The electricity consumption increases day by day, and we cannot imagine our lives without electricity. In today's scenario, the electricity should be available to all the consumers at minimum cost. So Economic Dispatch is a vital optimization problem in power system planning. The objective function of the economic load dispatch problem is highly non-linear. Consequently, standard optimization procedures may diverge or trap in local minima. This paper presents an overview of the economic dispatch problem, its formulation, and a comparison of addressing the issue with diversified soft computing techniques, i.e., differential evolution (D.E.), artificial bee colony algorithm (A.B.C.), particle swarm optimization (PSO), firefly algorithm (F.F.), Invasive weed optimization(I.W.O.), social group optimization(S.G.O.) and shuffled complex evolution(S.C.E.). All the methods are tested on 6-units, 7- units, and ten units test systems.