Authors E.V. Kuliev, D.Y. Zaporozhets, Vl.Vl. Kureichik
Month, Year 06, 2015 @en
Index UDC 004.82
Abstract In the article the problem of a combined approach to the adaptation and self-handling problem-oriented knowledge. The essence of the combined approach is consistent work bionic and genetic algorithms. Bionic algorithm is based on the use patterns of behavior of the colony of bees in the wild. This mechanism allows you to effectively solve the problem of pre-convergence of the algorithm by splitting the search space on a dynamically changing field. The search is performed in parallel in each area, which increases the speed of the algorithm. The solution of the problem demonstrated by the combined methods of exploring the region to find solutions. Presented formulation of the problem of problem-oriented knowledge of the search for optimal solutions. A generalized approach to the construction of the optimality criterion for the solution of the task of handling the problem-oriented knowledge. A modified architecture of a combined approach to solving the problem of handling the problem-oriented knowledge, based on the symbiosis of bioinspired algorithms. This architecture includes the main components of bionic research based on heuristics conduct a swarm of bees in the wild, the unit of evolutionary adaptation necessary to dynamically adjust control parameters and block the external environment, which is the decision maker. Experimental studies in which empirically been confirmed theoretical assessments of the time complexity of the algorithm. It was found that by using a combination of architecture developed, and decreases the chance factor is increased focus algorithm. Series of tests have shown that the time complexity is not beyond the polynomial dependence. At best, the time complexity of algorithms O(nlogn), in the worst case – O(n2).

Download PDF

Keywords Genetic algorithm; evolutionary algorithm; combined approach; adaptation; neighborhood; population; self-organization; problem-oriented knowledge.
References 1. Norenkov I.P., Arutyunyan N.M. Evolyutsionnye metody v zadachakh vybora proektnykh resheniy [Evolutionary techniques in the problems of choice of design solutions], Elektronnoe nauchno-tekhnicheskoe izdanie [Electronic scientific and technical edition], 2007, № 9.
2. Holland J.H. Adaptation in Natural&Artificial Systems. Ann Arbor: Uni of Michigan Press, 1975.
3. Kureichik V.V., Kureichik Vl.Vl. Bionicheskiy poisk pri proektirovanii i upravlenii [Search inspired by natural systems, for the design and managemen], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2012, No. 11 (136), pp. 178-183.
4. Kureichik V.V., Kureichik V.M., Rodzin S.I. Teoriya evolyutsionnykh vychisleniy [The theory of evolutionary computation]. Moscow: Fizmatlit, 2012, 260 p.
5. Bastos-Filho C.J.A., Lima-Neto F.B., Lins A., Nascimento, A., Lima, M. Fish School Search. Nature-inspired Algorithms for Optimization (NISCO’2010). Springer, Heidelberg, 2009, Vol. 193, pp. 261-277.
6. Kureichik V.V., Kureichik Vl.Vl. Arkhitektura gibridnogo poiska pri proektirovanii [The architecture of hybrid search for design], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2012, No. 7 (132), pp. 22-27.
7. Kureichik V.M., Lebedev B.K., Lebedev O.B. Poiskovaya adaptatsiya: teoriya i praktika [Search adaptation: theory and practice]. Moscow: Fizmatlit, 2006, 272 p.
8. Kureichik V.V., Sorokoletov P.V. Kontseptual'naya model' predstavleniya resheniy v geneticheskikh algoritmakh [Conceptual model of decisions representation in genetic algorithms], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2008, No. 9 (86), pp. 7-12.
9. 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.
10. Bova V.V., Lezhebokov A.A., Gladkov L.A. Problem-oriented algorithms of solutions search based on the methods of swarm intelligence, World Applied Sciences Journal, 2013, Vol. 27, No. 9, pp. 1201-1205.
11. Bova V.V., Kureichik V.V. Integrirovannaya podsistema gibridnogo i kombinirovannogo poiska v zadachakh proektirovaniya i upravleniya [Integrated subsystem hybrid and combined search in problems of design and management], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2010, No. 12 (113), pp. 37-42.
12. Kureichik V.V., Kureychik V.M., 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 (93), pp. 16-24.
13. Kureichik V.M. Genetic algorithms: state of the art, problems, and perspectives, Izvestiya Rossiyskoy akademii nauk. Teoriya i sistemy upravleniya [Journal of Computer and Systems Sciences International], 1999, Vol. 38, No. 1, pp. 144-161.
14. Kravchenko Yu.A., Bova V.V., Gladkov L.A., Kureichik V.V.,Kureichik V.M., Kuliev E.V., Lezhebokov A.A., Lebedev B.K., Lebedev O.B., Nuzhnov E.V., Rodzin S.I. Prinyatie resheniy, poisk i obrabotka problemno-orientirovannykh znaniy v intellektual'nykh informatsionnykh sistemakh [Decision making, search and processing of problem-oriented knowledge in intelligent information systems]. Rostov-on-Don: Izd-vo YuFU, 2014, 136 p.
15. Kureichik, V.M. Sovmestnye metody kvantovogo i bionicheskogo poiska [Joint methods in quantum and biologically inspired search], Intellektual'nye sistemy (IEE AIS'04). Intellektual'nye SAPR (CAD-2004): Tr. Mezhdunar. nauch.-tekhn. konf. [Intelligent systems (IEE AIS'04). Intelligent CAD systems (CAD-2004): proceedings of the International scientific and technical conference]. Moscow: Fizmatlit, 2004, pp. 12-19.
16. Tsoy Yu.R., Spitsyn V.G. K vyboru razmera populyatsii [The choice of the population size], Intellektual'nye sistemy (IEE AIS'04). Intellektual'nye SAPR (CAD-2004): Tr. Mezhdunar. nauch.-tekhn. konf. [Intelligent systems (IEE AIS'04). Intelligent CAD systems (CAD-2004): proceedings of the International scientific and technical conference]. Moscow: Fizmatlit, 2004, pp. 90-96.
17. Qing He, Xiu-Rong Zhao, Ping Luo, Zhong-Zhi Shi. Combination methodologies of multiagent hyper surface classifiers: design and implementation issues, Second international work-shop, AIS-ADM 2007, Proceedings. Springer Berlin Heidelberg, 2007, pp. 100-113.
18. Bova V.V., Gladkov L.A., Kravchenko Yu.A., Kureichik V.V., Kureichik V.M., Shcheglov S.N. Tekhnologii intellektual'nogo analiza i izvlecheniya dannykh na osnove printsipov evolyutsionnogo modelirovaniya [Technology mining and data extraction on the basis of the principles of the evolution of modeling]. Taganrog: Izd-vo TTI YuFU, 2009, 124 p.
19. Rodzin S.I., Rodzina L.S. Theory of Bioinspired Search for Optimal Solutions and its Application for the Processing of Problem-Oriented Knowledge, Proc. of the 8th IEEE Int. Conf. Application of Information and Communication Technologies (AICT'2014), Astana, Kazakhstan, pp. 142-146.

Comments are closed.