A new guiding force strategy for differential evolution

Hira Zaheer (), Millie Pant (), Sushil Kumar (), Oleg Monakhov (), Emilia Monakhova and Kusum Deep
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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

International Journal of System Assurance Engineering and Management, 2017, vol. 8, issue 4, No 14, 2170-2183

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.

Keywords: Optimization; Differential evolution; Mutation; Mutation strategies; Amplifying factor (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s13198-014-0322-6

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