This paper proposes a stochastic weight trade-off particle swarm optimization (SWT_PSO) for solving nonconvex economic dispatch. The proposed SWT_PSO preserves the balance between global exploration and local exploitation to improve the algorithm search capabilities. The balance is retained through trading off stochastic weights amongst previous velocity momentum, cognitive and social components together with using dynamic acceleration coefficients trade-off. The mechanisms for increasing diversity of swarm members are also incorporated to avoid premature convergence. In addition, the novel stochastic trade-off momentum control factor is exploited to enhance the capability of refining quality of a candidate solution during the late search process. The proposed SWT_PSO is tested on four economic dispatch test systems. Test results demonstrate that the proposed approach yields better solution quality than the best reported results in the literature for all test systems. (C) 2013 Elsevier Ltd. All rights reserved.