Adapting differential evolution algorithms for continuous optimization via greedy adjustment of control parameters

• Autorzy: Leon M. , Xiong N.

Identyfikatory

DOI

10.1515/jaiscr-2016-0009

• Języki publikacji: EN

Abstrakty

EN

Differential evolution (DE) presents a class of evolutionary and meta-heuristic techniques that have been applied successfully to solve many real-world problems. However, the performance of DE is significantly influenced by its control parameters such as scaling factor and crossover probability. This paper proposes a new adaptive DE algorithm by greedy adjustment of the control parameters during the running of DE. The basic idea is to perform greedy search for better parameter assignments in successive learning periods in the whole evolutionary process. Within each learning period, the current parameter assignment and its neighboring assignments are tested (used) in a number of times to acquire a reliable assessment of their suitability in the stochastic environment with DE operations. Subsequently the current assignment is updated with the best candidate identified from the neighborhood and the search then moves on to the next learning period. This greedy parameter adjustment method has been incorporated into basic DE, leading to a new DE algorithm termed as Greedy Adaptive Differential Evolution (GADE). GADE has been tested on 25 benchmark functions in comparison with five other DE variants. The results of evaluation demonstrate that GADE is strongly competitive: it obtained the best rank among the counterparts in terms of the summation of relative errors across the benchmark functions with a high dimensionality.

Słowa kluczowe

EN

differential evolution; optimization; parameter adaptation

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2016
• Tom: Vol. 6, No. 2
• Strony: 103--118
• Opis fizyczny: Bibliogr. 26 poz., rys.

Twórcy

autor

Leon M.

• School of Innovation, Design and Engineering Malardalen University, Vasteras, Sweden

autor

Xiong N.

• School of Innovation, Design and Engineering Malardalen University, Vasteras, Sweden

Bibliografia

• [1] N. Xiong, D. Molina, M. Leon, and F. Herrera, A walk into metaheuristics for engineering optimization: Principles, methods, and recent trends, International Journal of Computational Intelligence Systems, vol. 8, no. 4, pp. 606–636, 2015.
• [2] N. Hansen and A. Ostermeier, Completely derandomized self-adaptation in evolution strategies, Evolutionary Computation, vol. 9, no. 2, pp. 159–195, 2001.
• [3] F. Herrera and M. Lozano, Two-loop real-coded genetic algorithms with adaptive control of mutation step size, Applied Intelligence, vol. 13, pp. 187–204, 2000.
• [4] D. Molina, M. Lozano, A. M. Sanchez, and F. Herrera, Memetic algorithms based on local search chains for large scale continuous optimization problems: Ma-ssw-chains, Soft Computing, vol. 15, pp. 2201–2220, 2011.
• [5] R. Storn and K. Price, Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces, Journal of Global Optimization, vol. 11, no. 4, pp. 341 – 359, 1997.
• [6] J. Kenedy and R. C. Eberhart, Particle swarm optimization, in In Proc. IEEE Conference on Neural Networks, 1995, pp. 1942–1948.
• [7] D. Karaboga, B. Gorkemli, C.Ozturk, and N. Karaboga, A comprehensive survey: artificial bee colony (abc) algorithm and applications, Artificial Intelligence Review, vol. 42, no. 1, pp. 21–57, 2012.
• [8] M. Ali and A. Torn, Population set based global optimization algorithms: Some modifications and numerical studies, Computers and Operations Research, vol. 31, pp. 1703–1725, 2004.
• [9] S. Garcia, D. Molina, M. Lozano, and F. Herrera, A study on the use of non-parametric tests for analyzing the evolutionary algorithmss behaviour: A case study on the cec2005special session on real parameter optimization, Journal of Heuristics, vol. 15, no. 6, pp. 617–644, 2009.
• [10] S. Das and N. Suganthan, Differential evolution: A survey of the state-of-the-art, in IEEE Transactions on Evolutionary Computation, vol. 15, no. 1, 2011, pp. 4–31.
• [11] R. Gamperle, S. D. Muller, and P. Koumoutsakos, A parameter study for differential evolution, in Advances in intelligent systems, fuzzy systems, evolutionary computation, vol. 10, 2002, pp. 293–298.
• [12] K. Zielinski, P. Weitkemper, R. Laur, and K. D. Kammeyer, Parameter study for differential evolution using a power allocation problem including interference cancellation, in IEEE Congress on Evolutionary Computation, 2006, pp. 1857–1864.
• [13] J. Zhang and A. C. Sanderson, An approximate gaussian model of differential evolution with spherical fitness functions, in Proc. IEEE Congress on Evolutionary Computation, 2007, pp. 2220–2228.
• [14] J. Liu and J. Lampinen, A fuzzy adaptive differential evolution algorithm, Soft Computing, vol. 9, no. 6, pp. 448–462, 2005.
• [15] F. Xue, A. C. Sanderson, P. P. Bonissone, and R. J. Graves, Fuzzy logic controlled multiobjective differential evolution, in Proc. IEEE Conference on Fuzzy Systems, 2005, pp. 720–725.
• [16] A. Qin and P. Suganthan, Self-adaptive differential evolution algorithm for numerical optimization, The 2005 IEEE Congress on Evolutionary Computation, vol. 2, pp. 1785–1791, 2005.
• [17] J. Zhang and A. Sanderson, Jade: Adaptive differential evolution with optional external archive, IEEE Transactions on Evolutionary Computation, vol. 13, pp. 945–958, 2009.
• [18] S. M. Islam, S. Das, S. Ghoshand, S. Roy, and P. N. Suganthan, An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization, Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, vol. 42, no. 2, pp. 482–500, 2012.
• [19] Z. Yang, K. Tang, and X. Yao, Scability of generalized adaptive differential evolution for large-scale continuous optimization, Soft Computing, vol. 15, no. 11, pp. 2141–2155, 2001.
• [20] R. Tanabe and A. Fukinga, Success-history based parameter adaptation for differential evolution, in 2013 IEEE Congress on Evolutionary Computation (CEC), Cancun, Mexico, 2013, pp. 71–78.
• [21] M. Leon and N. Xiong, Investigation of mutation strategies in differential evolution for solving global optimization problems, in Artificial Intelligence and Soft Computing. springer, June 2014, pp. 372–383.
• [22] X. Yao, Y. Liu, and G. Lin, Evolutionary programming made faster, in Proc. IEEE Transactions on Evolutionary Computation, vol. 3, no. 2, 1999, pp. 82–102.
• [23] P. N. Suganthan, N. Hansen, J. J. Liang, K. Deb, Y. P. Chen, A. Auger, and S. Tiwari, Problem definitions and evaluation criteria for the cec 2005 special session on real-parameter optimization, Technical Report, Nanyang Technological University, Singapore And KanGAL Report Number 2005005 (Kanpur Genetic Algorithms Laboratory, IIT Kanpur), Tech. Rep., May 2005.
• [24] D. Wolpert and W. Macready, No free lunch theorems for optimization, IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, pp. 67–82, 1997.
• [25] D. Whitley and J. Rowe, Focused no free lunch theorems, in Proc. Conf. Genetic Evolutionary Computing, 2008, pp. 811–818.
• [26] M. Leon and N. Xiong, Using random local search helps in avoiding local optimum in diefferential evolution, in Proc. Artificial Intelligence and Applications, AIA2014, Innsbruck, Austria, 2014, pp. 413–420.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.