Abstract
We present a scheme based on differential evolution and local search to solve continuous optimization problems. Improvements have been made to the basic differential evolution algorithm. We have opted for an approach with three types of population regeneration and parameter value adaptation. These improvements offer a better exploration of the search space and avoid blocking on local optima. In order to intensify the search around the neighbourhood of the solutions obtained by the differential evolution algorithm, a local search is carried out from time to time using a trust-region method. The proposed scheme was tested on the COmparing Continuous Optimizers (COCO) platform, and the obtained results showed the superiority of the proposed approach over state-of-the-art algorithms.
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References
Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11, 341–359 (1997)
Das, S., Mullick, S.S., Suganthan, P.N.: Recent advances in differential evolution: an updated survey. Swarm and Evol. Comput. 27, 1–30 (2016)
Hansan, N., Augery, A., Finck, S., Ros, R.: Real-parameter black-box optimization benchmarking: experimental setup (2015). http://coco.lri.fr/downloads/download15.03/bbobdocexperiment.pdf
Official COCO website (2017). http://coco.gforge.inria.fr/
Qin, A.K., Suganthan, P.N.: Self-adaptive differential evolution algorithm for numerical optimization. In: IEEE Congress on Evolutionary Computation, pp. 1785–1791, Edinburgh (2005)
Brest, J., Greiner, S., Boskovic, 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)
Draa, A., Bouzoubia, S., Boukhalfa, I.: A sinusoidal differential evolution algorithm for numerical optimization. Appl. Soft Comput. 27, 99–126 (2015)
Wu, G., Rammohan, M., Suganthan, P.N., Wang, R., Huangke, C.: Differential evolution with multi-population based ensemble of mutation strategies. Inf. Sci. 329, 329–345 (2016)
Ge, Y., Yu, W., Lin, Y., Gong, Y., Zhan, Z., Chen, W., Zhang, J.: Distributed differential evolution based on adaptive mergence and split for large-scale optimization. IEEE Trans. Cybern. 48(7), 2166–2180 (2018)
Gong, W., Cai, Z., Zhang, J., Jia, L., Li, H.: A generalized hybrid generation scheme of differential evolution for global numerical optimization. Int. J. Comput. Intell. Appl. 10(1), 35–65 (2011)
Chang, L., Liao, C., Lin, W., Chen, L.L., Zheng, X.: A hybrid method based on differential evolution and continuous ant colony optimization and its application on wideband antenna design. Prog. Electromagnet. Res. 122, 105–118 (2012)
Biswal, B., Behera, H.S., Bisoi, R., Dash, P.K.: Classification of power quality data using decision tree and chemotactic differential evolution based fuzzy clustering. Swarm and Evol. Comput. 4, 12–24 (2012)
Abdullah, A., Deris, S., Anwar, S., Arjunan, S.N.V.: An evolutionary firefly algorithm for the estimation of nonlinear biological model parameters. PLoS One 8(3), e56310 (2013)
Lee, C.H., Kuo, C.T., Chang, H.H.: Performance enhancement of the differential evolution algorithm using local search and a self-adaptive scaling factor. Int. J. Innov. Compu. Inf. Control 8(4), 2665–2679 (2012)
Conn, A.R., Gould, N.I.M., Toint, P.L.: Trust-Region Methods. Society for Industrial and Applied Mathematics, Philadelphia (2000)
Posik, P., Klems, V.: Benchmarking the differential evolution with adaptive encoding on noiseless functions. In: Proceedings of the 14th Annual Conference Companion on Genetic and Evolutionary Computation GECCO ’12, pp. 189–196. ACM, New York, NY, USA (2012)
Vogli, C., Piperagkas, G.S., Parsopoulos, K.E., Papageorgiou, D.G., Lagaris, I.E.: Mempsode: an empirical assessment of local search algorithm impact on a memetic algorithm using noiseless testbed. In: Proceedings of the 14th Annual Conference Companion on Genetic and Evolutionary Computation GECCO’12 (2012)
Zhang, J., Sanderson, A.C.: Jade: adaptive differential evolution with optional external archive. IEEE Trans. Evol. Comput. 13(5), 945–958 (2009)
Tanabe, R., Fukunaga, A.: Tuning differential evolution for cheap, medium, and expensive computational budgets. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 2018–2025 (2015)
El-Abd, M., Kamel, M.S.: Black-box optimization benchmarking for noiseless function testbed using particle swarm optimization. In: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference. GECCO ’09, pp. 2269–2274 (2009)
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Talbi, H., Draa, A. (2020). A Continuous Optimization Scheme Based on an Enhanced Differential Evolution and a Trust Region Method. In: Bouhlel, M., Rovetta, S. (eds) Proceedings of the 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT’18), Vol.1. SETIT 2018. Smart Innovation, Systems and Technologies, vol 146. Springer, Cham. https://doi.org/10.1007/978-3-030-21005-2_22
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DOI: https://doi.org/10.1007/978-3-030-21005-2_22
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