Abstract
Teaching–learning-based optimization (TLBO) algorithm is one of the recently proposed optimization algorithms. It has been successfully used for solving optimization problems in continuous spaces. To improve the optimization performance of the TLBO algorithm, a modified TLBO algorithm with differential and repulsion learning (DRLTLBO) is presented in this paper. In the proposed algorithm, the differential evolution (DE) operators are introduced into the teacher phase of DRLTLBO to increase the diversity of the new population. In the learner phase of DRLLBO, local learning method or repulsion learning method are adopted according to a certain probability to make learners search knowledge from different directions. In the local learning method, learners learn knowledge not only from the best learner but also from another random learner of their neighbors. In the repulsion learning method, learners learn knowledge from the best learner and keep away from the worst learner of their neighbors. Moreover, self-learning method is adopted to improve the exploitation ability of learners when they are not changed in some continuous generations. To decrease the blindness of random self-learning method, the history information of the corresponding learners in some continuous generations is used in self-learning phase. Furthermore, all learners are regrouped after a certain iterations to improve the local diversity of the learners. In the end, DRLTLBO is tested on 32 benchmark functions with different characteristics and two typical nonlinear modeling problems, and the comparison results show that the proposed DRLTLBO algorithm has shown interesting outcomes in some aspects.
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Akay B, Karaboga D (2012) A modified Artificial Bee Colony algorithm for real-parameter optimization. Inf. Sci. 192(1):120–142
Blum C (2005) Ant colony optimization: Introduction and recent trends. Phys. Life Reviews 2(4):353–373
Brest J, Greiner S, Boskovic B, Mernik M, Zumer V (2006) Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems. IEEE Trans. Evol. Comput. 10(6):646–657
Cai ZH, Gong WY, Ling CX, Zhang H (2011) A clustering-based differential evolution for global optimization. Appl, Soft Comput 11(1):1363–1379
Chang WD (2012) Differential evolution-based nonlinear system modeling using a bilinear series model. Applied Soft Computing 12:3401–3407
Chen DB, Zou F, Li Z, Wang JT, Li SW (2015) An improved teaching-learning-based optimization algorithm for solving global optimization problem. Inf. Sci. 297:171–190
Chen DB, Zou F, Wang J et al (2015) A teaching–learning-based optimization algorithm with producer–scrounger model for global optimization. Soft Computing 19(3):745–762
Cheng R, Jin Y (2015) A social learning particle swarm optimization algorithm for scalable optimization. Information Sciences 291:43–60
Degertekin SO, Hayalioglu MS (2013) Sizing truss structures using teaching-learning-based optimization. Comput. Struct. 119:177–188
Derrac J, García S, Molina D et al (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation 1(1):3–18
Dhadwal MK, Jung SN, Kim CJ (2014) Advanced particle swarm assisted genetic algorithm for constrained optimization problems. Computational Optimization and Applications 58:781–806
Dor AE, Clerc M, Siarry P (2012) A multi-swarm PSO using charged particles in a partitioned search space for continuous optimization. Comput. Optim. Appl. 53(1):271–295
Gao WF, Yen GG, Liu SY (2014) A Cluster-Based Differential Evolution With Self-Adaptive Strategy for Multimodal Optimization. IEEE Trans, Cybern 44(8):1314–1327
Hsieh ST, Chiu SY, Yen SJ (2012) Adoptive Population Differential Evolution with Local Search for Solving Large Scale Global Optimization, In Proc: IEEE Int. Conf. Sys. Man, Cybern, pp 1090–1094
Ji X, Ye H, Zhou J, et al. (2017) An improved teaching-learning-based optimization algorithm and its application to a combinatorial optimization problem in foundry industry. Applied Soft Computing,
Kennedy J, Eberhart R, et al. (1995) Particle swarm optimization, In: Proc. IEEE International Conf. Neural Networks, 1942-1948
Kennedy J, Mendes R (2002) Population structure and particle swarm performance. In: Proceedings of the 2002 Congress on Evolutionary Computation, 1671-1676
Kim HK, Chong JK, Park KY, Lowther DA (2007) Differential Evolution Strategy for Constrained Global Optimization and Application to Practical Engineering Problems. IEEE Trans. Magnetics 43(4):1565–1568
Kovačević D, Mladenović N, Petrović B, Milošević P (2014) DE-VNS: Self-adaptive Differential Evolution with crossover neighborhood search for continuous global optimization. Computers & Operations Research 52:157–169
Krohling RA, Coelho LDS (2006) Coevolutionary Particle Swarm Optimization Using Gaussian Distribution for Solving Constrained Optimization Problems. IEEE Trans. Sys. Man, Cybern. Part B: Cybern 36(6):1407–1416
Li YH, Zhan ZH, Lin SJ, Wang RM, Luo XN (2015) Competitive and cooperative particle swarm optimization with information sharing mechanism for global optimization problems. Inf. Sci. 293(1):370–382
Liang J, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evol. Comput. 10(3):281–295
Liu Y, Wang H, Yu J, Li P (2010) Selective recursive kernel learning for online identification of nonlinear systems with NARX form. Journal of Process Control 20:181–194
Mendes R, Kennedy J, Neves J (2004) The fully informed particle swarm: simpler, maybe better. IEEE Trans. Evol. Comput. 8(3):204–210
Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications 27(4):1053–1073
Niknam T, Golestaneh F, Sadeghi MS (2012) \(\theta \)-multi-objective teaching-learning-based optimization for dynamic economic emission dispatch. IEEE Syst. J. 6(2):341–352
Patel VK, Savsani VJ (2016) A multi-objective improved teaching-learning based optimization algorithm (MO-ITLBO). Information Sciences 357:182–200
Olivier F (1998) An evolutionary strategy for global minimization and its Markor chain analysis. IEEE Trans. Evol. Comput. 2(30):77–90
Peram T, Veeramachaneni K, Mohan CK (2003) Fitness-distance-ratio based particle swarm optimization. In: Proc. Swarm Intelligence Symp. 174–181
Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comput. 13(2):398–417
Rao RV (2015) Teaching learning based optimization algorithm: and its engineering applications. Springer Publishing Company, Incorporated, Berlin
Rao RV (2016) Review of applications of TLBO algorithm and a tutorial for beginners to solve the unconstrained and constrained optimization problems. Decis Sci Lett 5(1):1–30
Rao RV, Kalyankar VD (2012) Parameter optimization of modern machining processes using teaching-learning-based optimization algorithm. Eng Appl Artif Intell 26(1):524–531
Rao RV, Patel V (2012) An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems. Int. J. Ind. Eng. Comput. 3:535–560
Rao RV, Patel V (2013) An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems. Sci. Iran. Trans. D: Comput. Sci. Eng. Electr. Eng. 20(3):710–720
Rao RV, Savsani VJ, Vakharia DP (2012) Teaching-learning-based optimization: an optimization method for continuous non-linear large scale problems. Inf. Sci. 183(1):1–15
Rao RV, Savsani VJ, Vakharia DP (2011) Teaching-learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems. Eng. Optim. 44(12):1447–1462
Satapathy SC, Naik A, Parvathi K (2013) Weighted Teaching-Learning-Based Optimization for Global Function Optimization. Applied Mathematics 4:429–439
Satapathy SC, Naik A (2014) Modified Teaching-Learning-Based Optimization algorithm for global numerical optimization–A comparative study. Swarm Evol. Comput. 16:28–37
Satapathy S, Naik A (2016) Social group optimization (SGO): a new population evolutionary optimization technique[J]. Complex & Intelligent Systems, :1-31
Storn R, Price K (1997) Differential evolution–A simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization 11(4):341–359
Thomsen R (2004) Multimodal optimization using Crowding-based differential evolution. In: Proc. of the IEEE 2004 Congress on Evolut. Comput. 1382-1389
Tian M, Gao X, Dai C (2017) Differential evolution with improved individual-based parameter setting and selection strategy. Applied Soft Computing 56:286–297
Togan V (2012) Design of planar steel frames using teaching-learning based optimization. Eng. Struct. 34:225–232
Tu Z, Lu GY (2004) A Robust Stochastic Genetic Algorithm (StGA) for Global Numerical Optimization. IEEE Trans. Evol. Comput. 8(5):456–470
Wang L, Zou F, Hei XH, Yang DD, Chen DB et al (2014) A hybridization of Teaching-learning-based optimization and differential evolution for chaotic time series prediction. Neural Computing & Applications. 25(6):1407–1422
Wang L, Zou F, Hei XH, Yang DD, Chen DB et al (2014) An improved teaching-learning-based optimization with neighborhood search for applications of ANN. Neurocomputing 143:231–247
Wilcoxon F (1945) Individual Comparisons by Ranking Methods. Biometrics Bulletin 1(6):80–83
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1):67–82
Wu G, Mallipeddi R, Suganthan PN et al (2016) Differential evolution with multi-population based ensemble of mutation strategies. Information Sciences 329:329–345
Yang CM, Simon D (2005) A new particle swarm optimization technique. In: Proc. the 18th Intern. Conf. Sys. Eng. 164-169
Yang SX, Li CH (2010) A Clustering Particle Swarm Optimizer for Locating and Tracking Multiple Optima in Dynamic Environments. IEEE Trans. Evol. Comput. 14(6):959–974
Yao X, Liu Y, Lin GM (1999) Evolutionary programming made faster. IEEE Trans. Evol. Comput. 3(2):82–102
Yin X, Germay N (1993) A fast genetic algorithm with sharing scheme using cluster analysis methods in multi-modal function optimization, in: Proc. of Int. Conf. on Artificial Neural Nets and Genetic Algorithms, 450–457
Zhang J, Zhao H (2010) A novel adaptive bilinear filter based on pipelined architecture. Digital Signal Processing 20:23–38
Zou F, Wang L, Hei XH, Chen DB, Yang DD (2014) Teaching-learning-based optimization with dynamic group strategy for global optimization. Inf. Sci. 273:112–131
Acknowledgements
This work is partially supported by the National Natural Science Foundation of China (Grant Nos. 61572224, 41475017 and 11504121) and the National Science Fund for Distinguished Young Scholars (Grants No. 61425009). This work is also partially supported by Anhui Provincial Natural Science Foundation (Grant No. 1708085MF140), the Major Project of Natural Science Research in Anhui Province (Grant No. KJ2015ZD36) and the Natural Science Foundation in colleges and universities of Anhui Province (Grant No. KJ2016A639). The codes of DA is taken from http://www.alimirjalili.com/DA.html.
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Zou, F., Chen, D., Lu, R. et al. Teaching–learning-based optimization with differential and repulsion learning for global optimization and nonlinear modeling. Soft Comput 22, 7177–7205 (2018). https://doi.org/10.1007/s00500-017-2722-4
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DOI: https://doi.org/10.1007/s00500-017-2722-4