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• A novel hybrid optimization approach based on meta-heuristic algorithms to solve the non-convex economic dispatch (ED) problem in large-scale power grids is proposed by an optimal and adaptive formulation. • This proposal optimizes the solution of the ED problem, reducing the generation costs and demonstrating a better performance during its convergence characteristics. • The adaptive hybrid optimization offers flexibility and soft computing for large-scale power system analysis. • The proposed approach demonstrates the superiority of the contribution to show the new advantages regarding other algorithms via numerical results in large-scale power grids. This paper proposes a robust adaptive hybrid optimization approach taking advance of two meta-heuristic algorithms to solve the non-convex economic dispatch (ED) problem in large-scale power grids. The demanded load is continuously increasing, leading to the operators to cope with new challenges associated with the reduction of the generation costs. As a consequence of this unmeasured increasing, algorithms with flexible and soft computing capabilities for analysing large-scale power systems are required. Thus, the key idea behind the proposed hybrid optimization framework is based on the abilities of the adaptive simulated annealing (ASA) and genetic operators for evaluating the economic dispatch problem, since these accelerate the convergence and reduce the total number of evaluations. This proposal optimizes the solution of the non-convex ED problem, decreasing the generation costs and emissions, and demonstrating a better performance during its convergence characteristics. Numerical tests are carried out in two power grids, where a reduced version of the Mexican interconnected power system is considered with a 24-h wind profile, aiming to demonstrate the superiority of this work, and show the new advantages regarding other algorithms. [ABSTRACT FROM AUTHOR]