DE-Acceleration Based Particle Swarm Optimizer for Short-Term Hydrothermal Scheduling
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The problem of short-term hydrothermal scheduling (STHS) is one of the most important issues in power systems optimizations. To solve this problem, a DE-acceleration based particle swarm optimizer (DPSO) is proposed in this paper. In the proposed optimizer, an adaptive mutation operation which acts on the position of a particle is introduced to enhance the swarm diversity. The DE algorithm is employed as an acceleration operation to speed up the algorithm's convergence rate. A novel inertia weight mechanism, in which the inertia weight factor is a function of the swarm diversity and the generation number, is designed for the DPSO also. Two hydrothermal test systems in literature are considered to compare the results obtained by the proposed approach with other population based algorithms. Two other PSO versions are also implemented to compare the detailed statistic properties of them with the proposed one. Numerical results show that the proposed DPSO can provide a more near-global solution than the other algorithms considered
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