Log in

Relevant bibliographies by topics / Differential Evolution / Journal articles

Journal articles on the topic 'Differential Evolution'

To see the other types of publications on this topic, follow the link: Differential Evolution.

Author: Grafiati

Published: 4 June 2021

Last updated: 30 January 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Differential Evolution.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Ali, M. M. "Differential evolution with generalized differentials." Journal of Computational and Applied Mathematics 235, no. 8 (February 2011): 2205–16. http://dx.doi.org/10.1016/j.cam.2010.10.018.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Wang, Shir Li, Theam Foo Ng, and Farid Morsidi. "Self-adaptive Ensemble Based Differential Evolution." International Journal of Machine Learning and Computing 8, no. 3 (June 2018): 286–93. http://dx.doi.org/10.18178/ijmlc.2018.8.3.701.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Worasucheep, Chukiat. "A Hybrid Artificial Bee Colony with Differential Evolution." International Journal of Machine Learning and Computing 5, no. 3 (June 2015): 179–86. http://dx.doi.org/10.7763/ijmlc.2015.v5.504.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

ONUKI, Ryosuke, Satoshi KITAYAMA, Koetsu YAMAZAKI, and Masao ARAKAWA. "Proposal of Adaptive Range Differential Evolution." TRANSACTIONS OF THE JAPAN SOCIETY OF MECHANICAL ENGINEERS Series C 79, no. 798 (2013): 429–41. http://dx.doi.org/10.1299/kikaic.79.429.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Veloso de Melo, Vinícius, Danilo Vasconcellos Vargas, and Marcio Kassouf Crocomo. "Phylogenetic Differential Evolution." International Journal of Natural Computing Research 2, no. 1 (January 2011): 21–38. http://dx.doi.org/10.4018/jncr.2011010102.

Full text

Abstract:

This paper presents a new technique for optimizing binary problems with building blocks. The authors have developed a different approach to existing Estimation of Distribution Algorithms (EDAs). Our technique, called Phylogenetic Differential Evolution (PhyDE), combines the Phylogenetic Algorithm and the Differential Evolution Algorithm. The first one is employed to identify the building blocks and to generate metavariables. The second one is used to find the best instance of each metavariable. In contrast to existing EDAs that identify the related variables at each iteration, the presented technique finds the related variables only once at the beginning of the algorithm, and not through the generations. This paper shows that the proposed technique is more efficient than the well known EDA called Extended Compact Genetic Algorithm (ECGA), especially for large-scale systems which are commonly found in real world problems.

APA, Harvard, Vancouver, ISO, and other styles

6

Mininno, Ernesto, Ferrante Neri, Francesco Cupertino, and David Naso. "Compact Differential Evolution." IEEE Transactions on Evolutionary Computation 15, no. 1 (February 2011): 32–54. http://dx.doi.org/10.1109/tevc.2010.2058120.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Kamiyama, Daichi, Kenichi Tamura, and Keiichiro Yasuda. "Down-hill Simplex Method Based Differential Evolution." IEEJ Transactions on Electronics, Information and Systems 130, no. 7 (2010): 1271–72. http://dx.doi.org/10.1541/ieejeiss.130.1271.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Liu, Yong-Jin, Chun-Xu Xu, Ran Yi, Dian Fan, and Ying He. "Manifold differential evolution (MDE)." ACM Transactions on Graphics 35, no. 6 (November 11, 2016): 1–10. http://dx.doi.org/10.1145/2980179.2982424.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Ardia, David, Kris Boudt, Peter Carl, Katharine,M Mullen, and Brian,G Peterson. "Differential Evolution with DEoptim." R Journal 3, no. 1 (2011): 27. http://dx.doi.org/10.32614/rj-2011-005.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Rahnamayan, S., H. R. Tizhoosh, and M. M. A. Salama. "Opposition-Based Differential Evolution." IEEE Transactions on Evolutionary Computation 12, no. 1 (February 2008): 64–79. http://dx.doi.org/10.1109/tevc.2007.894200.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Cai, Yiqiao, Jingliang Liao, Tian Wang, Yonghong Chen, and Hui Tian. "Social learning differential evolution." Information Sciences 433-434 (April 2018): 464–509. http://dx.doi.org/10.1016/j.ins.2016.10.003.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Cai, Yiqiao, Meng Zhao, Jingliang Liao, Tian Wang, Hui Tian, and Yonghong Chen. "Neighborhood guided differential evolution." Soft Computing 21, no. 16 (March 5, 2016): 4769–812. http://dx.doi.org/10.1007/s00500-016-2088-z.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Sharma, Harish, Jagdish Chand Bansal, and K. V. Arya. "Fitness based Differential Evolution." Memetic Computing 4, no. 4 (November 6, 2012): 303–16. http://dx.doi.org/10.1007/s12293-012-0096-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Omran, Mahamed G. H., Andries P. Engelbrecht, and Ayed Salman. "Bare bones differential evolution." European Journal of Operational Research 196, no. 1 (July 2009): 128–39. http://dx.doi.org/10.1016/j.ejor.2008.02.035.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Sharma, Harish, Jagdish Chand Bansal, and K. V. Arya. "Self Balanced Differential Evolution." Journal of Computational Science 5, no. 2 (March 2014): 312–23. http://dx.doi.org/10.1016/j.jocs.2012.12.002.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Zhan, Zhi-Hui, Zi-Jia Wang, Hu Jin, and Jun Zhang. "Adaptive Distributed Differential Evolution." IEEE Transactions on Cybernetics 50, no. 11 (November 2020): 4633–47. http://dx.doi.org/10.1109/tcyb.2019.2944873.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Iacca, Giovanni, Ernesto Mininno, and Ferrante Neri. "Composed compact differential evolution." Evolutionary Intelligence 4, no. 1 (December 24, 2010): 17–29. http://dx.doi.org/10.1007/s12065-010-0046-8.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Bui, Ngoc Tam, and Hiroshi Hasegawa. "Training Artificial Neural Network Using Modification of Differential Evolution Algorithm." International Journal of Machine Learning and Computing 5, no. 1 (February 2015): 1–6. http://dx.doi.org/10.7763/ijmlc.2015.v5.473.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Salman, Ayed A., Imtiaz Ahmad, and Mahmad G. H. Omran. "Stochastic Diffusion Binary Differential Evolution to Solve Multidimensional Knapsack Problem." International Journal of Machine Learning and Computing 6, no. 2 (April 2016): 130–33. http://dx.doi.org/10.18178/ijmlc.2016.6.2.586.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Bharothu, Dr Jyothilal Nayak, Dr B. Madhu Kiran, Dr G. Kishor Babu, and B. N. V. Satish Kumar Kolla. "IEEE -30 Bus System Study with Memetic Differential Evolution Algorithm." Journal of Advanced Research in Dynamical and Control Systems 11, no. 11 (November 20, 2019): 86–96. http://dx.doi.org/10.5373/jardcs/v11i11/20193172.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Hu, Gui Wu, and Xiao Yong Du. "Cellular Differential Evolution for Optimization." Key Engineering Materials 474-476 (April 2011): 1770–75. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.1770.

Full text

Abstract:

This paper is to illustrate the Cellular Differential Evolution with the cellular structure originated from Cellular automata. Cellular neighbor local search has been designed; base vector or global best in mutation operator is substituted by neighborhood-best, which overcomes the weakness of single selection relating to global best, and balances the contradiction of local and global search, and improves the diversity of population. In addition, cellular structure ensures information exchange, inheritance and diffusion. Finally, a specific algorithm has been implemented: compared with similar variants of DE, the simulation results on 9 benchmark functions demonstrate that cellular differential evolutions are provided with obvious advantages in the solution-quality, stability and speed. <b></b>

APA, Harvard, Vancouver, ISO, and other styles

22

Zhang, Zijia, Yaoming Cai, and Dongfang Zhang. "Solving Ordinary Differential Equations With Adaptive Differential Evolution." IEEE Access 8 (2020): 128908–22. http://dx.doi.org/10.1109/access.2020.3008823.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

KITAYAMA, Satoshi, Masao ARAKAWA, and Koetsu YAMAZAKI. "1210 Proposal of Discrete Differential Evolution." Proceedings of Design & Systems Conference 2012.22 (2012): _1210–1_—_1210–9_. http://dx.doi.org/10.1299/jsmedsd.2012.22._1210-1_.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Su, Haijun, and Yupu Yang. "Differential evolution and quantum-inquired differential evolution for evolving Takagi–Sugeno fuzzy models." Expert Systems with Applications 38, no. 6 (June 2011): 6447–51. http://dx.doi.org/10.1016/j.eswa.2010.11.107.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Kim, Youngmin, Yong-Kuk Jeong, SuHeon Ju, Jong-Gye Shin, and Jung-Hack Shin. "Development of a Naval Vessel Compartment Arrangement Application using Differential Evolution Algorithm." Transactions of the Society of CAD/CAM Engineers 19, no. 4 (December 1, 2014): 410–22. http://dx.doi.org/10.7315/cadcam.2014.410.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Li, Kangshun, Liang Zhong, Lei Zuo, and Zhaopeng Wang. "Constrained evolution algorithm based on adaptive differential evolution." International Journal of High Performance Computing and Networking 11, no. 3 (2018): 223. http://dx.doi.org/10.1504/ijhpcn.2018.091893.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Zuo, Lei, Zhaopeng Wang, Liang Zhong, and Kangshun Li. "Constrained evolution algorithm based on adaptive differential evolution." International Journal of High Performance Computing and Networking 11, no. 3 (2018): 223. http://dx.doi.org/10.1504/ijhpcn.2018.10012998.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Li, Binxu, and Panchi Li. "Quantum Inspired Differential Evolution Algorithm." Open Journal of Optimization 04, no. 02 (2015): 31–39. http://dx.doi.org/10.4236/ojop.2015.42004.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Shuzhen Wan, Shengwu Xiong, and Jialiang Kou. "An Improved Trigonometric Differential Evolution." International Journal of Advancements in Computing Technology 3, no. 11 (December 31, 2011): 156–62. http://dx.doi.org/10.4156/ijact.vol3.issue11.20.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Zámečníková, Hana, Daniela Einšpiglová, Radka Poláková, and Petr Bujok. "Is Differential Evolution Rotationally Invariant?" Tatra Mountains Mathematical Publications 72, no. 1 (December 1, 2018): 155–65. http://dx.doi.org/10.2478/tmmp-2018-0027.

Full text

Abstract:

Abstract In this paper, we study a problem of the control parameter settings in Differential Evolution algorithm and test a novel variant of the algorithm called CoBiDE. Although Differential Evolution with basic setting (i.e., CR=0.5; F =0.5) works quite well, it starts to fail on rotated functions. In general, we want to improve the convergence of algorithm primarily on rotated functions. It is done by adapting crossover parameter CR whereas parameter F is fixed to 0.5. There is a recommendation to set CR = 1 for rotated functions. It means that trial vectors are essentially composed from mutant. However, it is not easy task to set the parameters appropriately for solving optimization problem but it is crucial for obtaining good results. Moreover, the quality of points produced in evolution is highly affected by the coordinate system. In CoBiDE, the authors proposed a new coordinate system based on the current distribution of points in the population. We test these two approaches by running both algorithms on six pairs of rotated and non-rotated functions from CEC 2013 benchmark set in two levels of dimension space. This experimental study aims to reveal if such algorithm’s setting is invariant under a rotation.

APA, Harvard, Vancouver, ISO, and other styles

31

Osuna-Enciso, Valentín, and Elizabeth Guevara-Martínez. "A Stigmergy-Based Differential Evolution." Applied Sciences 12, no. 12 (June 15, 2022): 6093. http://dx.doi.org/10.3390/app12126093.

Full text

Abstract:

Metaheuristic algorithms are techniques that have been successfully applied to solve complex optimization problems in engineering and science. Many metaheuristic approaches, such as Differential Evolution (DE), use the best individual found so far from the whole population to guide the search process. Although this approach has advantages in the algorithm’s exploitation process, it is not completely in agreement with the swarms found in nature, where communication among individuals is not centralized. This paper proposes the use of stigmergy as an inspiration to modify the original DE operators to simulate a decentralized information exchange, thus avoiding the application of a global best. The Stigmergy-based DE (SDE) approach was tested on a set of benchmark problems to compare its performance with DE. Even though the execution times of DE and SDE are very similar, our proposal has a slight advantage in most of the functions and can converge in fewer iterations in some cases, but its main feature is the capability to maintain a good convergence behavior as the dimensionality grows, so it can be a good alternative to solve complex problems.

APA, Harvard, Vancouver, ISO, and other styles

32

WANG Shenwen, DING Lixin, SHU Wanneng, XIE Chenwang, and YANG Hua. "Differential Evolution Without Scaling Factor." International Journal of Advancements in Computing Technology 4, no. 4 (March 15, 2012): 41–48. http://dx.doi.org/10.4156/ijact.vol4.issue4.6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Kononova, Anna V., Fabio Caraffini, and Thomas Bäck. "Differential evolution outside the box." Information Sciences 581 (December 2021): 587–604. http://dx.doi.org/10.1016/j.ins.2021.09.058.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Lichtblau, Daniel. "Differential Evolution in Discrete Optimization." International Journal of Swarm Intelligence and Evolutionary Computation 1 (2012): 1–10. http://dx.doi.org/10.4303/ijsiec/z110301.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Hui Wang, S. Rahnamayan, Hui Sun, and M. G. H. Omran. "Gaussian Bare-Bones Differential Evolution." IEEE Transactions on Cybernetics 43, no. 2 (April 2013): 634–47. http://dx.doi.org/10.1109/tsmcb.2012.2213808.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Du, Yongzhao, Yuling Fan, Xiaofang Liu, Yanmin Luo, Jianeng Tang, and Peizhong Liu. "Multiscale Cooperative Differential Evolution Algorithm." Computational Intelligence and Neuroscience 2019 (December 17, 2019): 1–17. http://dx.doi.org/10.1155/2019/5259129.

Full text

Abstract:

A multiscale cooperative differential evolution algorithm is proposed to solve the problems of narrow search range at the early stage and slow convergence at the later stage in the performance of the traditional differential evolution algorithms. Firstly, the population structure of multipopulation mechanism is adopted so that each subpopulation is combined with a corresponding mutation strategy to ensure the individual diversity during evolution. Then, the covariance learning among populations is developed to establish a suitable rotating coordinate system for cross operation. Meanwhile, an adaptive parameter adjustment strategy is introduced to balance the population survey and convergence. Finally, the proposed algorithm is tested on the CEC 2005 benchmark function and compared with other state-of-the-art evolutionary algorithms. The experiment results showed that the proposed algorithm has better performance in solving global optimization problems than other compared algorithms.

APA, Harvard, Vancouver, ISO, and other styles

37

Poole, Daniel J., and Christian B. Allen. "Constrained niching using differential evolution." Swarm and Evolutionary Computation 44 (February 2019): 74–100. http://dx.doi.org/10.1016/j.swevo.2018.11.004.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Fan, Qinqin, Xuefeng Yan, and Yu Xue. "Prior knowledge guided differential evolution." Soft Computing 21, no. 22 (June 27, 2016): 6841–58. http://dx.doi.org/10.1007/s00500-016-2235-6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Bedri Ozer, Ahmet. "CIDE: Chaotically Initialized Differential Evolution." Expert Systems with Applications 37, no. 6 (June 2010): 4632–41. http://dx.doi.org/10.1016/j.eswa.2009.12.045.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Ali, M. M. "Differential evolution with preferential crossover." European Journal of Operational Research 181, no. 3 (September 2007): 1137–47. http://dx.doi.org/10.1016/j.ejor.2005.06.077.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Sun, Liang, Hongwei Ge, and Limin Wang. "Convergent Stochastic Differential Evolution Algorithms." International Journal of Hybrid Information Technology 9, no. 7 (July 31, 2016): 191–206. http://dx.doi.org/10.14257/ijhit.2016.9.7.18.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Rubio, J. E. "Partial differential equations of evolution." Endeavour 15, no. 4 (January 1991): 190. http://dx.doi.org/10.1016/0160-9327(91)90135-x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Vidossich, Giovanni. "Differential inequalities for evolution equations." Nonlinear Analysis: Theory, Methods & Applications 25, no. 9-10 (November 1995): 1063–69. http://dx.doi.org/10.1016/0362-546x(95)00101-z.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Rahnamayan, Shahryar, Jude Jesuthasan, Farid Bourennani, Greg F. Naterer, and Hojjat Salehinejad. "Centroid Opposition-Based Differential Evolution." International Journal of Applied Metaheuristic Computing 5, no. 4 (October 2014): 1–25. http://dx.doi.org/10.4018/ijamc.2014100101.

Full text

Abstract:

The capabilities of evolutionary algorithms (EAs) in solving nonlinear and non-convex optimization problems are significant. Differential evolution (DE) is an effective population-based EA, which has emerged as very competitive. Since its inception in 1995, multiple variants of DE have been proposed with higher performance. Among these DE variants, opposition-based differential evolution (ODE) established a novel concept in which individuals must compete with theirs opposites in order to make an entry in the next generation. The generation of opposite points is based on the current extreme points (i.e., maximum and minimum) in the search space. This paper develops a new scheme that utilizes the centroid point of a population to calculate opposite individuals. The classical scheme of an opposite point is modified. Incorporating this new scheme into DE leads to an enhanced ODE that is identified as centroid opposition-based differential evolution (CODE). The accuracy of the CODE algorithm is comprehensively evaluated on well-known complex benchmark functions and compared with the performance of conventional DE, ODE, and other state-of-the-art algorithms. The results for CODE are found to be promising.

APA, Harvard, Vancouver, ISO, and other styles

45

Radtke, Jonas, Guilherme Bertoldo, and Carlos Marchi. "DEPP - Differential Evolution Parallel Program." Journal of Open Source Software 5, no. 47 (March 20, 2020): 1701. http://dx.doi.org/10.21105/joss.01701.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Phuong, Phan Thi Thu, Hoang Van Lai, and Bui Dinh Tri. "Reservoir optimization with differential evolution." Vietnam Journal of Mechanics 38, no. 1 (March 15, 2016): 39–48. http://dx.doi.org/10.15625/0866-7136/38/1/6490.

Full text

Abstract:

Reservoir optimization, is one of recent problems, which has been researched by several methods such as Linear Programming (LP), Non-linear Programming (NLP), Genetic Algorithm (GA), and Dynamic Programming (DP). Differential Evolution (DE), a method in GA group, is recently applied in many fields, especially water management. This method is an improved variant of GA to converge and reach to the optimal solution faster than the traditional GA. It is also capable to apply for a wide range space, to a problem with complex, discontinuous, undifferential optimal function. Furthermore, this method does not requirethe gradient information of the space but easily find the global solution by asimple algorithm. In this paper, we introduce DE, compare to LP which was considered mathematically decades ago to prove DE's accuracy, then apply DE to Pleikrong, a reservoir in Vietnam, then discuss about the results.

APA, Harvard, Vancouver, ISO, and other styles

47

YAMAMOTO, Risako, Qingshuang YE, Hideyuki SUGIURA, Yi ZUO, and Eisuke KITA. "Improvement of Grammatical Differential Evolution." Proceedings of The Computational Mechanics Conference 2016.29 (2016): 007. http://dx.doi.org/10.1299/jsmecmd.2016.29.007.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

ABBASS, H. A., and R. SARKER. "THE PARETO DIFFERENTIAL EVOLUTION ALGORITHM." International Journal on Artificial Intelligence Tools 11, no. 04 (December 2002): 531–52. http://dx.doi.org/10.1142/s0218213002001039.

Full text

Abstract:

The use of evolutionary algorithms (EAs) to solve problems with multiple objectives (known as Vector Optimization Problems (VOPs)) has attracted much attention recently. Being population based approaches, EAs offer a means to find a group of pareto-optimal solutions in a single run. Differential Evolution (DE) is an EA that was developed to handle optimization problems over continuous domains. The objective of this paper is to introduce a novel Pareto Differential Evolution (PDE) algorithm to solve VOPs. The solutions provided by the proposed algorithm for five standard test problems, is competitive to nine known evolutionary multiobjective algorithms for solving VOPs.

APA, Harvard, Vancouver, ISO, and other styles

49

Iacca, Giovanni, Ferrante Neri, and Ernesto Mininno. "Noise analysis compact differential evolution." International Journal of Systems Science 43, no. 7 (July 2012): 1248–67. http://dx.doi.org/10.1080/00207721.2011.598964.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Wu, Guohua, Xin Shen, Haifeng Li, Huangke Chen, Anping Lin, and P. N. Suganthan. "Ensemble of differential evolution variants." Information Sciences 423 (January 2018): 172–86. http://dx.doi.org/10.1016/j.ins.2017.09.053.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!