Geodesic
Task allocation in distributed artificial intelligence using collective behavior models
News of the Kabardin-Balkar scientific center of RAS, no. 1 (2015), pp. 16-22.

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The paper presents a modified search architecture, based on the paradigm of multi-agent approach to solving complex problems. This approach allows us to parallelize the process of finding solutions and managing the problem pre-convergence algorithms. In this paper we propose a decomposition mechanism of the original problem based on the bee algorithm to determine the most perspective solutions and neighborhoods and further search process delegation to the agents that implement various optimization techniques. Conducted series of experiments have shown the efficiency of the designed search engine, compared with the genetic, evolutionary and swarms optimization algorithms. Parallel computing application for solving optimization problems improves the quality of the obtained solutions up to 8 percent.
Keywords: multi-agent system, collective behaviour, swarm algorithm, bee’s algorithm, design engineering, parallel computing. 
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A. N. Dukkardt; M. I. Anchekov; Z. V. Nagoev; A. U. Zammoev; O. V. Nagoeva; Yu. Kh. Khamukov. Task allocation in distributed artificial intelligence using collective behavior models. News of the Kabardin-Balkar scientific center of RAS, no. 1 (2015), pp. 16-22. http://geodesic.mathdoc.fr/item/IZKAB_2015_1_a1/

[1] V. V. Bova, A. N. Dukkardt, “Primenenie iskusstvennykh neironnykh setei dlya kollektivnogo resheniya intellektualnykh zadach”, Izvestiya YuFU. Tekhnicheskie nauki. Tematicheskii vypusk «Intellektualnye SAPR», 2012, no. 7 (132), 131–138, Izd-vo TTI YuFU, Taganrog:

[2] A. N. Dukkardt, L. Z. Shautsukova, V. V. Bova, “Ispolzovanie modelei kollektivnogo povedeniya dlya resheniya zadach raspredelennogo iskusstvennogo intellekta”, Izvestiya Kabardino-Balkarskogo nauchnogo tsentra RAN, 2013, no. 4 (54), 14–20

[3] A. V. Timofeev, “Multiagentnye robototekhnicheskie sistemy i neirosetevye tekhnologii”, Izvestiya YuFU. Tekhnicheskie nauki, 104:3 (2010), 20–23

[4] Victor Lesser, Daniel Corkill, History and Accomplishments of the Multi-Agent Systems Lab at the University of Massachusetts Amherst, 2011, 1–5 pp.

[5] V. V. Bova, V. V. Kureichik, “Integrirovannaya podsistema gibridnogo i kombinirovannogo poiska v zadachakh proektirovaniya i upravleniya”, Izvestiya Yuzhnogo federalnogo universiteta. Tekhnicheskie nauki, 113:12 (2010), 37–42

[6] A. Abraham, G. Grosan, V. Ramos, “Swarm Intelligence in Data Mining”, Berlin, 2006, 267, Springer Verlag, Heidelberg

[7] D. Yu. Zaporozhets, A. Yu. Kudaev, A. A. Lezhebokov, “Mnogourovnevyi algoritm resheniya zadachi parametricheskoi optimizatsii na osnove bioinspirirovannykh evristik”, Izvestiya Kabardino-Balkarskogo nauchnogo tsentra RAN, 2013, no. 4, 21–28

[8] V. M. Kureichik, V. V. Kureichik, S. I. Rodzin, “Modeli parallelizma evolyutsionnykh vychislenii”, Vestnik Rostovskogo gosudarstvennogo universiteta putei soobscheniya, 2011, no. 3, 93–97

[9] V. V. Kureichik, D. Yu. Zaporozhets, “Roevoi algoritm v zadachakh optimizatsii”, Izvestiya Yuzhnogo federalnogo universiteta. Tekhnicheskie nauki, 108:7 (2010), 28–32

[10] V. V. Kureichik, E. E. Polupanova, “Evolyutsionnaya optimizatsiya na osnove algoritma kolonii pch?l”, Izvestiya Yuzhnogo federalnogo universiteta. Tekhnicheskie nauki, 101:12 (2009), 41–46

[11] V. V. Kureichik, V. M. Kureichik, S. I. Rodzin, Teoriya evolyutsionnykh vychislenii, FIZMATLIT., M., 2012

[12] Yu. A. Kravchenko, A. A. Lezhebokov, D. Yu. Zaporozhets, “Sposoby intellektualnogo analiza dannykh v slozhnykh sistemakh”, Izvestiya Kabardino-Balkarskogo nauchnogo tsentra RAN, 2012, no. 3, 52–57