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建立了石油运输系统多级站定位优化大规模非线性MIP模型.由于该模型用传统方法求解相当困难,应用改进的鱼群算法对该模型进行了求解.在解算过程中,对模型中的连续实型变量进行离散化处理,从而使整个优化模型变成纯0-1非线性IP模型,使求解过程在基于二进制环境高速运算成为可能;用人工鱼体能累计和消耗程度来调度其行为;用海明距离度量个体间的距离;采用随机步距移动的贪婪法描述个体追尾行为;采用鱼群规模、视野大小、拥挤程度和最低生存体能控制等方法实现局部最优解逃逸策略;采用最大迭代次数和迭代过程中最优解平均值变化程度来控制迭代终止时机.应用结果表明,该算法计算速度和稳定性有较大提高,可在微机上稳定地获取问题的最优解.
A large-scale nonlinear MIP model for multi-station positioning optimization of petroleum transportation system is established. Since this model is quite difficult to solve by the traditional method, the improved fish swarm algorithm is used to solve the model. In the solution process, Continuous real variables are discretized so that the whole optimization model becomes a pure 0-1 nonlinear IP model, making the solution process possible at high speed based on the binary environment. The artificial fish can be accumulated and consumed to schedule its behavior. The distance between individuals was measured by using Hamming distance; the greedy method with random step moves was used to describe the individual rear-end behavior; the local optimal solution was escalated using fish size, field size, crowding degree and minimum subsistence control; The number of iterations and the average value of the optimal solution during the iteration to control the timing of the iteration.The application results show that the speed and stability of the algorithm are greatly improved and the optimal solution of the problem can be obtained stably on the microcomputer.