Abstract
Differential Evolution (DE) is one of the most simple and efficient Evolutionary Algorithms exist till now for global optimization problems. It has reported exceptionally good results when tested over all the benchmark problems and some of the real world problems, although it suffers from the troubles of slow and premature convergence. Generally the performance of DE is sensitive to the choice of mutation and crossover strategies and their associated control parameters. In this paper we propose a DE called Adaptive Differential Evolution with Directional Information based Search Moves (ADE-DISM) in which we basically have improved the mutation and crossover strategies adopted in ‘DE/rand/1/bin’. In ADE-DISM we varied the control parameters F and CR in an adaptive manner and have introduced a new parameter w. We have used some directional information based moves over the population and introduced a Mean_Best_Vector for mutation purpose. However, the proposed scheme is shown to be statistically significantly better than or at least comparable to several existing DE variants when tested over the CEC 2005 benchmark problems for 30 and 50 dimensions of the problems.
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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, ICSI (1995), http://http.icsi.berkeley.edu/~storn/litera.html
Joshi, R., Sanderson, A.C.: Minimal representation multisensor fusion using differential evolution. IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans 29(1), 63–76 (1999)
Rogalsky, R., Derksen, R.W., Kocabiyik, S.: Differential evolution in aerodynamic optimization. In: Proc. of 46th Annual Conference of Canadian Aeronautics and Space Institute, pp. 29–36 (1999)
Storn, R.: On the usage of differential evolution for function optimization. In: Biennial Conference of the North American Fuzzy Information Processing Society (NAFIPS), pp. 519–523. IEEE, Berkeley (1996)
Venu, M.K., Mallipeddi, R., Suganthan, P.N.: Fiber bragg grating sensor array interrogation using differential evolution. Optoelectronics and Advanced Materials - Rapid Communications 2(11), 682–685 (2008)
Chakraborthy, U.K., Das, S., Konar, A.: Differential evolution with local neighborhood. In: Proceedings of Congress on Evolutionary Computation, pp. 2042–2049. IEEE Press (2006)
Gämperle, R., Müller, S.D., Koumoutsakos, P.: A parameter study for differential evolution. In: Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation, pp. 293–298. WSEAS Press, Interlaken (2002)
Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11(4), 341–359 (1997)
Rönkkönen, J., Kukkonen, S., Price, K.: Real-parameter optimization with differential evolution. In: IEEE Congress on Evolutionary Computation, pp. 506–513 (2005)
Brest, J., Greiner, S., Boscovic, B., Mernik, S., Zumer, V.: Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE Transactions on Evolutionary Computation 10(8), 646–657 (2006)
Zhang, J.: Jade: Adaptive differential evolution with optional external archive. IEEE Transactions on Evolutionary Computation 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 Transactions on Evolutionary Computation 13(2), 398–417 (2009)
Das, S., Konar, A., Chakraborty, U.K.: Two improved differential evolution schemes for faster global search. In: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, pp. 991–998 (2005)
Mallipeddi, R., Suganthan, P.N.: Differential Evolution Algorithm with Ensemble of Parameters and Mutation and Crossover Strategies. Nanyang Technological University, Singapore
Zhang, X., Yuen, Yin, S.: A Directional Mutation Operator for Differential Evolution Algorithms, City University of Hong Kong, Electronic Engineering.
Suganthan, P. N, Hansen, N, Liang, J. J, Deb, K, Chen, Y. P., Auger, A., Tiwari, S.: Problem definitions and evaluation criteria for the CEC2005 special session on real-parameter optimization, Nanyang Technol. Univ., Singapore (May 2005)
Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y.-P., Auger, A., Tiwari, S.: Problem definitions and evaluation criteria for the CEC2005 special session on real-parameter optimization. IIT, Kanpur, India, KanGAL Rep. 2005005 (May 2005)
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Neogi, S., Das, D., Das, S. (2012). Adaptive Differential Evolution with Directional Information Based Search Moves. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Nanda, P.K. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2012. Lecture Notes in Computer Science, vol 7677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35380-2_51
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DOI: https://doi.org/10.1007/978-3-642-35380-2_51
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