Differential evolution methodology:
Differential evolution (DE) is a type of evolutionary algorithm developed by Rainer Storn and Kenneth Price [14–16] for optimization problems over a continuous domain. The prime idea of DE is to adapt the search during the evolutionary process. During the initial stage of evolution, the perturbations are large since parent individuals are far away from each other. As the evolutionary process matures, the population converges to a small region, and the perturbations adaptively become small. Hence, the DE performs a global exploratory search during the early stages of the evolutionary process and local exploitation during the mature stage of the search.
Hi, as for any evolutionary optimization method (Genetic Algorithms, Ant Colony Optimization, etc..) the algorithm used for the evolution of the current population is purely empirical. Depending on some parameters you can "tune" the convergence speed of the population to the final region leading to different solutions.
Basically it consists of taking three vectors randomly to generate a new vector as shown below
x4 = x3 + F * (x2-x1)
where F is called the differentiation operator. From experience I can say that this algorithm is very fast and recommended to deal with highly complex problems.
Differential evolution (DE) is an evolutionary algorithm for optimization. you can get some details about evolutionary algorithms from this paper.
https://link.springer.com/article/10.1007%2Fs11047-019-09731-z
This is a metaheuristic approach in which the search syrategy depends on the mutation of three soluctions randomly selected.
For more details see
Book Differential Evolution-A Practical Approach to Global Optimization
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