Abstract：In order to master modern optimal methods, which are suitable for solving large pumping stationoptimal operation with multivariables. Basic principles of genetic algorithms(GA), particle swarm optimization(PSO) and simulated annealing particle swarm optimization(SA-PSO) were introduced, and the similarities and differences were analyzed. It is concluded that PSO is more simple and efficient than GA. Taking Jiangdu pumping station system in Eastern Route of South to North Water Transfer Projectas an example, under the circumstances of certain pump assembly head, selecting the number of running pump units and blade setting angles of water pumps as variables, optimal mathematical models for pumping station operation schemes were established aiming at the least operation cost, meeting the constraint conditions such as total pumping discharge, allowed discharge of single pump and the number of running pump units. GA, PSO and SA-PSO were applied to solve the models respectively to determinethe number of running pump units, operation duties of pump units and daily operation cost of each pumping station. Constraint conditions were used to deal with feasible rules, and calculating procedurewas programmed with Matlab. The results indicate that the operation costs of the optimum schemes by adjusting pump blade setting angles with SA-PSO are 0.99％-4.22％ less than that of the conventional schemes under design blade angles, and among the three optimum schemes, the operation cost of the optimum scheme based on SA-PSO is about 0.22％-2.80％, 0.02％-0.40％ less than that based on GA and PSO respectively. Computing times of the three optimizing algorithms are 30, 52 and 25 s respectively. Therefore, SA-PSO is more suitable for solving large pumping station operation optimization problems.
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