Abstract
Extremal optimization is a dynamic, heuristic intelligent algorithm. It evolves a single solution and makes local modifications to the worst components. In this paper, a knowledge-base mutation operator is presented based on the distribution knowledge of candidate solutions. And then a population-based extremal optimization with knowledge-based mutation is proposed by introducing the idea of swarm evolution. Finally, the proposed method is applied to PID parameter tuning. The simulation results show that the proposed algorithm is characterized by high response speed, small overshoot and steady-state error, and obtains satisfactory control effect.
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References
Boettcher, S., Percus, A.G.: Extremal Optimization: Methods Derived from Co-evolution. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 825–832. Morgan Kaufmann, San Francisco (1999)
Boettcher, S., Percus, A.G.: Optimization with Extremal Dynamics. Phys. Rev. Lett. 86(23), 5211–5214 (2001)
Ding, J., Lu, Y.Z., Chu, J.: Studies on Controllability of Directed Networks with Extremal Optimization. Physica A 392(24), 6603–6615 (2013)
Lee, C.Y., Yao, X.: Evolutionary Algorithms with Adaptive Lévy Mutations. In: Proceedings of the 2001 Congress on Evolutionary Computation, pp. 568–575. IEEE Press, Piscataway (2001)
Chen, M.-R., Lu, Y.-Z., Yang, G.-k.: Population-Based Extremal Optimization with Adaptive Lévy Mutation for Constrained Optimization. In: Wang, Y., Cheung, Y.-m., Liu, H. (eds.) CIS 2006. LNCS (LNAI), vol. 4456, pp. 144–155. Springer, Heidelberg (2007)
Menai, M.E., Batouche, M.: Efficient Initial Solution to Extremal Optimization Algorithm for Weighted MAXSAT Problem. In: Chung, P.W.H., Hinde, C.J., Ali, M. (eds.) IEA/AIE 2003. LNCS (LNAI), vol. 2718, pp. 592–603. Springer, Heidelberg (2003)
Sousa, F.L., Vlassov, V., Ramos, F.M.: Generalized Extremal Optimization: An Application in Heat Pipe Design. Appl. Math. Model. 28(10), 911–931 (2004)
Zeng, G.Q., Lu, Y.Z., Mao, W.J., Chu, J.: Study on Probability Distributions for Evolution in Modified Extremal Optimization. Physica A 389(9), 1922–1930 (2010)
Li, X., Luo, J., Chen, M.R., Wang, N.: An Improved Shuffled Frog-leaping Algorithm with Extremal Optimisation for Continuous Optimisation. Inform. Sciences 192, 143–151 (2012)
Li, D.Y., Liu, C.Y., Du, Y., Han, X.: Artificial Intelligence with Uncertainty. Journal of Software 15(11), 1583–1594 (2004)
Li, J., Chai, T.Y., Gong, J.K.: Design of PID controller using cross entropy method. Control and Decision 26(5), 794–796 (2011)
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Chen, J., Xie, Y., Chen, H. (2014). A Population-Based Extremal Optimization Algorithm with Knowledge-Based Mutation. In: Tan, Y., Shi, Y., Coello, C.A.C. (eds) Advances in Swarm Intelligence. ICSI 2014. Lecture Notes in Computer Science, vol 8794. Springer, Cham. https://doi.org/10.1007/978-3-319-11857-4_11
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DOI: https://doi.org/10.1007/978-3-319-11857-4_11
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11856-7
Online ISBN: 978-3-319-11857-4
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