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An improved league championship algorithm with free search and its application on production scheduling

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Abstract

An improved league championship algorithm with free search (LCAFS) is proposed to avoid the drawbacks of basic LCA, such as premature convergence, slow convergence speed. The parameters of the algorithm vary linearly along with iteration. A novel match schedule is designed to improve the competition capability for the sport teams. Furthermore, the free search operation is introduced to promote the diversity of the league. Inspired by the real league degradation, degradation mechanism is used to preserve the team elites. It is convinced by using benchmark functions that LCAFS is superior to other compared algorithms in the global searching performance and convergence speed. The proposed algorithm is finally employed as learning method of parameters in neural network to establish the shop floor production scheduling model and achieves good results.

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References

  • Abharian, A. E., Sarabi, S. Z., & Yomi, M. (2014). Optimal sigmoid nonlinear stochastic control of HIV-1 infection based on bacteria foraging optimization method. Biomedical Signal Processing and Control, 10, 184–191.

    Article  Google Scholar 

  • Askarzadeh, A. (2013). A discrete chaotic harmony search-based simulated annealing algorithm for optimum design of PV/wind hybrid system. Solar Energy, 97, 93–101.

    Article  Google Scholar 

  • Chen, M. Y. (2013). A hybrid ANFIS model for business failure prediction utilizing particle swarm optimization and subtractive clustering. Information Sciences, 220, 180–195.

    Article  Google Scholar 

  • Dorigo, M., & Blum, C. (2005). Ant colony optimization theory: A survey. Theoretical Computer Science, 344, 243–278.

    Article  Google Scholar 

  • Eberhart, R. C., & Shi, Y. (2000). Comparing inertia weights and constriction factors in particle swarm optimization. In Proceedings of IEEE international congress on evolutionary computation, vol. 1. San Diego, CA, pp. 84–88.

  • Formato, R. A. (2007). Central force optimization: A new metaheuristic with applications in applied electromagnetics. Progress in Electromagnetics Research, 77, 425–491.

    Article  Google Scholar 

  • Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm: Harmony search. Simulation, 76(2), 60–68.

    Article  Google Scholar 

  • Holland, J. H. (1975). Adaptation in natural and artificial system. Ann Arbor: Michigan University Press.

    Google Scholar 

  • Kalin, P. (2009). Free search—a model of adaptive intelligence. In 2009 International conference on adaptive and intelligent systems, pp. 92–97.

  • Kalin, P., & Guy, L. (2005). Free search—a comparative analysis. Information Sciences, 172, 173–193.

    Article  Google Scholar 

  • Kashan, A. H. (2009). League championship algorithm: A new algorithm for numerical function optimization. In 2009 IEEE international conference of soft computing and pattern recognition, pp. 43–48, Dec. 4–9, Malacca.

  • Kashan, A. H., & Karimi, B. (2010). A new algorithm for constrained optimization inspired by the sport league championships. In 2010 IEEE congress on evolutionary computation, pp. 1–8, July 18–23, Barcelona.

  • Kashan, A. H., Karimi, B., & Jolai, F. (2006). Effective hybrid genetic algorithm for minimizing makespan on a single-batch processing machine with non-identical job sizes. International Journal of Production Research, 44(12), 2337–2360.

    Article  Google Scholar 

  • Kashan, A. H. (2011). An efficient algorithm for constrained global optimization and application to mechanical engineering design: League championship algorithm (LCA). Computer-Aided Design, 43(12), 1769–1792.

  • Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. In Proceedings of IEEE international conference on neural networks. Piscataway, NJ, USA, 4(2), 1942–1948.

  • Lenin, K., Reddy, B. R., & Kalavathi, M. S. (2013). League championship algorithm (LCA) for solving optimal reactive power dispatch problem. International Journal of Computer and Information Technologies, 1(3), 254–272.

    Google Scholar 

  • Metaxiotis, K. S., Askounis, D., & Psarras, J. (2002). Expert systems in production planning and scheduling: A state-of-the-art survey. Journal of Intelligent Manufacturing, 13(4), 253–260.

    Article  Google Scholar 

  • Morad, N., & Zalzala, A. (1999). Genetic algorithms in integrated process planning and scheduling. Journal of Intelligent Manufacturing, 10(2), 169–179.

    Article  Google Scholar 

  • Niu, B., Zhu, Y., He, X., & Wu, H. (2007). MCPSO: A multi-swarm cooperative particle swarm optimizer. Applied Mathematics and Computation, 185, 1050–1062.

    Article  Google Scholar 

  • Passino, K. M. (2002). Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems Magazine, 22(3), 52–67.

    Article  Google Scholar 

  • Pourali, Z., & Aminnayeri, M. (2012). A novel discrete league championship algorithm for minimizing earliness/tardiness penalties with distinct due dates and batch delivery consideration. In Lecture notes in computer science, vol. 6838, pp. 139–146.

  • Ramin, R. (2011). Cuckoo optimization algorithm. Applied Soft Computing, 11, 5508–5518.

    Article  Google Scholar 

  • Shi, Y., & Eberhart, R. C. (2001). Fuzzy adaptive particle swarm optimization. In Proceedings of IEEE international congress on evolutionary computation, vol. 1. Seoul, Korea, pp. 101–106.

  • Stacey, A., Jancic, M., & Grundy, I. (2003). Particle swarm optimization with mutation. In The 2003 congress on evolutionary computation, Dec. 8–12, vol. 2, pp. 1425–1430.

  • Sun, J., Wang, X., & Li, K., et al. (2013). An auction and league championship algorithm based resource allocation mechanism for distributed cloud. In Lecture notes in computer science, vol. 8299, pp. 334–346.

  • Wang, L., & Shen, W. (2003). DPP: An agent-based approach for distributed process planning. Journal of Intelligent Manufacturing, 14(5), 429–439.

    Article  Google Scholar 

  • Yang, X. S. (2009). Firefly algorithms for multimodal optimization. In O. Watanabe & T. Zeugmann (Eds.), Stochastic algorithms: Foundations and applications, SAGA 2009, lecture notes in computer science, vol. 5792. Berlin: Springer (pp. 169–78)

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Acknowledgments

This paper is supported by Shanghai Project of Absorption and Innovation of Imported Technologies (14XI-2-04).

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Authors and Affiliations

  1. Shanghai Electric Group Co., Ltd., Central Academe, Shanghai, 200070, China

    Wei Xu & Jiarong Yang

  2. College of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China

    Raofen Wang

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  1. Wei Xu
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  2. Raofen Wang
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  3. Jiarong Yang
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Correspondence to Wei Xu.

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Xu, W., Wang, R. & Yang, J. An improved league championship algorithm with free search and its application on production scheduling. J Intell Manuf 29, 165–174 (2018). https://doi.org/10.1007/s10845-015-1099-4

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  • DOI: https://doi.org/10.1007/s10845-015-1099-4

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