Abstract
Population based heuristic optimization techniques, though powerful, are often limited by the memory size of hardware context when implemented on micro-controllers, embedded systems and commercial robots platforms etc. On the other hand, the teaching-learning based optimization algorithm (TLBO) is a recently proposed algorithm of high performance on both constrained and unconstrained optimization problems. In this paper, a new compact teachinglearning based optimization algorithm (cTLBO) is proposed to combine the strength of the original TLBO and reduce the memory requirement through a compact structure that utilizes an adaptive statistic description to replace the process of a population of solutions. Numerical results on test benchmark functions show that the new algorithm does not sacrifice the efficiency within the limited hardware resources.
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References
Goldberg, D.E., Holland, J.H.: Genetic Algorithms And Machine Learning. Machine Learning 3(2), 95–99 (1988)
Shi, Y., Eberhart, R.: A Modified Particle Swarm Optimizer. In: The 1998 IEEE International Conference on Evolutionary Computation Proceedings of the IEEE World Congress on Computational in Telligence, pp. 69–73. IEEE (1998)
Geem, Z.W., Kim, J.H., Loganathan, G.V.: A New Heuristic Optimization Algorithm: Harmony Search. Simulation 76(2), 60–68 (2001)
Mininno, E., Cupertino, F., Naso, D.: Real-Valued Compact Genetic Algorithms for Embedded Microcontroller Optimization. IEEE Transactions on Evolutionary Computation 12(2), 203–219 (2008)
Harik, G.R., Lobo, F.G., Goldberg, D.E.: The Compact Genetic Algorithm. IEEE Transactions on Evolutionary Computation 3(4), 287–297 (1999)
Gallagher, J.C., Vigraham, S., Kramer, G.: A Family Of Compact Genetic Algorithms for in trinsic Evolvable Hardware. IEEE Transactions on Evolutionary Computation 8(2), 111–126 (2004)
Neri, F., Mininno, E.: Memetic Compact Differential Evolution for Cartesian Robot Control. IEEE Computational intelligence Magazine 5(2), 54–65 (2010)
Neri, F., Iacca, G., Mininno, E.: Disturbed Exploitation Compact Differential Evolution for Limited Memory Optimization Problems. Information Sciences 181(12), 2469–2487 (2011)
Mininno, E., Neri, F., Cupertino, F., Naso, D.: Compact Differential Evolution. IEEE Transactions on Evolutionary Computation 15(1), 32–54 (2011)
Neri, F., Mininno, E., Iacca, G.: Compact Particle Swarm Optimization. Information Sciences 239, 96–121 (2013)
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., Patel, V.: An Improved Teaching-Learning-Based Optimization Algorithm for Solving Unconstrained Optimization Problems. Scientia Iranica (2012)
Crepinsek, M., Liu, S.-H., Mernik, L.: A Note on Teaching-Learningbased Optimization Algorithm. Information Sciences 212, 79–93 (2012)
Waghmare, G.: Comments on A Note on Teaching-Learning-Based Optimization Algorithm. Information Sciences 229, 159–169 (2013)
Niknam, T., Azizipanah-Abarghooee, R., Aghaei, J.: A New Modified Teaching-Learning Algorithm for Reserve Constrained Dynamic Economic Dispatch. IEEE Transactions on Power Systems 28(2), 749–763 (2013)
Yang, Z., Li, K., Foley, A., Zhang, A.C.: A New Self-Learning Tlbo Algorithm for Rbf Neural Modelling Of Batteries in Electric Vehicles. In: 2014 IEEE World Congress on Computational intelligence. IEEE (2014)
Niknam, T., Fard, A.K., Baziar, A.: Multi-Objective Stochastic Distribution Feeder Reconfiguration Problem Considering Hydrogen And Thermal Energy Production byFuel Cell Power Plants. Energy 42(1), 563–573 (2012)
Harik, G., Cantu-Paz, E., Goldberg, D.E., Miller, B.L.: The Gambler’s Ruin Problem, Genetic Algorithms, And The Sizing Of Populations. Evolutionary Computation 7(3), 231–253 (1999)
Ahn, C.W., Ramakrishna, R.S.: Elitism-Based Compact Genetic Algorithms. IEEE Transactions on Evolutionary Computation 7(4), 367–385 (2003)
Kramer, G.R., Gallagher, J.C., Raymer, M.: On The Relative Efficacies Of Cga Variants for in trinsic Evolvable Hardware; Population, Mutation, And Random Immigrants. In: Proceedings of the 2004 NASA/Dod Conference on Evolvable Hardware, 2004, pp. 225–230. IEEE (2004)
Yao, X., Liu, Y., Lin, G.: Evolutionary Programming Made Faster. IEEE Transactions on Evolutionary Computation 3(2), 82–102 (1999)
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Yang, Z., Li, K., Guo, Y. (2014). A New Compact Teaching-Learning-Based Optimization Method. In: Huang, DS., Jo, KH., Wang, L. (eds) Intelligent Computing Methodologies. ICIC 2014. Lecture Notes in Computer Science(), vol 8589. Springer, Cham. https://doi.org/10.1007/978-3-319-09339-0_72
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DOI: https://doi.org/10.1007/978-3-319-09339-0_72
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