Differential Evolution

Differential Evolution (DE) is a popular population-based stochastic evoluation algorithm.

DE optimizes a problem by maintaining a population of candidate solutions and creating new candidate solutions by combining existing ones according to its simple formulae, and then keeping whichever candidate solution has the best score or fitness on the optimization problem at hand.

The code for the Parallel DE algorithm is modified from the Differential Evolution code from SciPy.

The technique implementation of Differential Evolution is available http://www.softcomputing.net/aciis.pdf

[1]	Lampinen, J. Differential evolution- New naturally parallel approach for engineering design optimization. Developments in computational mechanics with high performance computing 217-228, 1999.

[2]	Das, S., Abraham, A., Konar, A.. Particle swarm optimization and differential evolution algorithms: technical analysis, applications and hybridization perspectives, Advances of computational intelligence in industrial systems. Springer, pp. 1-38, 2008.