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Differential Evolution with Alternating Strategies: A Novel Algorithm for Numeric Function Optimization

by Mohammad Shafiul Alam, Md. Tawseef Alam, Farniba Khan, A. A. Fattah Islam, Md. Rasel Kabir
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Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 4 - Number 2
Year of Publication: 2016
Authors: Mohammad Shafiul Alam, Md. Tawseef Alam, Farniba Khan, A. A. Fattah Islam, Md. Rasel Kabir
10.5120/cae2016652030

Mohammad Shafiul Alam, Md. Tawseef Alam, Farniba Khan, A. A. Fattah Islam, Md. Rasel Kabir . Differential Evolution with Alternating Strategies: A Novel Algorithm for Numeric Function Optimization. Communications on Applied Electronics. 4, 2 ( January 2016), 12-16. DOI=10.5120/cae2016652030

@article{ 10.5120/cae2016652030,
author = { Mohammad Shafiul Alam, Md. Tawseef Alam, Farniba Khan, A. A. Fattah Islam, Md. Rasel Kabir },
title = { Differential Evolution with Alternating Strategies: A Novel Algorithm for Numeric Function Optimization },
journal = { Communications on Applied Electronics },
issue_date = { January 2016 },
volume = { 4 },
number = { 2 },
month = { January },
year = { 2016 },
issn = { 2394-4714 },
pages = { 12-16 },
numpages = {9},
url = { https://www.caeaccess.org/archives/volume4/number2/500-2016652030/ },
doi = { 10.5120/cae2016652030 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-09-04T19:53:18.741951+05:30
%A Mohammad Shafiul Alam
%A Md. Tawseef Alam
%A Farniba Khan
%A A. A. Fattah Islam
%A Md. Rasel Kabir
%T Differential Evolution with Alternating Strategies: A Novel Algorithm for Numeric Function Optimization
%J Communications on Applied Electronics
%@ 2394-4714
%V 4
%N 2
%P 12-16
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The Differential Evolution (DE) is a prominent meta-heuristic algorithm that has been successfully employed to numerous complex and diverse problems from the fields of mathematics, science and engineering. DE belongs to the evolutionary family of algorithms which is based on the Darwinian theory of natural selection and evolution. DE maintains a population of candidate solutions and uses the vector differences between randomly picked candidate solution vectors to produce new, improved solutions to advance its evolutionary optimization process, generation by generation. This paper introduces a novel DE-variant — the DE with Alternating Strategies (DEAS) and evaluates its performance using a number of benchmark problems on numeric function optimization. DEAS effectively combines the exploitative and explorative characteristics of five different DEvariants by randomly alternating and executing these DEvariants in a single algorithm. The experimental results indicate that DE-AS can perform better than many other existing DE-variants on most of the benchmark functions, in terms of both final solution quality and convergence speed.

References
  1. D. Karaboga and B. Basturk, On the performance of artificial bee colony (ABC) algorithm, Applied Soft Computing 8 (1) (2008) 687–697.
  2. D. Karaboga, An idea based on honey bee swarm for numerical optimization, Erciyes University, Kayseri, Turkey, Technical Report-TR06, 2005.
  3. X. Yao, Y. Liu and G. Lin, “Evolutionary programming made faster”, IEEE Transactions on Evolutionary Computation 3 (2) (1999) 82–102.
  4. S. Sobti and P. Singla, Solving travelling salesman problem using bee colony based approach, Int. Journal of Engg. Research and Technology 2 (6) (2013) 186–189.
  5. K. Naidu, H. Mokhlis and A.H.A. Bakar, Multiobjective optimization using weighted sum Artificial Bee Colony algorithm for Load Freq. Control, Int. Jour. of Electrical Power and Energy Systems 55 (2) (2014) 657–667.
  6. R. Mukherjee, D. Goswami and S. Chakraborty, Parametric optimization of Nd:YAG laser beam machining process using artificial bee colony algorithm, Journal of Industrial Engineering, vol. 2013, Article ID 570250, 15 pages, 2013. DOI: 10.1155/2013/570250.
  7. H. Garg, Solving structural engineering design optimization problems using an artificial bee colony algorithm, Journal of Industrial and Management Optimization, 10 (3) (2014) 777–794.
  8. Z. Zhao, D. Yin and Y. Jiang, Improved bee colony algorithm based on knowledge strategy for digital filter design, International Journal of Computer Applications, 47 (2) (2013) 241–248.
  9. A. Mishra, A. Khanna, N. Singh and V. Mishra, Speed control of DC motor using bee colony optimization, Universal Journal of Electrical and Electronic Engineering 1 (3) (2013) 68–75.
  10. A. Karegowda and M. Darshan, Optimizing feed forward neural network connection weights using artificial bee colony algorithm, International Journal of Advanced Research in Computer Science and Software Engineering 3 (7) (2013) 452–454.
  11. A. Bolaji, A. Khader, M. Betar and M. Awadallah, Bee colony algorithm, its variants and applications: A survey, Journal of Theoretical and Applied Technology 47 (2) (2013) 434–459.
  12. T. Park and K. R. Ryu, A Dual population genetic algorithm for adaptive diversity control, IEEE Trans. Evolutionary Computation 14 (6) (2010) 865–884.
  13. R. K. Ursem, Diversity guided evolutionary algorithms, in Proc. 7th Int. Conf. Parallel Problem Solving from Nature (PPSN), 2002, pp. 462–474.
  14. J. Lampinen and I. Zelinka, On stagnation of the differential evolution algorithm, in Proc. 6th Int. Mendel Conf. Soft Computing, Brno, Czech Republic, 2000, pp. 76–83.
  15. T. Bäck and H.–P. Schwefel, “An overview of evolutionary algorithms for parameter optimization”, Evolutionary Computation 1 (1) (1993) 1–23.
  16. W. Lee and W. Cai, A novel artificial bee colony algorithm with diversity strategy, in Proc. 7th Int. Conf. Natural Computation, 2011, pp. 1441–1444.
Index Terms

Computer Science
Information Sciences

Keywords

Evolutionary algorithm differential evolution exploitation and exploration numeric function optimization.

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