Abstract
Most of the multiobjective evolutionary algorithm inherently has heavy computational burden, so it takes a long processing time. For this reason, many researches for reducing computational time have been carried out, in particular by using distributed computing such as multi-thread coding, GPU coding, etc. In this paper, multi-thread coding is used to reduce computational time and applied to multiobjective quantum-inspired evolutionary algorithm (MQEA). In MQEA, nondominated sorting and crowding distance assignment which take a long time are carried out in each subpopulation. By multi-thread coding, the processes in each subpopulation can be performed simultaneously. To demonstrate the effectiveness of the proposed distributed MQEA (DMQEA), comparisons with single-thread and multi-thread are carried out for seven DTLZ functions.
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References
Han, K.-H., Kim, J.-H.: Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Trans. Evol. Computat. 6(6), 580–593 (2002)
Han, K.-H., Kim, J.-H.: Quantum-inspired evolutionary algorithms with a new termination criterion, Hε gate, and two phase scheme. IEEE Trans. Evol. Computat. 8(2), 156–169 (2004)
Han, K.-H., Kim, J.-H.: On the analysis of the quantum-inspired evolutionary algorithm with a single individual. Paper presented at IEEE Congress Evolutionary Computation, pp. 9172–9179 (2006)
Kim, Y.-H., Kim, J.-H., Han, K.-H.: Quantum-inspired multiobjective evolutionary algorithm for multiobjective 0/1 knapsack problems. Paper presented at IEEE Congress Evolutionary Computation, pp. 2601–2606 (2006)
Kim, J.-H., Han, J.-H., Kim, Y.-H., Choi, S.-H., Kim, E.-S.: Preference-based Solution Selection Algorithm for Evolutionary Multiobjective Optimization. IEEE Trans. Evol. Computat. 16(1), 20–34 (2012)
Ryu, S.-J., Lee, K.-B., Kim, J.-H.: Improved version of a multiobjective quantum-inspired evolutionary algorithm with preference-based selection. Paper presented at IEEE Congress Evolutionary Computation, pp. 1–7 (2012)
Tan, K.C., Yang, Y.J., Goh, C.K.: A distributed Cooperative coevolutionary algorithm for multiobjective optimization. IEEE Trans. Evol. Computat. 10(5), 527–549 (2006)
Deb, K., Zope, P., Jain, A.: Distributed Computing of Pareto-Optimal Solutions with Evolutionary Algorithms. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 534–549. Springer, Heidelberg (2003)
Tan, K.C., Tay, A., Cai, J.: Design and implementation of a distributed evolutionary computing software. IEEE Trans. Syst. Man Cybern. C, Appl. 33(3), 325–338 (2003)
Hey, T.: Quantum computing: an introduction. Computing and Control Eng. J. 10(3), 105–112 (1999)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Computat. 6(2), 182–197 (2002)
Zitzler, E.: Evolutionary algorithms for multiobjective optimization: methods and applications. Berichte aus der Informatik. Shaker Verlag, Aachen-Maastricht (1999)
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Ryu, SJ., Kim, JH. (2013). Distributed Multiobjective Quantum-Inspired Evolutionary Algorithm (DMQEA). In: Kim, JH., Matson, E., Myung, H., Xu, P. (eds) Robot Intelligence Technology and Applications 2012. Advances in Intelligent Systems and Computing, vol 208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37374-9_63
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DOI: https://doi.org/10.1007/978-3-642-37374-9_63
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