University:
Berkeley College
Course:
MGT2220 | Principles of Management
Academic year:

2023
Views:

159

Pages:

19
Author:

sengll8rsq

Differential Evolution: a stochastic nonlinear optimization algorithm by Storn and Price, 1996 The highlights of Differential Evolution (DE) A population of solution vectors are successively updated by addition, subtraction, and component swapping, until the population converges, hopefully to the optimum. No derivatives are used. Very few parameters to set. A simple and apparently very reliable method. DE: the algorithm Start with NP randomly chosen solution vectors. For each i in (1, …NP), form a ‘mutant vector’ vi = xr1+F.(xr2-xr3) Where r1, r2, and r3 are three mutually distinct randomly drawn indices from (1, …NP), and also distinct from i, and 0