Differential Evolution (DE) algorithm generates a population of individuals by encoding with a floating point vector, and it is a simple and effective population-based stochastic optimization algorithm for global optimization of continuous space. Because of its excellent performance, DE variants can be applied in a wide range of applications in science and engineering. However, the performance of DE is sensitive to the choice of trial vector generation strategy and the associated control parameters. Therefore, it is necessary to choose appropriate mutation strategy and control parameters when tackling optimization applications. In this paper, an adaptive update mechanism is proposed to update control parameters F and Cr. The experimental results are verified on the CEC 2013 test suite which contains 28 benchmark functions for the evaluation of single objective real parameter optimization. The proposed algorithm is compared with jDE, iwPSO and ccPSO, and experiment results show its good performance.
DasS. and SuganthanP.N., Differential evolution: A survey of the state-of-the-art, IEEE Transactions on Evolutionary Computation15(1) (2010), 4–31.
2.
MengZ., PanJ.-S. and TsengK.-K., PaDE: An enhanced differential evolution algorithm with novel control parameter adaptation schemes for numerical optimization, Knowledge-Based Systems168 (2019), 80–99.
3.
EberhartR. and KennedyJ., Particle swarm optimization. In Proceedings of the IEEE international conference on neural networks, volume 4, pages 1942–1948Citeseer, 1995.
4.
MengZ., PanJ.-S. and AlelaiwiA., A new meta-heuristic ebb-tide-fish-inspired algorithm for traffic navigation, Telecommunication Systems62(2) (2016), 403–415.
5.
WangH., WangW., CuiZ., ZhouX., ZhaoJ. and LiY., A new dynamic firefly algorithm for demand estimation of water resources, Information Sciences438 (2018), 95–106.
6.
PanJ.-S., MengZ., ChuS.-C. and XuH.-R., Monkey King Evolution: An enhanced ebb-tide-fish algorithm for global optimization and its application in vehicle navigation under wireless sensor network environment, Telecommunication Systems65(3) (2017), 351–364.
7.
WangJ., GaoY., LiuW., SangaiahA.K. and KimH.-J., An improved routing schema with special clustering using pso algorithm for heterogeneous wireless sensor network, Sensors19(3) (2019), 671.
8.
MengZ. and PanJ.-S., Monkey King Evolution: A new memetic evolutionary algorithm and its application in vehicle fuel consumption optimization, Knowledge-Based Systems97 (2016), 144–157.
9.
HeQ. and WangL., A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization, Applied mathematics and computation186(2) (2007), 1407–1422.
10.
PanJ.-S., KongL., SungT.-W., TsaiP.-W. and SnaselW., α-fraction first strategy for hierarchical wireless sensor neteorks, J Internet Technol19(6) (2018), 1717–1726.
11.
MengZ. and PanJ.-S., A simple and accurate global optimizer for continuous spaces optimization. In Genetic and evolutionary computing, pages 121–129. Springer, 2015.
12.
WangJ., JuC., GaoY., SangaiahA.K. and KimG.-J., A pso based energy efficient coverage control algorithm for wireless sensor networks, Comput Mater Contin56 (2018), 433–446.
13.
MengZ., PanJ.-S. and XuH., QUasi-Affine TRansformation Evolutionary (QUATRE) algorithm: a cooperative swarm based algorithm for global optimization, Knowledge-Based Systems109 (2016), 104–121.
14.
EberhartR. and KennedyJ., A new optimizer using particle swarm theory. In MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pages 39–43. IEEE, 1995.
15.
MengZ. and PanJ.-S., QUasi-Affine TRansformation Evolutionary (QUATRE) algorithm: a parameter-reduced differential evolution algorithm for optimization problems. In 2016 IEEE Congress on Evolutionary Computation (CEC), pages 4082–4089, IEEE, 2016.
16.
ShiY. and EberhartR., A modified particle swarm optimizer. In 1998 IEEE international conference on evolutionary computation proceedings. IEEE world congress on computational intelligence (Cat. No. 98TH8360), pages 69–73. IEEE, 1998.
17.
MengZ. and PanJ.-S., QUasi-Affine TRansformation Evolution with External ARchive (QUATRE–EAR): an enhanced structure for differential evolution, Knowledge-Based Systems155 (2018), 35–53.
18.
EberhartR.C. and ShiY., Comparing inertia weights and constriction factors in particle swarm optimization. In Proceedings of the 2000 congress on evolutionary computation. CEC00 (Cat. No. 00TH8512), volume 1, pages 84–88. IEEE, 2000.
19.
MengZ. and PanJ.-S., HARD–DE: Hierarchical archive based mutation strategy with depth information of evolution for the enhancement of differential evolution on numerical optimization, IEEE Access7 (2019), 12832–12854.
20.
StornR. and PriceK., Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces, Journal of global optimization11(4) (1997), 341–359.
21.
MengZ., PanJ.-S. and ZhengW.-M., Differential evolution utilizing a handful top superior individuals with bionic bi-population structure for the enhancement of optimization performance, Enterprise Information Systems, pages 1–22, 2018.
22.
KirkpatrickS., GelattC.D. and VecchiM.P., Optimization by simulated annealing, Science220(4598) (1983), 671–680.
23.
WuJ.M.-T., et al., High-utility item set mining with effective pruning strategies, ACM Transactions on Knowledge Discovery from Data13(6) (2019), 58.
24.
MengZ., PanJ.-S. and KongL., Parameters with Adaptive Learning Mechanism (PALM) for the enhancement of differential evolution, Knowledge-Based Systems141 (2018), 92–112.
25.
PriceK., StornR.M. and LampinenJ.A., Differential evolution: A practical approach to global optimization. Springer Science & Business Media, 2006.
26.
MengZ. and PanJ.-S., QUasi-Affine TRansformation Evolutionary (QUATRE) algorithm: the framework analysis for global optimization and application in hand gesture segmentation. In 2016 IEEE 13th International Conference on Signal Processing (ICSP), pages 1832–1837, IEEE, 2016.
27.
BrestJ., ZamudaA., FisterI. and BoskovicB., Some improvements of the self-adaptive jde algorithm. In 2014 IEEE Symposium on Differential Evolution (SDE), pages 1–8. IEEE, 2014.
28.
MengZ. and PanJ.-S., A Competitive QUasi-Affine TRansformation Evolutionary (C-QUATRE) algorithm for global optimization. In 2016 IEEE International Conference on Systems, Man and Cybernetics (SMC), pages 001644–001649. IEEE, 2016.
29.
PengF., TangK., ChenG. and YaoX., Multi-start jade with knowledge transfer for numerical optimization. In 2009 IEEE Congress on Evolutionary Computation, pages 1889–1895, IEEE, 2009.
30.
MengZ., PanJ.-S. and LiX., The QUuasi-Affine TRansformation Evolution (QUATRE) algorithm: an overview. In The Euro-China Conference on Intelligent Data Analysis and Applications, pages 324–333. Springer, 2017.
31.
PanJ.-S., MengZ., ChuS.-C. and RoddickJ.F., QUATRE algorithm with sort strategy for global optimization in comparison with DE and PSO variants. In The Euro-China Conference on Intelligent Data Analysis and Applications, pages 314–323. Springer, 2017.
32.
BrestJ., GreinerS., BoskovicB., MernikM. and ZumerV., Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems, IEEE transactions on evolutionary computation10(6) (2006), 646–657.
33.
PanJ.-S., LeeC.-Y., SghaierA., ZeghidM. and XieJ., Novel systolization of subquadratic space complexity multipliers based on toeplitz matrix–vector product approach, IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2019.
34.
PanJ.-S., MengZ., XuH. and LiX., A matrix–based implementation of DE algorithm: the compensation and deficiency. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pages 72–81. Springer, 2017.
35.
PanJ.-S., KongL., SungT.-W., TsaiP.-W. and SnášelV., A clustering scheme for wireless sensor networks based on genetic algorithm and dominating set, Journal of Internet Technology19(4) (2018), 1111–1118.
36.
WuT.-Y., ChenC.-M., WangK.-H., MengC. and WangE.K., A provably secure certificateless public key encryption with keyword search, Journal of the Chinese Institute of Engineers42(1) (2019), 20–28.
37.
ChenC.-M., WangK.-H., YehK.-H., XiangB. and WuT.-Y., Attacks and solutions on a three-party password-based authenticated key exchange protocol for wireless communications, Journal of Ambient Intelligence and Humanized Computing10(8) (2019), 3133–3142.
38.
ChenC.-M., XiangB., LiuY. and WangK.-H., A secure authentication protocol for internet of vehicles, IEEE Access7 (2019), 12047–12057.
