—тать€ '“еоретические вопросы и современные проблемы развити€ когнитивных биоинспирированных алгоритмов оптимизации (обзор)' - журнал ' ибернетика и программирование' - NotaBene.ru
Journal Menu
> Issues > Rubrics > About journal > Authors > About the Journal > Requirements for publication > Council of Editors > Peer-review process > Policy of publication. Aims & Scope. > Article retraction > Ethics > Online First Pre-Publication > Copyright & Licensing Policy > Digital archiving policy > Open Access Policy > Open access publishing costs > Article Identification Policy > Plagiarism check policy
Journals in science databases
About the Journal

ѕубликаци€ за 72 часа - теперь это реальность!
ѕри необходимости издательство предоставл€ет авторам услугу сверхсрочной полноценной публикации. ”же через 72 часа стать€ по€вл€етс€ в числе опубликованных на сайте издательства с DOI и номерами страниц.
ѕо первому требованию предоставл€ем все подтверждающие публикацию документы!
MAIN PAGE > Back to contents
Cybernetics and programming

Theoretical issues and modern problems concerning development of cognitive bioinspiral optimization algorithms (a survey).

Rodzin Sergey Ivanovich

PhD in Technical Science

Professor, Department of Software and Computer Usage, Southern Federal University

347928, Russia, Rostovskaya oblast', g. Taganrog, ul. Chekhova, 80-1

ƒругие публикации этого автора

Kureichik Vladimir Viktorovich

Doctor of Technical Science

Professor, Department of Computer Aided Design, Southern Federal University

347928, Russia, Rostovskaya oblast', g. Taganrog, per. Nekrasovskii, 44, of. G-435

ƒругие публикации этого автора




Review date:


Publish date:


Abstract: An overview concerns topical issues and the current situation regarding cognitive bioinspiral optimization algorithms research. Optimization problems form the majority among the many problems, which are faced by the researchers in the theoretical sphere as well as in the sphere of practical application. For some such problems the solution requires a full search for options. However, the dimensions of these problems are such that the implementation of the search for options is almost impossible  due to  the extremely high time costs. An alternative approach to solving these problems involves the application of methods based on the methodology of cognitive bioinspiral algorithms. When the computer systems became sufficiently fast and inexpensive, the bioengineered algorithms formed an important tool for finding solutions close to optimal solutions for the problems,which were previously been considered insoluble. The methodological and theoretical basis of the survey was found in the provisions of the theory of artificial intelligence and bioinspired computing, decision theory and optimization methods. The review includes a list of world scientific schools and scientists who have made a significant contribution to the development of cognitive bioinspiral algorithms, and also a brief description of the classification, terminology and libraries of bioengineered algorithms. A classical result is presented in the theory of cognitive bioinspiral algorithms - the CPT theorem and the NFL-theorem. The authors provide analysis of regularities, basic elements and structure of cognitive bioinspired calculations, they analyze the issues concerning  representation (coding) of solutions, basic cycle of bioinspired algorithms, extension of cognitive capabilities of operators of bioinspiral algorithms, and drift analysis as a promicing direction in  the sphere of  time of cognitive bioinspiral algorithms analysis.

Keywords: metaheuristics, fitness function, evolutionary computation, evolution operator, NFL-theorem, drift analysis, optimization, modeling, programming, cognitive bioinspired algorithm
This article written in Russian. You can find full text of article in Russian here .

Rastrigin L.A. The convergence of the random search method in the extremal control of a many parameter system // Automation and remote control. Ц 1963. Ц No. 24(10). Ц P. 1337Ц1342.
Nelder J.A., Mead R. A simplex method for function minimization // Computer journal. Ц 1965. Ц No. 7. Ц P. 308Ц313.
Fogel L., Owens A.J., Walsh M.J. Artificial intelligence through simulated evolution. ЦWiley, 1966. Ц 452 r.
Kernighan B.W., Lin S. An efficient heuristic procedure for partitioning graphs // Bell system technical journal. Ц 1970. Ц No. 49. Ц P. 291Ц307.
Holland J.H. Adaptation in natural and artificial systems. Ц Uni of Michigan press, 1975.
Smith S.F. A Learning system based on genetic adaptive algorithms. Ц PhD thesis, Uni of Pittsburgh, 1980.
Kirkpatrick S., Gelatt Jr. C.D., Vecchi M.P. Optimization by simulated annealing // Science. Ц 1983. Ц No. 220. Ц P. 671Ц680.
Glover F. Future paths for integer programming and links to artificial intelligence // Computers and operations research. Ц 1986. Ц No. 13. Ц P.533Ц549.
Moscato P. On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms // Caltech concurrent computation program, 1989. Ц Report 826.
Dorigo M. Optimization, learning and natural algorithms // PhD thesis, Politecnico di Milano, Italy, 1992. Ц 152 r.
Wolpert D.H., Macready W.G. The no free lunch theorems for optimization // IEEE Trans. evol. comp. Ц 1997. Ц vol.1. Ц No. 1. Ц P.67Ц82.
Petrovskii A.B. Teoriya prinyatiya reshenii. Ц M.: Akademiya. Ц 400 s.
Eremeev A.P., Vagin V.N. A real-time decision support system prototype for management of a power block // Int. jour. information theories & applications. Ц 2003. Ц vol. 10. Ц No.3. Ц P. 248Ц255.
Gorodetskii V.I., Karsaev O.V., Samoilov V.V., Serebryakov S.V. Prikladnye mnogoagentnye sistemy gruppovogo upravleniya // Intellektual'nye sistemy. Ц 2009. Ц є 2. Ц S. 3Ц24.
Smirnov A.V. i dr. Kontekstno-upravlyaemaya podderzhka prinyatiya reshenii v raspredelennoi informatsionnoi srede // Informatsionnye tekhnologii i vychislitel'nye sistemy. Ц 2009. Цє 1. Ц S. 38Ц48.
Bianchi L., Dorigo M., Gambardella L.M., Gutjahr W.J. A survey on metaheuristics for stochastic combinatorial optimization // Natural computing: an int. jour. Ц 2009. Ц No.8. Ц P. 239Ц287.
Abraham A., Grosan G., Ramos V. Swarm intelligence in data mining. Ц Berlin-Heidelberg: Springer verlag, 2006. Ц DOI 10.1007 / 978-3-540-34956-3.
Goncalves G., Allaoui H., Kurejchik V. Hybrid parallel genetic approach for one-dimensional bin packing problem // 23rd european conf. of operational research, Bonn, 2009. Ц P. 202Ц208.
Blum C., Roli A. Metaheuristics in combinatorial optimization: overview and conceptual comparison // ACM computing surveys. Ц 2003. Ц No. 35. Ц P. 268Ц308.
Glover F., Kochenberger G.A. Handbook of metaheuristics. Ц Springer, 2010. Ц 648 p.
Talbi E.-G. Metaheuristics: from design to implementation. Ц Wiley, 2009. Ц 596 p.
Tomoiaga B., et. Pareto optimal reconfiguration of power distribution systems using a genetic algorithm based on NSGA-II // Energies. Ц 2013. Ц No. 6. Ц P. 1439Ц1455.
Yang X.S. Metaheuristic optimization // Scholarpedia. Ц 2011. Ц No. 6(8): 11472.
Auger A., Teytaud O. Continuous lunches are free plus the design of optimal optimization algorithms. Ц Springer-verlag, 2013. Ц P. 121Ц146.
Kaucic M. A multi-start opposition-based particle swarm optimization algorithm with adaptive velocity for bound constrained global optimization // Jour. glob. optimization. Ц 2013. Ц No. 55. Ц R. 165Ц188.
Qin A.K., Forbes F. Dynamic regional harmony search with opposition and local learning // Proc. of 13th annual conf. on genetic and evolutionary computation, Dublin, Ireland, 2011. Ц R. 53Ц54.
Yang X.J., Huang Z.G. Opposition-based artificial bee colony with dynamic cauchy mutation for function optimization // Int. jour. adv. computer technol. Ц 2012. Ц No. 4. Ц R. 56Ц62.
Ergezer M., Sikder I. Survey of oppositional algorithms // Proc. of int. conf. on computer and information technology, Dhaka, Bangladesh, 2011. Ц R. 623Ц628.
Kureichik V.V., Kureichik V.M., Rodzin S.I. Teoriya evolyutsionnykh vychislenii. Ц M.: Fizmatlit, 2012. Ц 260 s.
Rodzin S. Smart dispatching and metaheuristic swarm flow algorithm // Jour. of computer and systems sciences international. Ц 2014. Ц vol. 53. Ц No. 1. Ц P. 109Ц115.
Dorigo M., et. A survey on metaheuristics for stochastic combinatorial optimization // Int. jour. natural computing. Ц 2009. Ц No. 8 (2). Ц P. 239Ц287.
Blum C., Roli A. Metaheuristics in combinatorial optimization: overview and conceptual comparison // ACM computing surveys. Ц 2003. Ц No. 35 (3). Ц P. 268Ц308.
Karpenko A.P. Populyatsionnye algoritmy global'noi poiskovoi optimizatsii. Obzor novykh i maloizvestnykh algoritmov // Prilozhenie k zhurnalu ЂInformatsionnye tekhnologiiї. Ц 2012. Ц є 7. Ц S. 1Ц31.
Wall M. GAlib Ц A C++ library of genetic algorithm components [elektronnyi resurs]: informatsionnyi portal Ц rezhim dostupa http://lancet.mit.edu/ga/
Merelo J.J. Library for doing evolutionary computation in Perl [elektronnyi resurs]:-rezhim dostupa http://opeal.sourceforge.net/
[Elektronnyi resurs]: Ц rezhim dostupa https://github.com/dknoester/ealib/
[Elektronnyi resurs]: Ц rezhim dostupa http://www.math.nsc.ru/AP/benchmarks/index.html
[Elektronnyi resurs]: Ц rezhim dostupa http://watchmaker.uncommons.org/
Goldberg D.E. Genetic algorithms in search, optimization, and machine learning. Ц USA: AddisonЦWesley publishing company, inc., 1989. Ц 432 r.
Gladkov L.A., Kureichik V.V., Kureichik V.M., Sorokaletov P.V. Bioinspirirovannye metody v optimizatsii. Ц M.: Fizmatlit, 2009. Ц 380 s.
Kureichik V.M., Rodzin S.I. Evolyutsionnye algoritmy: geneticheskoe programmirovanie (obzor) // Izvestiya RAN. Teoriya i sistemy upravleniya. Ц 2002. Ц є 1. Ц S. 127Ц137.
Rodzin S.I. Schemes of evolution strategies // Proc. of 2002 IEEE int. conf. on AI-systems (ICAISТ2002). Ц P. 375Ц380.
Eberhart R., Shi Yu., Kennedy J. Swarm intelligence. Ц Morgan Kaufmann, 2010. Ц 512 r.
Rutkovskaya D., Pilin'skii M., Rutkovskii L. Neironnye seti, geneticheskie algoritmy i nechetkie sistemy. Ц M.: Goryachaya liniya Ц Telekom, 2013. Ц 384 s.
Rodzin S.I. Intellektual'nye sistemy. Geneticheskie algoritmy: bazovaya kontseptsiya, kognitivnye vozmozhnosti i problemnye voprosy teorii. Ц M.: Fizmatlit, 2007. Ц S. 47Ц66.
Rodzin S., Rodzina O. New computational models for big data and optimization // Proc. of the 9th IEEE int. conf. application of information and communication technologies (AICT'2015). Ц P. 3Ц7.
Rodzin S.I., Rodzina O.N. Algoritmy biomemetiki // Obrazovatel'nye resursy i tekhnologii. Ц 2014. Ц є 2(5). Ц S. 129Ц132.
Rodzin S., Rodzina O. Metaheuristics memes and biogeography for trans computational combinatorial optimization problems // Proc. of the 6th IEEE int. conf. on cloud system and big data engineering (Confluence-2016), India, 14-15 jan., 2016.
Kureichik V.M., Rodzin S.I. Evolutionary algorithms: genetic programming // Jour. of computer and systems sciences international. Ц 2002. Ц vol. 41. Ц No. 1. Ц P. 123Ц132.
Korobeinikov A.G., Kutuzov I.M., Kolesnikov P.Yu. Analiz metodov obfuskatsii // Kibernetika i programmirovanie. Ц 2012. Ц є 1. Ц C. 31 Ц 37. URL: http://www.e-notabene.ru/kp/article_13858.html
He J., Yao X. Drift analysis and average time complexity of evolutionary algorithms // Artificial intelligence. Ц 2001. Ц vol. 127. No. 1. Ц P. 57Ц85.
Jansen T. Fixed budget computations: why, how and what? // Proc. of Dagstuhl seminar on theory of evolutionary algorithms, 2013. Ц P. 1325Ц1332.
Doerr B., Goldberg L. Adaptive drift analysis // Algorithmica. Ц 2013. Ц vol. 65, No. 1. Ц P. 224Ц250.
Rodzin S., Rodzina L. Theory of bionic optimization and its application to evolutionary synthesis of digital devices // Proc. of the 14th IEEE east-west design & test symposium (EWDTS'14), 2014. Ц P. 147Ц152
Link to this article

You can simply select and copy link from below text field.

Other our sites:
Official Website of NOTA BENE / Aurora Group s.r.o.
"History Illustrated" Website