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2018 | OriginalPaper | Buchkapitel

2. Agent-Based Optimization

verfasst von : Ewa Ratajczak-Ropel

Erschienen in: Population-Based Approaches to the Resource-Constrained and Discrete-Continuous Scheduling

Verlag: Springer International Publishing

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Abstract

Agents and agent-based approaches are an active research topis in artificial intelligence and expert systems.

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Vorheriges Kapitel Introduction
Nächstes Kapitel Project Scheduling Models
Literatur
1.
Genesereth, M.R., Ketchpel, S.P.: Software agents. Commun. ACM 37(7), 48–53 (1994)CrossRef
2.
Wooldridge, M., Jennings, N.R.: Intelligent agents: theory and practice. Knowl. Eng. Rev. 10(2), 115–152 (1995)CrossRef
3.
Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Pearson Education Inc., Prentice Hall (2010)MATH
4.
Bellifemine, F., Caire, G., Greenwood, D.: Developing Multi-agent Systems with JADE. Wiley, Chichester (2007)CrossRef
5.
Wooldridge, M.: Agent-based computing. Interoper. Commun. Netw. 1, 71–98 (1998)
6.
Niazi, M., Hussain, A.: Agent-based computing from multi-agent systems to agent-based models: a visual survey. Scientometrics 89(2), 479–499 (2011)CrossRef
7.
Weiss, G. (ed.): Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. The MIT Press, Cambridge, MA (1999)
8.
Wooldridge, M.: An Introduction to Multiagent Systems, 2nd edn. Wiley (2009)
9.
Madejski, J.: Survey of the agent-based approach to intelligent manufacturing. J. Achiev. Mater. Manuf. Eng. 21(1), 67–70 (2007)
10.
Balaji, P.G., Srinivasan, D.: An introduction to multi-agent systems. In: Innovations in Multi-agent Systems and Applications — 1, Studies in Computational Intelligence, vol. 310, pp. 1–27 (2010)
11.
Vlassis N.: A concise introduction to multiagent systems and distributed artificial intelligence. In: Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool (2007)
12.
Burke, E.K., Graham Kendall, G.: Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques. Springer, US (2014)CrossRefMATH
13.
Jennings, N.R., Wooldridge, M.: Applications of intelligent agents. In: Jennings, N.R., Wooldridge, M.J. (eds.) Agent Technology: Foundations, Applications, and Markets, pp. 3–28. Springer, Berlin (1998)CrossRef
14.
Jennings, N.: The archon system and its applications. In: Proceedings of the 2nd International Working Conference on Cooperating Knowledge Based Systems (CKBS-94), pp. 13–29. Dake Centre, University of Keele, UK (1994)
15.
Albert, M., Laengle, T., Woern, H., Capobianco, M., Brighenti, A.: Multi-agent systems for industrial diagnostics. In: Proceedings of 5th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, pp. 483–488, Washington, DC (2003)
16.
Neagu, N., Dorer, K., Greenwood, D., Calisti, M.: LS/ATN: reporting on a successful agent-based solution for transport logistics optimization. In: Proceedings of the IEEE 2006 Workshop on Distributed Intelligent Systems (WDIS06), Prague (2006)
17.
Greenwood, D., Vitaglione, G., Keller, L., Calisti, M.: Service level agreement management with adaptive coordination. In: Proceedings of the International Conference on Networking and Services (ICNS06), Silicon Valley, USA (2006)
18.
Johnson, P.G., Balke, T., Kotthoff, L.: Integrating optimisation and agent-based modelling. In: ECMS — Proceedings 28th European Conference on Modelling and Simulation, pp. 775–781. Digitaldruck Pirrot GmbH, Germany (2014)
19.
Parunak, H.V.D., Kindrick, J., Irish, B.W.: A conservative domain for neural connectivity and propagation. In: Proceedings of Proceedings of the 6th National Conference on Artificial Intelligence (AAAI’87). Distributed artificial intelligence, pp. 307–311. Pitman, London (1987)
20.
Sirikijpanichkul, A., van Dam, K.H., Ferreira, L., Lukszo, Z.: Optimizing the location of intermodal freight hubs: an overview of agent based modelling approach. J. Transp. Syst. Eng. Inf. Technol. 7(4), 71–81 (2007)
21.
22.
Böcker, J., Lind, J., Zirkler, B.: Using a multi-agent approach to optimise the train coupling and sharing system. Eur. J. Oper. Res. 131(2), 242–252 (2010)CrossRefMATH
23.
Liang, W.Y., Huang, C.C.: Agent-based demand forecast in multi-echelon supply chain. Decis. Support Syst. 42(1), 390–407 (2006)CrossRef
24.
Barbucha, D., Jędrzejowicz, P.: An agent-based approach to vehicle routing problem. In. J. Appl. Math. Comput. Sci. 4(2), 538–543 (2007)
25.
Polyakovsky, S., M’Hallah, R.: An agent-based approach to the two dimensional guillotine bin packing problem. Eur. J. Oper. Res. 192(31), 767–781 (2009)CrossRefMATH
26.
Xie, X.F., Liu, J.: Multiagent optimization system for solving the traveling salesman problem (TSP). IEEE Trans. Syst. Man Cybern. Part B Cybern. 39(2), 489–502 (2009)CrossRefMathSciNet
27.
Barbati, M., Bruno, G., Genovese, A.: Applications of agent-based models for optimization problems: a literature review. Expert Syst. Appl. 39, 6020–6028 (2012)CrossRef
28.
Knotts, G., Dror, M., Hartman, B.C.: Agent-based project scheduling. IIE Trans. 32(5), 387–401 (2000)
29.
Chen, Y.M., Wang, S.C.: Framework of agent-based intelligence system with two stage decision-making process for distributed dynamic scheduling. Appl. Soft Comput. 7(1), 229–245 (2007)CrossRefMathSciNet
30.
Xiang, W., Lee, H.P.: Ant colony intelligence in multi-agent dynamic manufacturing scheduling. Eng. Appl. Artif. Intell. 21(1), 73–85 (2008)CrossRef
31.
Ramos, C.: An architecture and a negotiation protocol for the dynamic scheduling of manufacturing systems. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 8–13 (1994)
32.
Davidsson, P., Holmgren, J., Persson, J.A.: On the integration of agent-based and mathematical optimization techniques. Lect. Notes Artif. Intell. 4496, 1–10 (2007)
33.
Chen, R.S., Tu, M.A.: Development of an agent-based system for manufacturing control and coordination with ontology and RFID technology. Expert Syst. Appl. 36(4), 7581–7593 (2009)CrossRef
34.
Persson, J.A., Davidsson, P., Johansson, S.J., Wernstedt, F.: Combining agent-based approaches and classical optimization techniques. In: Proceedings of the Third European Workshop on Multi-Agent Systems (EUMAS 2005), pp. 260–269 (2005)
35.
Talukdar, S., Baerentzen, L., Gove, A., De Souza, P.: Asynchronous Teams: Co-operation Schemes for Autonomous, Computer-Based Agents. Technical Report EDRC 18-59-96, Carnegie Mellon University, Pittsburgh (1996)
36.
Talukdar, S., Baerentzen, L., Gove, A., de Souza, P.: Asynchronous teams: cooperation schemes for autonomous agents. J. Heuristics 4(4), 295–332 (1998)CrossRefMATH
37.
Barbucha, D., Czarnowski, I., Jędrzejowicz, P., Ratajczak-Ropel, E., Wierzbowska, I.: e-JABAT — An implementation of the web-based A-Team. In: Nguyen, N.T., Jain, L.C. (eds.) Intelligence Agents in the Evolution of Web and Applications. Studies in Computational Intelligence 167, 57–86 (2009)
38.
Jędrzejowicz, P., Wierzbowska, I.: JADE-based A-Team environment. In: Computational Science — ICCS. Lecture Notes in Computer Science, vol. 3993, pp. 719–726 (2006)
39.
Talukdar, S.N., de Souza, P.: Scale efficient organizations. In: IEEE International Conference on Systems, Man, and Cybernetics, Chicago, pp. 1458–1463 (1992)
40.
Talukdar, S., Murthy, S., Akkiraju, R.: Asynchronous teams. In: Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol. 57, pp. 537–556 (2003)
41.
Correa, R., Gomes, F.C., Oliveira, C., Pardalos, P.M.: A parallel implementation of an asynchronous team to the point-to-point connection problem. Parallel Comput. 29, 447–466 (2003)CrossRefMathSciNet
42.
Zhu, Q.: Topologies of agents interactions in knowledge intensive multi-agent systems for networked information services. Adv. Eng. Inform. 20, 31–45 (2006)CrossRef
43.
Jędrzejowicz, P.: A-Teams and their applications. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. Lecture Notes in Computer Science(LNAI), vol. 5796, pp. 36–50 (2009)
44.
Carle, M.A., Martel, A., Zufferey, N.: Collaborative Agent Teams (CAT) for Distributed Multi-Dimensional Optimization. CIRRELT, CIRRELT-2012-43, (2012)
45.
Talukdar, S.N.: Collaboration rules for autonomous software agents. Decis. Support Syst. 24, 269–278 (1999)CrossRef
46.
Rachlin, J., Goodwin, R., Murthy, S., Akkiraju, R., Wu, F., Kumaran, S., Das, R.: A-Teams: an agent architecture for optimization and decision-support. In: Papadimitriou, C., Singh, M.P., Müller, J.P. (eds.) ATAL 1998. Lecture Notes in Artificial Intelligence, vol. 1555, pp. 261–276 (1999)
47.
Barbucha, D., Czarnowski, I., Jędrzejowicz, P., Ratajczak, E., Wierzbowska, I.: JADE-Based A-Team as a tool for implementing population-based algorithms. In: Chen, Y., Abraham, A. (eds.) Intelligent Systems Design and Applications, Jinan Shandong, China, pp. 144–149. IEEE, Los Alamitos (2006)
48.
Talukdar, S.N., Ramesh, V.C.: A multi-agent technique for contingency constrained optimal power flows. IEEE Trans. Power Syst. 9(2), 855–861 (1994)CrossRef
49.
Avila-Abascal, P., Talukdar, S.N.: Cooperative algorithms and abductive causal networks for the automatic generation of intelligent substation alarm processors. In: Proceedings of ISCAS’96 (1996)
50.
Kao, J.H., Hemmerle, J.S., Prinz, F.B.: Collision avoidance using asynchronous teams. In: 1996 IEEE International Conference on Robotics and Automation, vol. 2, pp. 1093–1100. OMNI Press, USA (1996)
51.
Murthy, S., Rachlin, J., Akkiraju, R., Wu, F.: Agent-based cooperative scheduling. In: Charniak, E.C. (ed.) Constraints and Agents, AAAI Technical Report WS-97-05, pp. 112–117 (1997)
52.
Blum, J., Eskandarian, A.: Enhancing intelligent agent collaboration for flow optimization of railroad traffic. Transp. Res. 36(10), 919–930 (2002)CrossRef
53.
Chen, S.Y., Talukdar, S. N., Sadeh N. M.: Job-Shop-Scheduling by a team of asynchronous agents. In: IJCAI-93 Workshop on Knowledge-Based Production, Scheduling and Control, Chambery, France (1993)
54.
Aydin, M.E., Fogarty, T.C.: Teams of autonomous agents for job-shop scheduling problems: an experimental study. J. Intell. Manuf. 15, 455–462 (2004)CrossRef
55.
Aydin, M.: Metaheuristic agent teams for job shop scheduling problems. In: Holonic and Multi-Agent Systems for Manufacturing. Lecture Notes in Computer Science, vol. 4659, pp. 185–194 (2007)
56.
Rachlin, J., Wu, F., Murthy, S., Talukdar, S., Sturzenbecker, M., Akkiraju, R., Fuhrer, R., Aggarwal, A., Yeh, J., Henry, R., Jayaraman, R.: ForestView: a system for integrated scheduling in complex manufacturing domains. IBM Report (1996)
57.
Lee, H., Murthy, S., Haider, W., Morse, D.: Primary production scheduling at steel making industries, IBM Report (1995)
58.
Tsen, C.K.: Solving train scheduling problems using A-Teams. Ph.D. dissertation, Electrical and Computer Engineering Department, CMU, Pittsburgh, PA (1995)
59.
Rabak, C.S., Sichman, J.S.: Using A-Teams to optimize automatic insertion of electronic components. Adv. Eng. Inform. 17, 95–106 (2003)CrossRef
60.
Czarnowski, I., Jędrzejowicz, P.: Agent-based NON-distributed and distributed clustering. In: Perner, P. (ed.) Machine Learning and Dara Mining in Pattern Recognition. Lecture Notes in Artificial Intelligence, vol. 5632, pp. 347–360. Springer, Berlin, Heidelberg (2009)CrossRef
61.
Jędrzejowicz, P., Wierzbowska, I. Parallel cooperating A-Teams solving instances of the euclidean planar traveling salesman problem. In: J. O’Shea et al. (eds.) Agent and Multi Agent Systems: Technologies and Applications. Lecture Notes in Artificial Intelligence, vol. 6682, pp. 456–465 (2011)
62.
Carle, M.A., Martel, A., Zufferey, N.: The CAT metaheuristic for the solution of multi-period activity-based supply chain network design problems. Int. J. Prod. Econ. 139(2), 664–677 (2012)CrossRef
63.
Barbucha, D.: Experimental Study of the Population Parameters Settings in Cooperative Multi-agent System Solving Instances of the VRP. In: Transactions on Computational Collective Intelligence IX. Lecture Notes in Computer Science, vol. 7770, pp. 1–28 (2013)
64.
Barbucha, D.: A cooperative population learning algorithm for vehicle routing problem with time windows. Neurocomputing 146, 210–229 (2014)CrossRef
65.
Barbucha, D.: Team of A-Teams approach for vehicle routing problem with time windows. In: Terrazas, G., Otero, F., Masegosa, A. (eds.) Nature Inspired Cooperative Strategies for Optimization (NICSO 2013), vol. 512, pp. 273–286. Springer International Publishing (2014)
66.
Barbucha, D., Czarnowski, I., Jędrzejowicz, P., Ratajczak-Ropel, E., Wierzbowska, I.: Influence of the working strategy on A-Team performance. In: Szczerbicki, E., Nguyen, N.T. (eds.) Smart Information and Knowledge Management. Studies in Computational Intelligence, vol. 260, pp. 83–102. Springer, Heidelberg (2010)
67.
Bellifemine, F., Caire, G., Poggi, A., Rimassa, G.: JADE. A White Paper, Exp. 3(3), 6–20 (2003)
68.
69.
Barbucha, D., I. Czarnowski, P. Jędrzejowicz, E. Ratajczak-Ropel, I. Wierzbowska: JABAT — an implementation of the A-Team concept. In: Proceedings of the International Multiconference on Computer Science and Information Technology, vol. 1, pp. 235–241. Polskie Towarzystwo Informatyczne, Wisła (2006)
70.
Barbucha, D., Czarnowski, I., Jędrzejowicz, P., Ratajczak-Ropel, E., Wierzbowska, I.: Parallel cooperating A-Teams, In: P.Jędrzejowicz et al. (eds.) Computational Collective Intelligence. Technologies and Applications. Lecture Notes in Artificial Intelligence, vol. 6923, pp. 322–331. Springer, Heidelberg (2011)
71.
Jędrzejowicz, P., Ratajczak-Ropel, E.: A-Team for solving the resource availability cost problem. In: Nguyen, N.T., Hoang, K., Jędrzejowicz, P. (eds.) Computational Collective Intelligence Technologies and Applications. Lecture Notes in Artificial Intelligence, vol. 7654, pp. 443–452 (2012)
72.
Barbucha, D., Czarnowski, I., Jędrzejowicz, P., Ratajczak-Ropel, E., Wierzbowska, I.: Team of A-Teams — A study of the cooperation between program agents solving difficult optimization problems, Agent-Based Optimization. In: Czarnowski, I., Jędrzejowicz, P., Kacprzyk, J. (eds) Studies in Computational Intelligence, vol. 456, pp. 123–142. Springer, Heidelberg (2013)
73.
Jędrzejowicz, P., Ratajczak-Ropel, E.: Reinforcement learning strategy for solving the resource-constrained project scheduling problem by a team of A-Teams. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds.) Intelligent Information and Database Systems. Lecture Notes in Artificial Intelligence, vol. 8398, pp. 197–206 (2014)
74.
Ren, H., Wang, Y.: A survey of multi-agent methods for solving resource constrained project scheduling problems. In: Proceedings of International Conference on Management and Service Science, vol. 2011, pp. 1–4 (2011)
75.
Jędrzejowicz, P., Ratajczak-Ropel, E.: Agent-Based Approach to Solving the Resource Constrained Project Scheduling Problem. Lecture Notes in Computer Science, vol. 4431, pp. 480–487 (2007)
76.
Jędrzejowicz, P., Ratajczak-Ropel, E.: New generation A-Team for solving the resource constrained project scheduling. In: Proceedings of the Eleventh International Workshop on Project Management and Scheduling, pp. 156–159. Istanbul (2008)
77.
Jędrzejowicz, P., Ratajczak-Ropel, E.: Solving the RCPSP/max problem by the team of agents. In: Hakansson, A., et al. (eds.) Agent and Multi-Agent Systems: Technologies an Applications. Lecture Notes in Artificial Intelligence, vol. 5559, pp. 734–743 (2009)
78.
Jędrzejowicz, P., Ratajczak-Ropel, E.: Team of A-Teams for solving the resource-constrained project scheduling problem. In: Grana, M., Toro, C., Posada, J., Howlett, R., Lakhmi, C.J. (eds.) Advances in Knowledge Based and Intelligent Information and Engineering Systems. Frontiers in Artificial Intelligence and Applications, vol. 243, pp. 1201–1210, (2012)
79.
Jędrzejowicz, P., Ratajczak-Ropel, E.: Reinforcement learning strategies for A-Team solving the resource-constrained project scheduling problem. Neurocomputing 146, 301–307 (2014)CrossRef
80.
Jędrzejowicz, P., Ratajczak-Ropel, E.: Reinforcement Learning Strategy for Solving the MRCPSP by a Team of Agents. In: Neves-Silva, R., Jain, L.C., Howlett, R.J. (eds.) Intelligent Decision Technologies, Proceedings of the 7th KES International Conference on Intelligent Decision Technologies (KES-IDT 2015), pp. 537–548. Springer International Publishing, Switzerland (2015)
81.
Jędrzejowicz, P., Ratajczak-Ropel, E.: PLA Based Strategy for Solving RCPSP by a Team of Agents. J. Univ. Comput. Sci. 22(6), 856–873 (2016)MathSciNet
82.
Jędrzejowicz P., Ratajczak-Ropel E.: Dynamic cooperative interaction strategy for solving RCPSP by a team of agents. In: Nguyen N.T., Manolopoulos, Y., Iliadis, L., Trawiński, B. (eds.) Computational Collective Intelligence. Lecture Notes in Artificial Intelligence, vol. 9875, pp. 454–463 (2016)
Metadaten
Titel
Agent-Based Optimization
verfasst von
Ewa Ratajczak-Ropel
Copyright-Jahr
2018
Buch
Population-Based Approaches to the Resource-Constrained and Discrete-Continuous Scheduling

Print ISBN: 978-3-319-62892-9

Electronic ISBN: 978-3-319-62893-6

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-62893-6_2