摘要
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 of optimum locating of multistage stations in oil field is established. Because the model is very difficult to solve by the traditional methods, a synthetic solution is presented by an improved fish-swarm algorithm. In the solution, the real continuous variables are changed into discrete 0-1 variables so that the nonlinear MIP model is transferred into a pure 0-1 nonlinear IP model and it is possible to solve the model with high speed because the whole solving process falls into a binary calculation environment ; behaviors of a fish are dispatched by its body energy status; the Hamming distance is used to measure the distance between two fisbes; the following behavior is described by the greedy method with random moving - steps; the number of fishes, the size of visual scope, the crowded degree and the lowest survival body energy controlling technique are used to realize escaping policy from locally optimum positions; the maximum iterating times and the changing rate of the optimum solutions during iterating are used to control the terminating time. The application shows that the speed and stability of calculation is increased greatly and the optimum solution of the optimum model can be gained on microcomputers.
出处
《系统工程理论与实践》
EI
CSCD
北大核心
2008年第3期94-102,共9页
Systems Engineering-Theory & Practice
关键词
石油运输
大规模非线性混合整数规划
鱼群算法
群聚智能
动物行为
oil transportation
large-scale nonlinear mixed integer programming
fish-swarm algorithm
swarm intelligence
animal behavior
