Abstract:

As an important procedure of the manufacturing process in refinery, oil blending could be abstracted as a complex non-linear optimization problem (NLP) with many constraints. It is difficult to obtain satisfying optimum solution by traditional methods. According to the oil blending and scheduling problem, a two particle swarms optimization algorithm with a mutation operator was presented. The proposed algorithm constructed two swarms of particles with different velocity restrictions, introduced a communication mechanism and a special mutation operator, and sequentially elevated the swarms to higher ability and velocity of global convergence. The new method was illustrated with a recipe optimization problem of gasoline blending, and the feasibility and effectiveness of the proposed algorithm was experimentally confirmed by the simulation results.

Key words:

微粒群算法, 油品调和, 配方优化

摘要：

针对复杂的非线性约束优化问题，提出了一种含变异算子的两群微粒群算法。算法构造了两个粒子群，分别设置了不同的搜索速度上限，并设计了粒子群间的协调机制和变异算子，使算法的寻优能力得到增强。针对油品调和配方优化进行了实例仿真，研究结果表明所提出的算法能获得较理想的调和配方，在满足调和利润最大的同时能保证对调和指标的卡边，使调和成品油的指标富余量大大降低。

关键词:

微粒群算法, 油品调和, 配方优化