.S., Li Victor O.K. Chemical-reaction-inspired metaheuristic for optimization // IEEE Transactions on Evolutionary Computation. 2010. Vol. 14. Issue 3. Рp. 381–399.
- Lam Albert Y.S., Li Victor O.K., Yu James J.Q. Real-coded chemical reaction optimization // IEEE Transactions on Evolutionary Computation. 2012. Vol. 16. Issue 3. Рp. 339–353.
- Tan Y., Yu C., Zheng S., Ding K. Introduction to Fireworks Algorithm // International Journal of Swarm Intelligence Research. 2015. No. 4 (4). Рp. 39–70.
- Ahrari A., Atai A.A. Grenade Explosion Method – A novel tool for optimization of multimodal functions // Applied Soft Computing. 2010. Vol. 10. Рp. 1132–1140.
- Geem Z.W. State-of-the-Art in the Structure of Harmony Search Algorithm / Recent Advances in Harmony Search Algorithm. Berlin: Springer-Verlag, 2010. Рp. 1–10.
- Ashrafi S.M., Dariane A.B. A novel and effective algorithm for numerical optimization: Melody Search (MS) / 11th International Conference on Hybrid Intelligent Systems (HIS), Malacca, Malaysia, 2011. Рp. 109–114.
- Ashrafi S.M., Ashrafi S.F., Moazami S. Developing Self-adaptive Melody Search Algorithm for Optimal Operation of Multi-reservoir Systems // Journal of Hydraulic Structures. 2017. Vol. 3. No. 1. Рp. 35–48.
- Mora-Guti rrez R.A., Ram rez-Rodr guez J., Rinc n-Garc a E.A. An optimization algorithm inspired by musical composition // Artificial Intelligence Review. 2014. Vol. 41. No. 3. Рp. 301–315.
- Chen T., Guo W., Gao Z. An improved artificial searching swarm algorithm and its performance analysis // Applied Mathematics. 2012. Vol. 3. No. 10A. Рp. 1435–1441.
- Song Z., Peng J., Li C., Liu P.X. A Simple Brain Storm Optimization Algorithm with a Periodic Quantum Learning Strategy // Access IEEE. 2018. Vol. 6. Рp. 19968–19983.
- Zhang T., Yang C., Zhao X. Using Improved Brainstorm Optimization Algorithm for Hardware/Software Partitioning // Applied Sciences, 2019. Vol. 9 (5). No. 866; doi:10.3390/app9050866
- Zhang X., Chen W., Dai C. Application of oriented search algorithm in reactive power optimization of power system / Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT 2008). Nanjing, China, 2008. Рp. 2856–2861.
- Al-Rifaie M.M., Bishop J.M. Stochastic Diffusion Search Review // Paladyn, Journal of Behavioral Robotics. 2013. Vol. 4. Issue 3. Рp. 155–173.
- Dai C., Chen W., Song Y., Zhu Y. Seeker optimization algorithm: A novel stochastic search algorithm for global numerical optimization // Journal of Systems Engineering and Electronics. 2010. Vol. 21. No. 2. Рp. 300–311.
- Zheng Y., Chen W., Dai C., Wang W. Stochastic focusing search: A novel optimization algorithm for real-parameter optimization // Journal of Systems Engineering and Electronics. 2009. Vol. 20. No. 4. Рp. 869–876.
- Ardjmand E., Amin-Naseri M.R. Unconscious search: A new structured search algorithm for solving continuous engineering optimization problems based on the theory of psychoanalysis / International Conference in Swarm Intelligence ICSI 2012: Advances in Swarm Intelligence. Lecture Notes in Computer Science, Springer, Berlin, Heidelberg. Vol. 7331. Рp. 233–242.
- Chen T., Wang Y., Li J. Artificial tribe algorithm and its performance analysis // Journal of Software. 2012. Vol. 7. Рp. 651–656.
- Daskin A., Kais S. Group leader’s optimization algorithm // Molecular Physics, Taylor & Francis. 2011. Vol. 109. Рp. 761–772.
- Abdollahi M., Isazadeh A., Abdollahi D. Imperialist competitive algorithm for solving systems of nonlinear equations // Computers and Mathematics with Applications. 2013. Vol. 65 (12). Рp. 1894–1908.
- Ray T., Liew K.M. Society and civilization: an optimization algorithm based on the simulation of social behavior // IEEE Transactions on Evolutionary Computation. 2003. Vol. 7 (4). Рp. 386–396.
- Cui Z., Cai X., Shi Z. Social emotional optimization algorithm with group decision // Scientific Research and Essays. 2011. Vol. 6 (22). Рp. 4848–4855.
- Sun C., Sun Y., Wang W. A Survey of MEC: 1998-2001 / 2002 IEEE International Conference on Systems, Man and Cybernetics. 2002. Vol. 6. Рp. 445–453.
- Zelinka I. SOMA – Self-Organizing Migrating Algorithm // New Optimization Techniques in Engineering. Studies in Fuzziness and Soft Computing: Springer, Berlin, Heidelberg. 2004, Vol. 141. Рp. 167–217.
- Kashan A.H. League Championship Algorithm: A new algorithm for numerical function optimization / International Conference of Soft Computing and Pattern Recognition, Malacca, Malaysia, 2009. Рp. 43–48.
- Rao R.V., Savsani V.J., Vakharia D.P. Teaching–Learning–Based optimization: A novel method for constrained mechanical design optimization problems // Computer-Aided Design. 2011. Vol. 43. Issue 3. Рp. 303–315.
- Kennedy J., Eberhart R. Particle swarm optimization / IEEE International conference on Neural Networks. 1995. Vol. 4. Рp. 1942–1948.
- Kennedy J., Mendes R. Population structure and particle swarm performance / 2002 Evolutionary Computation Congress. Washington, IEEE Computer Society. Vol. 2. Рp. 1671–1676.
- Карпенко А.П., Селиверстов Е.Ю. Глобальная оптимизация методом роя частиц. Обзор // Информационные технологии. 2010. № 2. С. 25–34.
- Tamura K., Yasuda K. Primary Study of Spiral Dynamics Inspired Optimization // IEEJ Transactions on Electrical and Electronic Engineering, 2011. Рp. 98–100.
- Tamura K., Yasuda K. Spiral Dynamics Inspired Optimization // Journal of Advanced Computational Intelligence and Intelligent Informatics, 2011. Vol. 15 (8). Рp. 1116–1122.
- Hasan ebi O., Kazemzadeh Azad S. An efficient metaheuristic algorithm for engineering optimization: SOPT // International Journal of Optimization in Civil Engineering. 2012. Vol. 2 (4). Рp. 477–489.
- Koohestani K., Kazemzadeh Azad S. An Adaptive Real-Coded Genetic Algorithm for Size and Shape Optimization of Truss Structures / First International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering, Civil-Comp Press, Stirlingshire, UK, 2009, Paper 13.
- Glover F., Laguna M., Marti R. Fundamentals of Scatter Search and Path Relinking // Control and Cybernetics. 2000. Vol. 29. No. 3. Рp. 653–684.
- Puris A., Bello R., Molina D., Herrera F. Variable mesh optimization for continuous optimization problems // Soft Computing, 2012. Vol. 16. Issue 3. Рp. 511–525.
- Navarro R., Bello R., Falcon R., Abraham A. Niche-Clearing-based Variable Mesh Optimization for multimodal problems / World Congress on Nature and Biologically Inspired Computing (NaBIC), 2013. Рp. 161–168.