Handbook of Research on Soft Computing and Nature-Inspired Algorithms by Shandilya Shishir K. Shandilya Smita Deep Kusum Nagar Atulya K

Author:Shandilya, Shishir K.,Shandilya, Smita,Deep, Kusum,Nagar, Atulya K.
Language: eng
Format: epub
Publisher: IGI Global

5. DIFFERENTIAL EVOLUTION

Differential evolution (DE) performs same as GA in initializing, representing the solution. It differs with GA in generation of solution.

5.1. Generation of New Value

5.1.1. Mutation

Each point (individual) can be represented as vector in search space. In DE each parent vector of current generation is known as target vector. All individuals in current generation get a chance to become a target vectors. The resultant vector obtain through mutation is known as donor vector. DE generates a donor vector by adding the weighted difference between two population vectors to a third vector. For each target vector Xi,G, i = 1, 2, . . . NP, a mutant vector is generated as follows

Vi,G+1 = Xr1,G + F * (Xr2,G - Xr3,G)

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