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Particle Swarm Optimization Algorithm (PSO) is often used to solve complex optimal scheduling. But in the process of particle swarm optimization, the homogenization of particle swarm is prone to premature homogenization re- sult. In order to solve this problem, this paper proposes the new mechanisms to assign the value to inertia factor adaptive- ly and dynamically with the evolution speed factor and mean fitness variance of population diversity factor to improve the traditional linear method. Then the improved particle swarm optimization algorithm is applied to the actual reservoir op- timal scheduling to verify that the algorithm has faster homogenization speed to get the global extreme and overcomes the shortcomings of easily fall into local optimum. This provides a new way for the reservoir optimal scheduling problem. proposed by Shi Y. is only associated with the iteration time of the algorithm and cannot truly reflect complexity of the PSO algorithm in operation. With decrease of the inertia factor, lack of global search capability of the PSO algorithm will be highlighted in the late operation period. Hong Guish- an adopts a strategy to identify wi=wmax-(wmax-wmin) t/tmax(t is the current iteration time and tmax is the maximum iteration time) of i th iteration within the weight range of the inertia factor, so the designed inertia factor will linearly reduce w value with iteration, but it does not solve the problem that too small w leads to loss of the new space expansion capabil- ity. The author adopts one adaptive and dynamic inertia fac- tor w mechanism, which is associated with the evolution speed of particle swarm and mean fitness variance of popula- tion diversity in order to discuss application of the PSO algo- rithm in the reservoir optimal scheduling.

Research on the Improved Particle Swarm Optimization Algorithm Applying in the Reservoir Optimal Scheduling