Bee colony optimization for scheduling independent tasks to identical processors
- 435 Downloads
The static scheduling of independent tasks on homogeneous multiprocessor systems is studied in this paper. This problem is treated by the Bee Colony Optimization (BCO) meta-heuristic. The BCO algorithm belongs to the class of stochastic swarm optimization methods inspired by the foraging habits of bees in nature. To investigate the performance of the proposed method extensive numerical experiments are performed. Our BCO algorithm is able to obtain the optimal value of the objective function in the majority of test examples known from literature. The deviation of non-optimal solutions from the optimal ones in our test examples is at most 2%. The CPU times required to find the best solutions by BCO are significantly smaller than the corresponding times required by the CPLEX optimization solver. Moreover, our BCO is competitive with state-of-the-art methods for similar problems, with respect to both solution quality and running time. The stability of BCO is examined through multiple executions and it is shown that solution deviation is less than 1%.
KeywordsSwarm intelligence Bee colony optimization (BCO) Combinatorial optimization Scheduling problems Homogeneous multiprocessor systems
The authors would like to express their gratitude to the anonymous referees for their useful comments and suggestions that yielded a significant improvement to this work. The gratefulness also goes to Mr. Milos Rancic for his dedicated approach to proofreading.
This work has been supported by Serbian Ministry of Science and Technological Development, grants No. 144007 and 144033.
- Abbass, H.A.: MBO: marriage in honey bees optimization-a haplometrosis polygynous swarming approach. In: Proceedings of the Congress on Evolutionary Computation, Seoul, South Korea, pp. 207–214 (2001) Google Scholar
- Coffman, E.G. Jr, Garey, M.R., Johnson, D.S.: Approximation algorithm for bin-packing: A survey. In: Hockbaum, D. (ed.) Approximation Algorithms for NP-Hard Problems, pp. 46–93. PSW, Boston (1996) Google Scholar
- Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completness. Freeman, New York (1979) Google Scholar
- ILOG CPLEX 11.2 Reference Manual (2008) Google Scholar
- Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Tech. rep., Erciyes University, Engineering Faculty Computer Engineering Department Kayseri/Turkiye (2005) Google Scholar
- Lučić, P., Teodorović, D.: Bee system: modeling combinatorial optimization transportation engineering problems by swarm intelligence. In: Preprints of the TRISTAN IV Triennial Symposium on Transportation Analysis, Sao Miguel, Azores Islands, pp. 441–445 (2001) Google Scholar
- Lučić, P., Teodorović, D.: Transportation modeling: an artificial life approach. In: Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence, Washington, DC, pp. 216–223 (2002) Google Scholar
- Lučić, P., Teodorović, D.: Vehicle routing problem with uncertain demand at nodes: the bee system and fuzzy logic approach. In: Verdegay, J.L. (ed.) Fuzzy Sets based Heuristics for Optimization, pp. 67–82. Physica, Berlin (2003b) Google Scholar
- Navrat, P.: Bee hive metaphor for web search. In: Rachev, B., Smrikarov, A. (eds.) Proceedings of the International Conference on Computer Systems and Technologies—CompSysTech 2006, Veliko Turnovo, Bulgaria, p. IIIA.12 (2006) Google Scholar
- Pham, D.T., Ghanbarzadeh, A., Koc, E., Otri, S., Zaidi, M.: The bees algorithm—a novel tool for complex optimisation problems. In: Proceedings of the 2nd Virtual International Conference on Intelligent Production Machines and Systems (IPROMS 2006), pp. 454–459. Elsevier, Cardiff (2006) Google Scholar
- Teodorović, D., Dell’Orco, M.: Bee colony optimization—a cooperative learning approach to complex transportation problems. In: Advanced OR and AI Methods in Transportation. Proceedings of the 10th Meeting of the EURO Working Group on Transportation, Poznan, Poland, pp. 51–60 (2005) Google Scholar
- Wedde, H.F., Sebastian, L., van Bernhard, B., Zeynep, B.: A novel class of multi-agent algorithms for highly dynamic transport planning inspired by honey bee behavior. In: Proceedings of the 12th IEEE International Conference on Factory Automation, Patras, Greece, pp. 1157–1164 (2007) Google Scholar
- Yang, X.: Engineering optimizations via nature-inspired virtual bee algorithms. In: Mira, J., Alvarez, J.R. (eds.) IWINAC 2005. LNCS, vol. 3562, pp. 317–323 (2005) Google Scholar