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Summary This paper presents a novel multi‐objective evolutionary algorithm, namely chemical reaction optimization (CRO) algorithm for solving dynamic economic emission dispatch (DEED) problem of power systems. The DEED problem is a non‐linear, non‐convex, multi‐dimensional, and highly constrained multi‐objective optimization problem. It has no unique optimal solution with respect to all criteria because it involves multiple and often conflicting optimization criteria. In order to improve the convergence speed and quality of the solutions attained by CRO, it is combined with differential evolution to escape from local minima solutions. This hybrid differential evolution‐based CRO (HCRO) methodology determines the feasible optimal solution of the non‐linear DEED problem. To demonstrate the superiority of the proposed CRO and HCRO methods in solving non‐convex, non‐linear, and constrained DEED problem, the proposed frameworks are implemented on 10‐unit and 30‐unit test systems. It is found from the simulation results that HCRO exhibits significantly better performance in terms of solution quality and convergence speed for all the cases compared with CRO algorithm. Furthermore, the proposed HCRO algorithm is superior to most of the existing algorithms available in the literature. Copyright © 2015 John Wiley & Sons, Ltd.

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