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A new guiding force strategy for differential evolution

Author

Listed:
  • Hira Zaheer

    () (Indian Institute of Technology Roorkee)

  • Millie Pant

    () (Indian Institute of Technology Roorkee)

  • Sushil Kumar

    () (LPU)

  • Oleg Monakhov

    () (Institute of Computational Mathematics and Mathematical Geophysics)

  • Emilia Monakhova

    (Institute of Computational Mathematics and Mathematical Geophysics)

  • Kusum Deep

    (Indian Institute of Technology Roorkee)

Abstract

Abstract Past few decades have witnessed the growth and development of different optimization techniques that can be applied for solving complex problems that are otherwise difficult to solve by traditional methods. Differential evolution (DE) has attained the reputation of a powerful optimization technique that can be used for solving a wide range of problems. In DE, mutation is the most important operator as it helps in generating a new solution vector. In this paper we propose an additional mutation strategy for DE. The suggested strategy is named DE/rand-to-best-best/2. It makes use of an additional parameter called guiding force parameter K, which takes a value between (0,1) besides using the scaling factor F, which has a fixed value. DE/rand-to-best-best/2 makes use of two difference vectors, where the difference is taken from the best solution vector. One vector difference will be produced with a randomly generated mutation factor K (0,1). Advantage of this strategy is, it will add a different vector to the old one and search space will increase with a random factor. Result shows that this strategy performs well in comparison to other mutation strategies of DE.

Suggested Citation

  • Hira Zaheer & Millie Pant & Sushil Kumar & Oleg Monakhov & Emilia Monakhova & Kusum Deep, 2017. "A new guiding force strategy for differential evolution," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(4), pages 2170-2183, December.
  • Handle: RePEc:spr:ijsaem:v:8:y:2017:i:4:d:10.1007_s13198-014-0322-6
    DOI: 10.1007/s13198-014-0322-6
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    References listed on IDEAS

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    1. Zhang, Jingqiao & Avasarala, Viswanath & Subbu, Raj, 2010. "Evolutionary optimization of transition probability matrices for credit decision-making," European Journal of Operational Research, Elsevier, vol. 200(2), pages 557-567, January.
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