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This paper proposes an application of the differential evolution (DE) algorithm for solving the economic dispatch problem (ED). Furthermore, the regenerating population procedure added to the conventional DE in order to improve escaping the local minimum solution. To test performance of DE algorithm, three thermal generating units with valve-point loading effects is used for testing. Moreover, investigating the DE parameters is presented. The simulation results show that the DE algorithm, which had been adjusted parameters, is better convergent time than other optimization methods.problems and multimodal environments [14][15]. The advantages of the DE are simple structure, a few control parameters and high reliable convergences. The DE is one type of modern optimization techniques, which based on a population searching mechanism like as GA [9], bee colony (Be) optimization [16] and PSO [17][18].The paper is organized as follows: Section II formulates the ED problem. Section III describes detail of the DE algorithm. Section IV shows the test system and computational results. Lastly, conclusion is given in Section VI. n

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This paper proposes an improved approach based on conventional particle swarm optimization (PSO) for solving an economic dispatch(ED) problem with considering the generator constraints. The mutation operators of the differential evolution (DE) are used for improving diversity exploration of PSO, which called hybrid particle swarm optimization -differential evolution (PSO-DE). The mutation operators are activated if velocity values of PSO nearly to zero or violated from the boundaries. Four scenarios of mutation operators are implemented for PSO-DE. The simulation results of all scenarios of the PSO-DE outperform over the PSO and other existing approaches which appeared in literatures.

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