Abstract
In many real word problems, optimization problems are non-stationary and dynamic. Optimization of these dynamic optimization problems requires the optimization algorithms to be able to find and track the changing optimum efficiently over time. In this paper, for the first time, a multi-swarm teaching-learning-based optimization algorithm (MTLBO) is proposed for optimization in dynamic environment. In this method, all learners are divided up into several subswarms so as to track multiple peaks in the fitness landscape. Each learner is learning from the teacher and the mean of his or her corresponding subswarm instead of the teacher and the mean of the class in teaching phase, and then learners learn from interaction between themselves in their corresponding subswarm in learning phase. Moreover, all subswarms are regrouped periodically so that the information exchange is made with all the learners in the class to achieve proper exploration ability. The proposed MTLBO algorithm is evaluated on moving peaks benchmark problem in dynamic environments. The experimental results show the proper accuracy and convergence rate for the proposed approach in comparison with other well-known approaches.
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References
Yang, S., Tinós, R.: A Hybrid Immigrants Scheme for Genetic Algorithms in Dynamic Environments. International Journal of Automation and Computing 4(3), 243–254 (2007)
Schönemann, L.: Evolution Strategies in Dynamic Environments. Evolutionary Computation in Dynamic and Uncertain Environments 51, 51–77 (2007)
Hu, J., Li, S., Goodman, E.: Evolutionary Robust Design of Analog Filters Using Genetic Programming. Evolutionary Computation in Dynamic and Uncertain Environments 51, 479–496 (2007)
Eberhart, R.C., Shi, Y.: Tracking and optimizing dynamic systems with particle swarms. In: Proceedings of the 2001 Congress on Evolutionary Computation, vol. 1, pp. 94–100. IEEE (2001)
Mendes, R., Mohais, A.S.: DynDE: a Differential Evolution for Dynamic Optimization Problems. In: The 2005 IEEE Congress on Evolutionary Computation, vol. 3, pp. 2808–2815 (2005)
Trojanowski, K., Wierzchon, S.T.: Immune-based algorithms for dynamic optimization. Information Sciences 179(10), 1495–1515 (2009)
Guntsch, M., Middendorf, M., Schmeck, H.: An Ant Colony Optimization Approach to Dynamic TSP. In: Proceedings of the Genetic and Evolutionary Conference (GECCO 2001), pp. 860–867 (2001)
Yang, S., Yao, X.: Experimental study on population-based incremental learning algorithms for dynamic optimization problems. Soft Computing 9(11), 815–834 (2005)
Saleem, S., Reynolds, R.: Cultural Algorithms in Dynamic Environments. In: Proceedings of the 2000 Congress on Evolutionary Computation, vol. 2, pp. 1513–1520 (2000)
Nasiri, B., Meybodi, M.R.: Speciation based firefly algorithm for optimization in dynamic environments. International Journal of Artificial Intelligence 8(12), 118–132 (2012)
Cruz, C., Gonzalez, J.R., Pelta, D.A.: Optimization in dynamic environments: a survey on problems, methods and measures. Soft Computing 15(7), 1427–1448 (2011)
Ayvaz, D., Topcuoglu, H.R., Gurgen, F.: Performance evaluation of evolutionary heuristics in dynamic environments. Applied Intelligence 37(1), 130–144 (2012)
Branke, J.: Evolutionary approaches to dynamic optimization problems-updated survey. In: GECCO Workshop on Evolutionary Algorithms for Dynamic Optimization Problems, pp. 27–30 (2001)
Jin, Y., Branke, J.: Evolutionary Optimization in Uncertain Environments–A Survey. IEEE Transactions on Evolutionary Computation 9(3), 303–317 (2005)
Rao, R.V., Savsani, V.J., Vakharia, D.P.: Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design 43(3), 303–315 (2011)
Rao, R.V., Savsani, V.J., Vakharia, D.P.: Teaching-learning-based optimization: An optimization method for continuous non-linear large scale problems. Information Sciences 183(1), 1–15 (2012)
Branke, J.: Memory enhanced evolutionary algorithms for changing optimization problems. In: Proceedings of the 1999 Congress on Evolutionary Computation, pp. 1875–1882. IEEE Service Center, Piscataway (1999)
Togan, V.: Design of planar steel frames using teaching-learning based optimization. Engineering Structures 34, 225–232 (2012)
Rao, R.V., Patel, V.: An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems. International Journal of Industrial Engineering Computations 3, 535–560 (2012)
Jadhav, H.T., Chawla, D., Roy, R.: Modified Teaching Learning Based Algorithm for Economic Load Dispatch Incorporating Wind Power. In: The 11th International Conference on Environment and Electrical Engineering (EEEIC), pp. 397–402 (2012)
Amiri, B.: Application of Teaching-Learning-Based Optimization Algorithm on Cluster Analysis. Journal of Basic and Applied Scientific Research 2(11), 11795–11802 (2012)
Naik, A., Parvathi, K., Satapathy, S.C., Nayak, R., Panda, B.S.: QoS Multicast Routing Using Teaching Learning Based Optimization. In: Aswatha Kumar, M., Selvarani, R., Suresh Kumar, T.V. (eds.) Proceedings of ICAdC. AISC, vol. 174, pp. 49–55. Springer, Heidelberg (2013)
Nayak, M.R., Nayak, C.K., Rout, P.K.: Application of Multi-Objective Teaching Learning Based Optimization Algorithm to Optimal Power Flow Problem. Procedia Technology 6, 255–264 (2012)
Rao, R.V., Patel, V.: Multi-objective optimization of heat exchangers using a modified teaching-learning-based optimization algorithm. Applied Mathematical Modelling 37(3), 1147–1162 (2013)
Niknamn, T., Azizipanah-Abarghooee, R., Narimani, M.R.: A new multi objective optimization approach based on TLBO for location of automatic voltage regulators in distribution systems. Engineering Applications of Artificial Intelligence 25(8), 1577–1588 (2012)
Liang, J.J., Suganthan, P.N.: Dynamic multi-swarm particle swarm optimizer. In: Proceedings 2005 IEEE Swarm Intelligence Symposium, SIS 2005, pp. 124–129 (2005)
Li, C., Yang, S., Nguyen, T.T., Yu, E.L., Yao, X., Jin, Y., Beyer, H.-G., Suganthan, P.N.: Benchmark Generator for CEC’2009 Competition on Dynamic Optimization. Technical Report, Department of Computer Science, University of Leicester, U.K (2008)
Yang, S.: A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments. IEEE Transactions on Evolutionary Computation 14(6), 959–974 (2010)
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Zou, F., Wang, L., Hei, X., Jiang, Q., Yang, D. (2013). Teaching-Learning-Based Optimization Algorithm in Dynamic Environments. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2013. Lecture Notes in Computer Science, vol 8297. Springer, Cham. https://doi.org/10.1007/978-3-319-03753-0_35
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DOI: https://doi.org/10.1007/978-3-319-03753-0_35
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03752-3
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