Abstract
Adaptive guided differential evolution algorithm (AGDE) is a differential evolution (DE) algorithm that utilizes the information of good and bad vectors in the population, it introduced a novel mutation rule in order to balance effectively the exploration and exploitation tradeoffs. It divided the population into three clusters (best, better and worst) with sizes 100p%, NP − 2 * 100% and 100% respectively. where p is the proportion of the partition with respect to the total number of individuals in the population (NP). AGDE selects three random individuals, one of each partition to implement the mutation process. Besides, a novel adaptation scheme was proposed in order to update the value of crossover rate without previous knowledge about the characteristics of the problems. This paper introduces enhanced AGDE (EAGDE) with non-linear population size reduction, which gradually decreases the population size according to a non-linear function. Moreover, a newly developed rule developed to determine the initial population size, that is related to the dimensionality of the problems. The performance of the proposed algorithm is evaluated using CEC2013 benchmarks and the results are compared with the state-of-art DE and non-DE algorithms, the results showed a great competitiveness for the proposed algorithm over the other algorithms, and the original AGDE.
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Storn, R., Price, K.: Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. International Computer Science Institute Technical Report, Tech. Rep. TR-95-012 (1995)
Storn, R., Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob. Optim. 11(4), 341–359 (1997)
Das, S., Abraham, A., Chakraborty, U.K., Konar, A.: Differential evolution using a neighborhood-based mutation operator. IEEE Trans. Evol. Comput. 13(3), 526–553 (2009)
Zhang, J., Sanderson, A.C.: JADE: adaptive differential evolution with optional external archive. IEEE Trans. Evol. Comput. 13(5), 945–958 (2009)
Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comput. 13(2), 398–417 (2009)
Mohamed, A.W., Sabry, H.Z.: Constrained optimization based on modified differential evolution algorithm. Inf. Sci. 171–208 (2012)
Mohamed, A.W., Sabry, H.Z., Khorshid, M.: An alternative differential evolution algorithm for global optimization. J. Adv. Res. 3(2), 149–165 (2012)
Mohamed, A.W., Sabry, H.Z., Farhat, A.: Advanced differential evolution algorithm for global numerical optimization. In: Proceedings of the IEEE International Conference on Computer Applications and Industrial Electronics (ICCAIE’11), Penang, Malaysia, pp. 156–161 (2011)
Li, X., Yin, M.: Modified differential evolution with self-adaptive parameters method. J. Comb. Optim. 31(2), 546–576 (2014)
Mohamed, A.W.: An improved differential evolution algorithm with triangular mutation for global numerical optimization. Comput. Ind. Eng. 85, 359–375 (2015)
Mohamed, A.W., Suganthan, P.N.: Real-parameter unconstrained optimization based on enhanced fitness-adaptive differential evolution algorithm with novel mutation. Soft Comput. (2017). https://doi.org/10.1007/s00500-017-2777-2
Mohamed, A.W.: An efficient modified differential evolution algorithm for solving constrained non-linear integer and mixed-integer global optimization problems. Int. J. Mach. Learn. Cybernet. 8, 989 (2017). https://doi.org/10.1007/s13042-015-0479-6
Mohamed, A.W.: A novel differential evolution algorithm for solving constrained engineering optimization problems. J. Intell. Manuf. (2017). https://doi.org/10.1007/s10845-017-1294-6
Mohamed, A.W., Almazyad, A.S.: Differential evolution with novel mutation and adaptive crossover strategies for solving large scale global optimization problems. Appl. Comput. Intell. Soft Comput. (2017), Article ID 7974218, 18 pp. https://doi.org/10.1155/2017/7974218
Mohamed, A.W.: Solving stochastic programming problems using new approach to differential evolution algorithm. Egypt. Inf. J. 18(2), 75–86 (2017)
Brest, J., Greiner, S., Bošković, B., Mernik, M., Zumer, V.: Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans. Evol. Comput. 10(6), 646–657 (2006)
Noman, N., Iba, H.: Accelerating differential evolution using an adaptive local search. IEEE Trans. Evol. Comput. 12(1), 107–125 (2008)
Peng, F., Tang, K., Chen, G., Yao, X.: Multi-start JADE with knowledge transfer for numerical optimization. In: IEEE CEC, pp. 1889–1895 (2009)
Montgomery, J., Chen, S.: An Analysis of the Operation of Differential Evolution at High and Low Crossover Rates, pp. 1–8. IEEE Congress on Evolutionary Computation, Barcelona (2010)
Mallipeddi, R., Suganthan, P.N., Pan, Q.K., Tasgetiren, M.F.: Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl. Soft Comput. 11(2), 1679–1696 (2011)
Wang, Y., Cai, Z., Zhang, Q.: Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans. Evol. Comput. 15(1), 55–66 (2011)
Yong, W., Han-Xiong, L., Tingwen, H., Long, L.: Differential evolution based on covariance matrix learning and bimodal distribution parameter setting. Appl. Soft Comput. 18, 232–247 (2014)
Draa, A., Bouzoubia, S., Boukhalfa, I.: A sinusoidal differential evolution algorithm for numerical optimization. Appl. Soft Comput. 27, 99–126 (2015)
Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2011)
Das, S., Mullick, S.S., Suganthan, P.N.: Recent advances in differential evolution-an updated survey. Swarm Evol. Comput. 27, 1–30 (2016)
Cheng, J.X., Zhang, G.X., Neri, F.: Enhancing distributed differential evolution with multicultural migration for global numerical optimization. Inf. Sci. 247, 72–93 (2013)
Gao, W.F., Pan, Z., Gao, J.: A new highly efficient differential evolution with self-adaptive strategy for multimodal optimization. IEEE Trans. Cybern. 44(8), 1314–1327 (2014)
Mallipeddi, R., Suganthan, P.N.: Empirical study on the effect of population size on differential evolution algorithm. In: Proceedings of IEEE Congress on Evolutionary Computation, Hong Kong (2008)
Wang, H., Wang, W.J., Cui, Z.H., Sun, H., Ranhnamayan, S.: Heterogeneous differential evolution for numerical optimization. Sci. World J. Article ID 318063 (2014)
Gao, W.F., Yen, G.G., Liu, S.Y.: A dual differential evolution with coevolution for constrained optimization. IEEE Trans. Cybern. 45(5), 1094–1107 (2015)
Brest, J., Maucec, M.S.: Self-adaptive differential evolution algorithm using population size reduction and three strategies. Soft. Comput. 15(11), 2157–2174 (2011)
Zamuda, A., Brest, J.: Self-adaptive control parameters’ randomization frequency and propagations in differential evolution. Swarm Evol. Comput. 25, 72–99 (2015)
Piotrowski, A.P.: Review of differential evolution population size. Swarm Evol. Comput. 32, 1–24 (2017)
Mohamed, A.W., Mohamed, A.K.: Adaptive guided differential evolution algorithm with novel mutation for numerical optimization. Int. J. Mach. Learn. Cybern. (2017). https://doi.org/10.1007/s13042-017-0711-7
Laredo, J.L.J., Fernandes, C., Guervós, J.J.M., Gagné, C.: Improving genetic algorithms performance via deterministic population shrinkage. In: GECCO, pp. 819–826 (2009)
Liang, J.J., Qin, B.Y., Suganthan, P.N., Hernandez-Diaz, A.G.: Problem Definitions and Evaluation Criteria for the CEC 2013 Special Session on Real-Parameter Optimization. Zhengzhou University/Nanyang Technological University, Zhengzhou, China/Singapore (2013)
García, S., Molina, D., Lozano, M., Herrera, F.: A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behavior: a case study on the CEC’2005 special session on real parameter optimization. J. Heurist. 15, 617–644 (2009). Springer
Hansen, N., Ostermeier, A.: Cma-es source code (2009). http://www.lri.fr/~hansen/cmaes_inmatlab.html
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Mohamed, A.K., Mohamed, A.W. (2019). Real-Parameter Unconstrained Optimization Based on Enhanced AGDE Algorithm. In: Hassanien, A. (eds) Machine Learning Paradigms: Theory and Application. Studies in Computational Intelligence, vol 801. Springer, Cham. https://doi.org/10.1007/978-3-030-02357-7_21
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