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Variant of Differential Evolution Algorithm

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Advances in Computer and Computational Sciences

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 553))

Abstract

Differential evolution is a nature-inspired optimization technique. It has achieved best solutions on large area of test suits. DE algorithm is efficient in programming and it has broad applicability in engineering. This paper presents modified mutation vector generation strategy of basic DE for solving stagnation problem. A new variant of differential evolution that is DE_New has been proposed and the performance of DE_New is tested on Comparing Continuous Optimisers (COCO) framework composed of 24 benchmark functions and found DE_New has better exploration capability inside the given search space in comparison to GA, DE-PSO, DE-AUTO on Black-Box Optimization Benchmarking (BBOB) 2015 devised by COCO.

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References

  1. Z. W. Geem, J. H. Kim, and G. Loganathan, “A new heuristic optimization algorithm: harmony search,” Simulation, vol. 76, no. 2, pp. 60–68, 2001.

    Article  Google Scholar 

  2. S. Das and P. N. Suganthan, “Differential evolution: a survey of the stateof-the-art,” Evolutionary Computation, IEEE Transactions on, vol. 15, no. 1, pp. 4–31, 2011.

    Google Scholar 

  3. 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.

    Article  MathSciNet  MATH  Google Scholar 

  4. S. Tiwari, K. Mishra, and A. K. Misra, “Test case generation for modified code using a variant of particle swarm optimization (pso) algorithm,” in Information Technology: New Generations (ITNG), 2013 Tenth International Conference on. IEEE, 2013, pp. 363–368.

    Google Scholar 

  5. K. Mishra, S. Tiwari, and A. Misra, “Combining non revisiting genetic algorithm and neural network to generate test cases for white box testing,” in Practical Applications of Intelligent Systems. Springer, 2011, pp. 373–380.

    Google Scholar 

  6. E. Mezura-Montes, J. Vel azquez-Reyes, and C. A. Coello Coello, “A comparative study of differential evolution variants for global optimization,” in Proceedings of the 8th annual conference on Genetic and evolutionary computation. ACM, 2006, pp. 485–492.

    Google Scholar 

  7. D. Zaharie, “Critical values for the control parameters of differential evolution algorithms,” in Proceedings of MENDEL, vol. 2, 2002, p. 6267.

    Google Scholar 

  8. Y. Ao and H. Chi, “Experimental study on differential evolution strategies,” in Intelligent Systems, 2009. GCIS’09. WRI Global Congress on, vol. 2. IEEE, 2009, pp. 19–24.

    Google Scholar 

  9. M. Daoudi, S. Hamena, Z. Benmounah, and M. Batouche, “Parallel diffrential evolution clustering algorithm based on mapreduce,” in Soft Computing and Pattern Recognition (SoCPaR), 2014 6th International Conference of. IEEE, 2014, pp. 337–341.

    Google Scholar 

  10. M. F. Tasgetiren, O. Bulut, Q.-K. Pan, and P. N. Suganthan, “A differential evolution algorithm for the median cycle problem,” in Differential Evolution (SDE), 2011 IEEE Symposium on. IEEE, 2011, pp. 1–7.

    Google Scholar 

  11. F. Neri and V. Tirronen, “Recent advances in differential evolution: a survey and experimental analysis,” Artificial Intelligence Review, vol. 33, no. 1–2, pp. 61–106, 2010.

    Google Scholar 

  12. R. Joshi and A. C. Sanderson, “Minimal representation multisensor fusion using differential evolution,” Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, vol. 29, no. 1, pp. 63–76, 1999.

    Google Scholar 

  13. J. Holland, “Adaption in natural and artificial systems,” Ann Arbor, MI: University of Michigan Press, 1975.

    MATH  Google Scholar 

  14. D. E. Goldberg et al., Genetic algorithms in search optimization and machine learning. Addison-wesley Reading Menlo Park, 1989, vol. 412.

    Google Scholar 

  15. H.-P. Schwefel, “Evolution and optimization seeking,” John Wiley Sons, 1995.

    Google Scholar 

  16. X.-S. Yang, Nature-inspired metaheuristic algorithms. Luniver press, 2010.

    Google Scholar 

  17. A. R. Vahdat, N. NourAshrafoddin, and S. S. Ghidary, “Mobile robot global localization using differential evolution and particle swarm optimization,” in Evolutionary Computation, 2007. CEC 2007. IEEE Congress on. IEEE, 2007, pp. 1527–1534.

    Google Scholar 

  18. R. Storn and K. Price, Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces. ICSI Berkeley, 1995, vol. 3.

    Google Scholar 

  19. D. Kumar and K. K. Mishra, “Incorporating logic in artificial bee colony (abc) algorithm to solve first order logic problems: The logical abc,” in Knowledge and Smart Technology (KST), 2015 7th International Conference on. IEEE, 2015, pp. 65–70.

    Google Scholar 

  20. C. Gonz alez, D. Blanco, and L. Moreno, “Optimum robot manipulator path generation using differential evolution,” in Evolutionary Computation, 2009. CEC’09. IEEE Congress on. IEEE, 2009, pp. 3322–3329.

    Google Scholar 

  21. J. Gu and G. Gu, “Differential evolution with a local search operator,” in Informatics in Control, Automation and Robotics (CAR), 2010 2nd International Asia Conference on, vol. 2. IEEE, 2010, pp. 480–483.

    Google Scholar 

  22. J. M. Hereford, M. Siebold, and S. Nichols, “Using the particle swarm optimization algorithm for robotic search applications,” in Swarm Intelligence Symposium, 2007. SIS 2007. IEEE. IEEE, 2007, pp. 53–59.

    Google Scholar 

  23. V. Pano and P. R. Ouyang, “Comparative study of ga, pso, and de for tuning position domain pid controller,” in Robotics and Biomimetics (ROBIO), 2014 IEEE International Conference on. IEEE, 2014, pp. 1254–1259.

    Google Scholar 

  24. M. G. Epitropakis, D. K. Tasoulis, N. G. Pavlidis, V. P. Plagianakos, and M. N. Vrahatis, “Enhancing differential evolution utilizing proximitybased mutation operators,” Evolutionary Computation, IEEE Transactions on, vol. 15, no. 1, pp. 99–119, 2011.

    Article  Google Scholar 

  25. P. Melin, F. Olivas, O. Castillo, F. Valdez, J. Soria, and M. Valdez, “Optimal design of fuzzy classification systems using pso with dynamic parameter adaptation through fuzzy logic,” Expert Systems with Applications, vol. 40, no. 8, pp. 3196–3206, 2013.

    Article  Google Scholar 

  26. C. Sun, H. Zhou, and L. Chen, “Improved differential evolution algorithms,” in Computer Science and Automation Engineering (CSAE), 2012 IEEE International Conference on, vol. 3. IEEE, 2012, pp. 142–145.

    Google Scholar 

  27. A. Tripathi, N. Saxena, K. K. Mishra, and A. K. Misra, “An environmental adaption method with real parameter encoding for dynamic environment,” Journal of Intelligent Fuzzy Systems, no. Preprint, pp. 1–13.

    Google Scholar 

  28. Y.-W. Chen, C.-L. Lin, and A. Mimori, “Multimodal medical image registration using particle swarm optimization,” in Intelligent Systems Design and Applications, 2008. ISDA’08. Eighth International Conference on, vol.3. IEEE, 2008, pp. 127–131.

    Google Scholar 

  29. Brockho. (n.d.). Cec-bbob-2015 [COmparing Continuous Optimisers: COCO]. Retrieved June 11, 2016, from http://coco.gforge.inria.fr/

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Author information

Authors and Affiliations

  1. Computer Science and Engineering Department, Amity University, Lucknow, 226028, India

    Richa Shukla, Bramah Hazela & Shashwat Shukla

  2. Computer Science and Engineering Department, MNNIT Allahabad, Allahabad, 211004, India

    Ravi Prakash & Krishn K. Mishra

Authors
  1. Richa Shukla
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  2. Bramah Hazela
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  3. Shashwat Shukla
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  4. Ravi Prakash
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  5. Krishn K. Mishra
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Corresponding author

Correspondence to Krishn K. Mishra .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Missouri, Columbia, Missouri, USA

    Sanjiv K. Bhatia

  2. Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology, Allahabad, Uttar Pradesh, India

    Krishn K. Mishra

  3. Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, Uttar Pradesh, India

    Shailesh Tiwari

  4. Banaras Hindu University, Varanasi, Uttar Pradesh, India

    Vivek Kumar Singh

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Shukla, R., Hazela, B., Shukla, S., Prakash, R., Mishra, K.K. (2017). Variant of Differential Evolution Algorithm . In: Bhatia, S., Mishra, K., Tiwari, S., Singh, V. (eds) Advances in Computer and Computational Sciences. Advances in Intelligent Systems and Computing, vol 553. Springer, Singapore. https://doi.org/10.1007/978-981-10-3770-2_56

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  • DOI: https://doi.org/10.1007/978-981-10-3770-2_56

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3769-6

  • Online ISBN: 978-981-10-3770-2

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