Abstract
Coordination of multi agent systems remains as a problem since there is no prominent method suggests any universal solution. Metaheuristic agents are specific implementations of multi-agent systems, which imposes working together to solve optimisation problems using metaheuristic algorithms. An idea for coordinating metaheuristic agents borrowed from swarm intelligence is introduced in this paper. This swarm intelligence-based coordination framework has been implemented as swarms of simulated annealing agents collaborated with particle swarm optimization for multidimensional knapsack problem. A comparative performance analysis is also reported highlighting that the implementation has produced much better results than the previous works.
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References
Aydin M. E., Fogarty T. C. (2004) A distributed evolutionary simulated annealing algorithm for combinatorial optimisation problems. Journal of Heuristics 10(3): 269–292
Aydin M. E. (2007) Meta-heuristic agent teams for job shop scheduling problems. Lecture Notes in Artificial Intelligence 4659: 185–194
Aydin, M. E. (2008). Swarm intelligence to coordinate metaheuristic agents. In Proceedings of the IMS 2008, 14–16 October 2008, Adapazari, Turkey
Beasley, J. E. (1990). Obtaining test problems via Internet. Journal of Global Optimisation, 8, 429–433. http://people.brunel.ac.uk/~mastjjb/jeb/info.html.
Chen A., Yang G., Wu Z. (2006) Hybrid discrete particle swarm optimization algorithm for capacitated vehicle routing problem. Journal of Zhejiang University SCIENCE A 7(4): 607–614
Colorni A., Dorigo M., Maniezzo V., Trubian M. (1994) Ant system for job-shop scheduling. Belgian Journal of Operations Research, Statistics and Computer Science (JORBEL) 34(1): 39–53
Dong C., Qiu Z. (2006) Particle swarm optimization algorithm based on the idea of simulated annealing. International Journal of Computer Science and Network Security 6(10): 152–157
Farooq M. (2008) Bee-inspired protocol engineering: From nature to networks. Springer, Berlin, Heidelberg, Germany
Hammami M., Ghediera K. (2005) COSATS, X-COSATS: Two multi-agent systems cooperating simulated annealing, tabu search and X-over operator for the K-Graph Partitioning problem. Lecture Notes in Computer Science 3684: 647–653
Hansen P., Mladenovic N., Dragan U. (2004) Variable neighborhood search for the maximum clique. Discrete Applied Mathematics 145(1): 117–125
Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. In Proceedings of IEEE International Conference on Neural Networks (pp. 1942–1948) Perth, Austrailia.
Kennedy, J., & Eberhart, R. C., (1997). A discrete binary version of the particle swarm optimization. In Proceedings of IEEE Conference on Systems Man and Cybernetics (pp. 4104–4108). Pisctaway, NY, USA.
Kolonko M. (1999) Some new results on simulated annealing applied to the job shop scheduling problem. European Journal of Operational Research 113: 123–136
Kolp M., Giorgini P., Mylopoulos J. (2006) Multi-agent architectures as organizational structures. Autonomous Agents and Multi-Agent Systems 13: 3–25
Kwan R., Aydin M. E., Luang C., Zhang J. (2009) Multiuser scheduling in high speed downlink packet access. IET Communications 3(8): 1363–1370
Nguyen T.-A., Kuonen P. (2007) Programming the grid with POP C++. Future Generation Computer Science 23(1): 23–30
Panait L., Luke S. (2005) Cooperative multi-agent learning: The state of the art. Autonomous Agents and Multi-Agent Systems 11: 387–434
Pham, D. T., Otri, S., Ghanbarzadeh, A., & Koc, E. (2006). Application of the bees algorithm to the training of learning vector quantisation networks for control chart pattern recognition. In Proceedings of the Information and Communication Technologies (ICTTA’06) (pp. 1624–1629). Syria.
Pham, D. T., Afify A., & Koc, E. (2007). Manufacturing cell formation using the Bees Algorithm. In Pham et al. (Eds.), IPROMS’2007 Innovative Production Machines and Systems Virtual Conference. Cardiff, UK.
Sevkli M., Aydin M. E. (2006) A variable neighbourhood search algorithm for job shop scheduling problems. Lecture Notes in Computer Science 3906: 261–271
Tasgetiren M. F., Liang Y. C., Sevkli G., Gencyilmaz M. (2007) Particle swarm optimization algorithm for makespan and total flowtime minimization in permutation flowshop sequencing problem. European Journal of Operational Research 177(3): 1930–1947
Vazquez-Salceda J., Dignum V., Dignum F. (2005) Organizing multiagent systems. Autonomous Agents and Multi-Agent Systems 11: 307–360
Wang X., Ma J.-J., Wang S., Bi D. -W. (2007) Distributed particle swarm optimization and simulated annealing for energy-efficent coverage in wireless sensor networks. Sensor 7: 628–648
Wilbaut C., Hanafi S., Salhi S. (2008) A survey of effective heuristics and their applications to a variety of knapsack problems. IMA Journal of Managment Mathematics 19: 227–244
Yigit V., Aydin M. E., Turkbey O. (2006) Solving large-scale uncapacitated facility location problems with evolutionary simulated annealing. International Journal of Production Research 44(22): 4773–4791
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Aydin, M.E. Coordinating metaheuristic agents with swarm intelligence. J Intell Manuf 23, 991–999 (2012). https://doi.org/10.1007/s10845-010-0435-y
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DOI: https://doi.org/10.1007/s10845-010-0435-y