Article

Article title GENERATION OF BIOINSPIRED SEARCH PROCEDURES FOR OPTIMIZATION PROBLEMS
Authors D.V. Zaruba, D.Yu. Zaporozgets
Section SECTION I. EVOLUTIONAL SIMULATION
Month, Year 06, 2016 @en
Index UDC 004.896
DOI
Abstract The paper deals with the problem of using the principles of behavior of wildlife objects to solve NP-complete optimization problems. The most promising deals with methods and algorithms based on swarm intelligence. To solve the problem of the balance between the rate of convergence and the breadth of the search mechanisms of adaptation are applied. adaptation techniques, the regular change in the algorithm tuning parameters values in a way that ensures a gradual transition from the diversification in the early stages of bioinspired algorithm to intensify in the final iteration. The paper proposes a new hybrid generation subsystem of algorithms based on bioinspired search methods. The architecture of a subsystem, including a data control about the algorithm of the problem, as well as the module automate the process of generating new search procedures. The paper proposes a new optimization approach based on the mechanism of hybridization of different atomic search procedures. This module is based on the mechanisms of genetic research and genetic programming. To ensure the efficiency of the module generating new solutions by the author constructed new mechanisms of encoding and decoding of a standardized presentation of the optimization algorithm. They allow you to provide standard descriptions in the form of alternative solutions (chromosomes). The paper proposes a new approach to the description of the data structure based on the XML description. As a data structure in the chromosome are encouraged to use reverse Polish notation (form of the expression, in which the operands are located in front of characters operators). The result of this module, and subsystems as a whole is an executable algorithm for solving optimization problems.

Download PDF

Keywords Swarm optimization methods; atomic search procedure; bioinspired search; XML; reverse Polish notation.
References 1. Karpenko A.P. Sovremennye algoritmy poiskovoy optimizatsii. Algoritmy, vdokhnovlennye prirodoy: ucheb. posobie [Modern algorithms of search engine optimization. Algorithms inspired by nature: a training manual]. Moscow: Izd-vo MGTU im. N.E. Baumana, 2014, 446 p.
2. Bel'kov V.N., Lanshakov V.L. Avtomatizirovannoe proektirovanie tekhnicheskikh sistem: ucheb. posobie [Automated design of technical systems: textbook]. Moscow: Izd-vo "Akademiya estestvoznaniya", 2009, 143 p.
3. Zaporozhets D., Zaruba D.V., Kureichik V.V. Hybrid bionic algorithms for solving problems of parametric optimization, World Applied Sciences Journal, 2013, No. 23 (8), pp. 1032-1036.
4. Kureychik V.V., Zaruba D.V., Zaporozhets D.Yu. Bioinspirirovannyy algoritm komponovki blokov EVA na osnove modifitsirovannoy raskraski grafa [Bioinspired approach to partitioning of ece schemes components problem based on the modified graph coloring], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2015, No. 4 (165), pp. 6-14.
5. Klag U., Kammings M. Osnovy genetiki [The basics of genetics]. Moscow: Tekhnosfera, 2007, 896 p.
6. Spetsifikatsiya yazyka opisaniya skhem XML (XML Schema Definition – XSD) [Specification description language XML schema (XML Schema Definition – XSD)]. Available at: http://www.w3.org/2001XMLShcema.
7. Gladkov L.A., Kureychik V.V., Kureychik V.M. Geneticheskie algoritmy [Genetic algorithms]. Moscow: Fizmatlit, 2010, 368 p.
8. Gladkov L.A., Kureychik V.V., Kureychik V.M., Sorokoletov P.V. Bioinspirirovannye metody v optimizatsii [Bioinspired methods in optimization]. Moscow: Fizmatlit, 2009, 384 p.
9. Kureychik V.M., Polkovnikova N.A. Mnogokriterial'naya optimizatsiya na osnove
evolyutsionnykh algoritmov [Multiobjective optimization on the base of evolutionary algorithms], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Science], 2015, No. 2 (163), pp. 149-162.
10. Kureychik V.M., Kureychik V.V. Geneticheskie algoritmy v kombinatorno-logicheskikh zadachakh iskusstvennogo intellekta [Genetic algorithms in combinatorial-logical problems of the artificial intelligence], Izvestiya TRTU [Izvestiya TSURE], 1999, No. 3 (13), pp. 126-128.
11. Kureychik V.V., Kureychik V.M., Rodzin S.I. Teoriya evolyutsionnykh vychisleniy. Nauchnoe izdanie [The theory of evolutionary computing. Scientific publication], under ed. V.M. Kureychika. Moscow: Fizmatlit, 2012, 260 p.
12. Zaporozhets D.Yu., Zaruba D.V., Lezhebokov A.A. Ob odnom sposobe kodirovaniya resheniya dlya zadachi razmeshcheniya [A method of coding solutions for solving problems placement], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Science], 2012, No. 11 (136), pp. 183-188.
13. Kureychik V.V., Zaruba D.V., Zaporozhets D.Yu. Ierarkhicheskiy podkhod pri razmeshchenii komponentov SBIS [Hierarchical approach for VLSI components placement], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Science], 2014, No. 7 (156), pp. 75-84.
14. Zaporozhets D.U., Zaruba, D.V., Kureichik, V.V. Representation of solutions in genetic VLSI placement algorithms, IEEE East-West Design & Test Symposium – (EWDTS’2014) Kiev, Ukraine, 2014, pp. 1-4.
15. Kureychik V.V., Zaruba D.V., Zaporozhets D.Yu. Algoritm parametricheskoy optimizatsii na osnove modeli povedeniya roya svetlyachkov [Parametric optimization algorithm based on the model of glowworm swarm behavior], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Science], 2015, № 6 (167), pp. 6-15.
16. Kureichik, V.V., Kureichik, V.V. Jr., Zaruba, D.V. Partitioning of ECE schemes components based on modified graph coloring algorithm. IEEE.
17. Alpert C.J., Dinesh P.M., Sachin S.S. Handbook of Algorithms for Physical design Automation, Auerbach Publications Taylor & Francis Group, USA, 2009. East-West Design & Test Symposium – (EWDTS’2014) Kiev, Ukraine, 2014, pp. 1-4.
18. Zaporozhets D.Yu., Kudaev A.Yu., Lezhebokov A.A. Mnogourovnevyy algoritm resheniya zadachi parametricheskoy optimizatsii na osnove bioinspirirovannykh evristik [A multilevel algorithm for solving the problem of parametric optimization based on bio-inspired heuristics], Izvestiya Kabardino-Balkarskogo nauchnogo tsentra RAN [Izvestiya of Kabardino-Balkar scientific centre of the RAS], 2013, No. 4 (54), pp. 21-28.
19. Rastrigin L.A. Random Search in Evolutionary Computations, Proceedings 1st International conf., Evolutionary Computation and Its Application, EvCA’ 96. Moscow, 1996, pp. 135-143.
20. Kuliev E.V., Lezhebokov A.A., Dukkardt A.N. Podkhod k issledovaniyu okrestnostey v roevykh algoritmakh dlya resheniya optimizatsionnykh zadach [Neural network technologies, fuzzy clustering and genetic algorithms in expert system], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Science], 2014, No. 7 (156), pp. 15-26.

Comments are closed.