**Abstract:**
Economic Dispatch is the process of allocating the required load demand between the available generation units such that the cost of operation is minimized. There have been many algorithms proposed for economic dispatch out of which a Differential Evolution (DE) is discussed in this paper. Differential Evolution (DE) is very effective for solving optimization problems with non-smooth and non-convex characteristics. This technique combines simple arithmetic operator with classic evolutionary operators, such as mutation, crossover and selection. The key idea behind DE is a scheme for generating trial vectors. Mutation is used to generate a mutant vector by adding differential vectors obtained from the difference of several randomly chosen parameter vectors to the parent vector. After that, a trial vector is produced by a crossover through recombining the obtained mutant vector with the target vector. The DE is used to solve the Economic Dispatch problem (ED) with transmission loss by satisfying the linear equality and inequality constraints. The proposed method is compared with Lemda Iteration(LI),Genetic Algorithm (GA),Artificial Bee Colony(ABC), Particle Swarm Optimization (PSO) for a 3 Unit Test System and 6 UNIT Test System.

**Keywords:**
Differential Evolution, Genetic Algorithm, Artificial Bee Colony, Particle Swam Optimisation.