Abstract
In this paper we propose a novel variant of the Differential Evolution (DE) algorithm based on local search. The corresponding algorithm is named as Differential Evolution with Interpolated Local Search (DEILS). In DEILS, the local search operation is applied in an adaptive manner. The adaptive behavior enables the algorithm to search its neighborhood in an effective manner and the interpolation helps in exploiting the solutions. In this way a balance is maintained between the exploration and exploitation factors. The performance of DEILS is investigated and compared with basic differential evolution, modified versions of DE and some other evolutionary algorithms. It is found that the proposed scheme improves the performance of DE in terms of quality of solution without compromising with the convergence rate.
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References
Storn, R., Price, K.: Differential evolution – a simple and efficient adaptive scheme for global optimization over continuous spaces.Technical Report TR-95-012, Berkeley, CA (1995)
Storn, R., Price, K.V.: Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces. J. Global Opt. 11(4), 341–359 (1997)
Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. Springer, Berlin (2005)
Lampinen, J., Zelinka, I.: On stagnation of the differential evolution algorithm. In: Ošmera, P. (ed.) Proc. of MENDEL 2000, 6th International Mendel Conference on Soft Computing, pp. 76–83 (2000)
Pant, M., Thangaraj, R., Abraham, A., Grosan, C.: Differential Evolution with Laplace Mutation Operator. IEEE Congress on Evolutionary Computation, 2841–2849 (2009)
Ali, M., Pant, M., Abraham, A.: Mixed Strategy Embedded Differential Evolution. IEEE Congress on Evolutionary Computation, 2841–2849 (2009)
Brest, J., Zamuda, A., Boskovic, B., Maucec, M.S., Zumer, V.: Dynamic Optimization using Differential Evolution. IEEE Congress on Evolutionary Computation, 415–421 (2009)
Epitropakis, M.G., Plagianakos, V.P., Vrahatis, M.N.: Evolutionary Adaption of the Differential Evolution Control Parameters. IEEE Congress on Evolutionary Computation, 1359–1366 (2009)
Menchaca-Mendez, A., Coello, C.A.C.: A new proposal to hybridize the Nelder Mead Differential Evolution Algorithm for Constrained Optimization. IEEE Congress on Evolutionary Computation, 2598–2605 (2009)
Tasgetiren, M.F., Pan, Q.-K., Suganthan, P.N., Liang, Y.-C.: A Differential Evolution Algorithm with Variable Parameter Search for Real Parameter Continuous Function Optimization. IEEE Congress on Evolutionary Computation, 1247–1254 (2009)
Pant, M., Ali, M., Singh, V.P.: Parent centric differential evolution algorithm for global optimization. J. Opsearch 46(2), 153–168 (2009)
Lai, J.C.Y., Leung, F.H.F., Ling, S.H.: A new Differential Evolution Algorithm with Wavelet Theory based Mutation operation. IEEE Congress on Evolutionary Computation, 1116–1122 (2009)
Omran, G.H.M., Engelbrecht, A.P.: Free Search Differential Evolution. IEEE Congress on Evolutionary Computation, 110–117 (2009)
Fan, H.-Y., Lampinen, J.: A Trigonometric Mutation Operation to Differential Evolution. J. of Global Optimization 27, 105–129 (2003)
Rahnamayan, S., Tizhoosh, H.R., Salama, M.M.A.: A novel population initialization method for accelerating evolutionary algorithms. J. Computer and Applied Mathematics with Application 53, 1605–1614 (2007)
Chakraborty, U.K.: Advances in Differential Evolution. Springer, Heidelberg (2008)
Noman, N., Iba, H.: Accelerating differential evolution using an adaptive local search. J. IEEE transactions on evolutionary computation 12(1), 107–125 (2008)
Mohan, C., Shanker, K.: A Controlled Random Search Technique For Global Optimization using Quadratic Approximation. J. Asia-Pacific Journal of Operational Research 11, 93–101 (1994)
Ali, M.M., Torn, A., Vitanen, D.S.: A numerical comparison of some modified controlled random search algorithms. J. Global Optimization 11, 341–359 (1997)
Li, H., Jiao, Y.C., Wang, Y.P.: Integrating the simplified interpolation into the genetic algorithm for constrained optimization problems, pp. 247–254. Springer, Heidelberg (2005)
Zhang, L., Jiao, Y.-C., Li, H., Zhang, F.-S.: Hybrid Differential Evolution and the Simplified Quadratic Interpolation for Global Optimization. In: GEC 2009, Shanghai, China, June 12-14. ACM, New York (2009), 978-1-60558-326-6/09/06
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Ali, M., Pant, M., Singh, V.P. (2010). Differential Evolution Using Interpolated Local Search. In: Ranka, S., et al. Contemporary Computing. IC3 2010. Communications in Computer and Information Science, vol 94. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14834-7_10
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DOI: https://doi.org/10.1007/978-3-642-14834-7_10
Publisher Name: Springer, Berlin, Heidelberg
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