基于改进离散粒子群算法的炼钢连铸最优浇次计划(英文)

Optimum steel making cast plan with unknown cast number based on the modified discrete particle swarm optimization

中文关键词  离散粒子群优化  序列倒置算子  炼钢连铸组浇计划  旅行商问题

英文关键词  discrete particle swarm optimization  inver over operator  steel making cast plan  traveling salesman problem

中文摘要

      提出了浇次数未知的最优浇次计划模型. 在分析该模型求解困难的基础上, 提出了用伪旅行商表示该模型的方法. 针对离散粒子群优化具有收敛速度、精度低, 但能充分利用各粒子的局部最优值和全局最优值信息的特点,而序列倒置算子具有收敛速度和精度较高, 但学习具有盲目性的特点, 结合二者优点, 提出了一种基于序列倒置的改进离散粒子群优化算法. 实验研究表明, 该算法与普通离散粒子群优化算法相比, 不论是收敛速度和还是求解精度都有了较大提高. 基于该改进算法求解最优浇次计划模型的研究表明: 所提伪旅行商问题模型非常适合用于组浇模型描述. 应用实际生产数据的计算表明该模型及其求解方法均非常有效.

英文摘要

      An optimum furnace cast plan model with unknown cast number is presented. Based on the analysis of the difficulties in solving the problem, a pseudo traveling salesman problem(TSP) model is presented to describe the plan and scheduling model. Based on that the discrete particle swarm optimization(DPSO) can make the best of the particles local and global optima, but it has the disadvantages of slow convergence and low search precision and the inver over operator is fast converged and high precise, but it is blindfold to learn from the other particles, a novel modified discrete particle swarm optimization algorithm based on the inver over operator(IDPSO) is presented. Experiments carried out on TSP show that IDPSO achieves good results comparing with the general DPSO. It can improve both the convergence speed and solution precision. IDPSO is used to solve the optimum cast plan problem. Simulations have been carried and the results show that the pseudo traveling salesman problem is very fit for describe the model. The computation with practical data shows that the model and the solving method are very effective.