[1]LIU San-yang,ZHANG Xiao-wei.A hybrid strategy for differential variation[J].CAAI Transactions on Intelligent Systems,2008,3(6):487-491.
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A hybrid strategy for differential variation

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 3 Number of periods: 2008 6 Page number: 487-491 Column: 学术论文—人工智能基础 Public date: 2008-12-25
Title:
A hybrid strategy for differential variation
Author(s):
LIU San-yang1; 2; ZHANG Xiao-wei1
1.Department of Mathematical Sciences, Xidian University, Xi’an 710071, China；2.State Key Lab.of ISN,Xidian University,Xi’an 710071,China
Keywords:
global optimization;  particle swarm optimization;  differential evolution
CLC:
TP18；O224
DOI:
-
Abstract:
A new hybrid strategy for differential variation was developed in order to improve the performance of differential evolution. The strategy regards each individual in the population as a charged particle, and uses the charge level of the particle and the attraction-repulsion mechanism among particles to determine the probable direction and magnitude of motion. This strategy can compel individuals to move adaptively in the direction of the forces exerted on them by three other individuals. Numerical experiments showed that differential evolution based on this strategy has high accuracy and requires fewer evaluations.
References:
［1］STORN R, PRICE K. Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces［J］. Journal of Global Optimization, 1997, 11(4): 341-359.
［2］FEOKTSTOV V. Differential evolution: in search of solutions［M］. Berlin:Springer, 2006.
［3］QIN A, SUGANTHAN P. Self-adaptive differential evolution algorithm for numerical optimization［C］//Proceedings of the 2005 IEEE Congress on Evolutionary Computation. Edinburgh, UK， 2005(2)：1785-1791.
［4］KIM H, CHONG J, PARK K, et al. Differential evolution strategy for constrained global optimization and application to practical engineering problems［J］. IEEE Transactions on Magnetics, 2007, 43(4):1565-1568.
［5］KENNEDY J, EBERHART R. Particle swarm optimization［C］//Proceedings of the IEEE International Joint Conference on Neural Networks. Perth, Australia.1995(4)：1942-1948.
［6］BIRBIL S, FANG S. An electromagnetism like mechanism for global optimization［J］. Journal of Global Optimization，2003, 25(3):263-282.
［7］BIRBIL S, FANG S, SHEU R. On the convergence of a population-based global optimization［J］. Journal of Global Optimization，2004, 30(3):301-318.
［8］DEBELS D, REYCK B, LEUS R, et al. A hybrid scatter search/electromagnetism meta heuristic for project scheduling［J］. European Journal of Operational Research, 2006,169(2): 638-653.
［9］HAO Zhifeng, GUO Guanghan, HUANG Han. A particle swarm optimization algorithm with differential evolution［C］//Proceedings of the Sixth International Conference on Machine Learning and Cybernetics. Hong Kong, China, 2007(2)：1031-1035.
［10］PRICE K. Differential evolution vs. the functions of the 2nd ICEO［C］//Proceeding of the 1997 IEEE International Conference on Evolutionary Computation. Indianapolis, USA,1997, 153-157.
［11］CLERC M, KENNEDY J. The particle swarm explosion, stability, and convergence in a multidimensional complex space［J］. IEEE Transactions on Evolutionary Computation, 2002, 6(1):58-73.
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