Article

Article title DEVELOPMENT OF ALGORITHMS INSPIRED BY NATURAL SYSTEMS, FOR EFFECTIVE DECISION-MAKING TASKS CAD
Authors S.N. Shсheglov
Section SECTION III. SIMULATION AND DESIGN
Month, Year 06, 2016 @en
Index UDC 623.2.045.772.12
DOI
Abstract The paper presents the process of developing algorithms inspired by natural systems, for effective decision-making in problems of CAD. Recently started research of possibilities of applica-tion and development of algorithms inspired by natural systems, for effective decision-making in problems of CAD. Constantly there is a conflict between the complexity of CAD and the requirements of making effective decisions in real time. These problems cannot be completely solved by parallelization of the decision-making process, increase in the number of operators, users and decision makers. One possible approach to solving this problem is the use of new technologies at the intersection of computer science, bionics and computer aided design. In this regard, the development of new principles and approaches for making effective decisions in the design and control has important economic and social significance and is currently relevant and important. The purpose of the study is to assess the possibility of using integrated methods, inspired by natural systems, for solving problems of engineering design CAD on the example of use of algorithm of behavior of the pack of grey wolves in nature. Given the formulation of the problem of placing circuit elements of EVA on the set of the set of discrete positions of the working field. Presents a modified technology development of nature inspired algorithms. Shows a simplified diagram of an integrated search for solving a location of the circuit elements of computer and electronic hard-ware. Given the requirements for the construction of algorithms design, based on nature inspired techniques. Shows the main steps of the algorithm behavior of the pack of grey wolves in the context of accommodation. The comparative results of computational experiments.

Download PDF

Keywords Management; design automation; model; algorithm; optimum; computational experiments; graph models; decision making; search; system
References 1. Karpenko A.P. Sovremennye algoritmy poiskovoy optimizatsii Algoritmy, vdokhnovlennye prirodoy: uchebnoe posobie [Modern algorithms of search optimization algorithms, the inspiration provided by nature: a training manual]. Moscow: Izd-vo MGTU im. N.E. Baumana, 2014, 446 p.
2. Pospelov D.A. Dannye i znaniya. Iskusstvennyy intellect [The data and knowledge. Artificial intelligence]. In 3 book. Book 1. Moscow: Radio i svyaz', 1990, 464 p.
3. Norenkov I.P. Osnovy avtomatizirovannogo proektirovaniya: uchebnik dlya VUZov [Fundamentals of computer-aided design: textbook for Universities]. Moscow: Izd-vo MGTU im. N.E.Baumana, 2009, 432 p.
4. Kureychik V.M., Kureychik V.V. Evolyutsionnye, sinergeticheskie i gomeostaticheskie strategii v iskusstvennom intellekte: sostoyanie i perspektivy [Evolutionary, synergetic and homeostatic strategies in artificial intelligence: state and prospects], Novosti iskusstvennogo intellekta [AI News], 2000, No. 3, pp. 39-67.
5. Malyshev V.V., Piyavskiy B.S., Piyavskiy S.A. Metody prinyatiya resheniy v usloviyakh mnogoobraziya sposobov ucheta neopredelennosti [Decision-making methods in a variety of ways of accounting for the uncertainty], Izvestiya RAN. Teoriya i sistemy upravleniya [Journal of Computer and Systems Sciences International], 2010, No. 1, pp. 46-61.
6. Kureychik V.M., Lebedev B.K., Lebedev V.B. Planirovanie sverkhbol'shikh integral'nykh skhem na osnove integratsii modeley adaptivnogo poiska [Planning ultralarge integrated circuits on the basis of integration models adaptive search], Izvestiya RAN. Teoriya i sistemy upravleniya [Journal of Computer and Systems Sciences International], 2013, No. 1, pp. 84-101.
7. Mirjalili S., Lewis A. Grey Wolf Optimizer - Advances in Engineering Software 69, 2014, pp. 46-61.
8. Madadi A., Motlagh M. Optimal Control of DC motor using Grey Wolf Optimizer Algorithm, Technical Journal of Engineering and Applied Science, 2014, No. 4-04, pp. 373-379.
9. Gavrilova T.A. Khoroshevskiy V.F. Bazy znaniy intellektual'nykh system [Knowledge base of intelligent systems]. St. Petersburg: Piter, 2000, 384 p.
10. Gladkov L.A, Kureichik V.V., Kravchenko Yu.A. Evolutionary Algorithm for Extremal Subsets Comprehension in Graphs, World Applied Sciences Journal, 2013, No. 24 (14).
11. Kureychik V.M., Kureychik V.V., Rodzin S.I. Kontseptsiya evolyutsionnykh vychisleniy, inspirirovannykh prirodnymi sistemami [Concept evolutionary computation is inspired by natural systems], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2009, No. 4, pp. 16-25.
12. Kureychik V.M., Lebedev B.K., Lebedev O.B. Gibridnyy algoritm razbieniya na osnove prirodnykh mekhanizmov prinyatiya resheniy [A hybrid algorithm of splitting based on natural mechanisms of decision-making], Iskusstvennyy intellekt i prinyatie resheniy [Artificial Intelligence and Decision Making], 2012, No. 2, pp. 3-15.
13. Kureichik V.V., Kureichik V.M., Sorokoletov P.V. Аnalysis and a survey of evolutionary models, Journal of Computer and Systems Sciences International, 2007, Vol. 46, No. 5, pp. 779-791.
14. Kureychik V.V., Kureychik Vl.Vl. Integrirovannyy algoritm razmeshcheniya fragmentov SBIS [Integrated vlsi fragment placement algorithm], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2014, No. 7 (156), pp. 84-93.
15. Kureychik V.M., Kureychik V.V., Rodzin S.I. Modeli parallelizma evolyutsionnykh vychisleniy [Models of parallelism in evolutionary computing], Vestnik Rostovskogo gosudarstvennogo universiteta putey soobshcheniya [Vestnik of Rostov state University of Railways], 2011, No. 3, pp. 93-97.
16. Kureychik V.V., Kureychik V.M., Gladkov L.A., Sorokoletov P.V. Bionspirirovannye metody v optimizatsii [Inspirowanie methods in optimization]. Moscow: Fizmalit, 2009, 384 p.
17. Kureychik V.M., Lebedev B.K., Lebedev O.B. Razbienie na osnove modelirovaniya adaptivnogo povedeniya biologicheskikh sistem [Partitioning based on simulation of ADAP-alternative behavior of biological systems], Neyrokomp'yutery: razrabotka, primenenie [Neurocomputers: Development, Application], 2010, No. 2, pp. 28-34.
18. Lebedev B.K., Lebedev V.B. Razmeshchenie na osnove metoda pchelinoy kolonii [Plasement on the basis of the beer colony method], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2010, No. 12 (113), pp. 12-19.
19. Gladkov L.A. Gibridnyy geneticheskiy algoritm resheniya zadachi razmeshcheniya elementov SBIS s uchetom trassiruemosti soedineniy [A hybrid genetic algorithm for solving the problem of placing of elements VLSI taking into account the traceability of compounds], Vestnik Rostovskogo gosudarstvennogo universiteta putey soobshcheniya [Vestnik of Rostov state University of Railways], 2011, No. 3, pp. 58-66.
20. Kureychik V.V., Bova V.V., Kureychik Vl.Vl. Kombinirovannyy poisk pri proektirovanii [Combined search in the design], Obrazovatel'nye resursy i tekhnologii [Educational Resources and Technology], 2014, No. 2 (5), pp. 90-94.
21. Kuliev E.V., Lezhebokov A.A. Issledovanie kharakteristik gibridnogo algoritma razmeshcheniya [Research parameters of hybrid algorithm for placement], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2013, No. 3 (140), pp. 255-261.

Comments are closed.