ARTIFICIAL IMMUNE ALGORITHM OF MULTICELLULAR GROUP AND ITS CONVERGENCE

Objective To find out more extrema simultaneously including global optimum and multiple local optima existed in multi-modal functions. Methods Germinal center is the generator and selector of high-affinity B cells, a multicellular group＇s artificial immune algorithm was proposed based on the germinal center reaction mechanism of natural immune systems. Main steps of the algorithm were given, including hyper-mutation, selection, memory, similarity suppression and recruitment of B cells and the convergence of it was proved. Results The algorithm has been tested to optimize various multi-modal functions, and the simulation results show that the artificial immune algorithm proposed here can find multiple extremum of these functions with lower computational cost. Conclusion The algorithm is valid and can converge on the satisfactory solution set D with probability 1 and approach to global solution and many local optimal solutions existed.     

Weekly Fleet Assignment Model and Algorithm

A 0-1 integer programming model for weekly fleet assignment was put forward based on linear network and weekly flight scheduling in China. In this model, the objective function is to maximize the total profit of fleet assignment, subject to the constraints of coverage, aircraft flow balance, fleet size, aircraft availability, aircraft usage, flight restriction, aircraft seat capacity, and stopover. Then the branch-and-bound algorithm based on special ordered set was applied to solve the model. At last, a real-world case study on an airline with 5 fleets, 48 aircrafts and 1786 flight legs indicated that the profit increase was $1591276 one week and the running time was no more than 4 min, which shows that the model and algorithm are fairly good for domestic airline.     

METADATA EXPANDED SEMANTICALLY BASED RESOURCE SEARCH IN EDUCATION GRID

With the rapid increase of educational resources, how to search for necessary educational resource quickly is one of most important issues. Educational resources have the characters of distribution and heterogeneity, which are the same as the characters of Grid resources. Therefore, the technology of Grid resources search was adopted to implement the educational resources search. Motivated by the insufficiency of currently resources search methods based on metadata, a method of extracting semantic relations between words constituting metadata is proposed. We mainly focus on acquiring synonymy, hyponymy, hypernymy and parataxis relations. In our schema, we extract texts related to metadata that will be expanded from text spatial through text extraction templates. Next, metadata will be obtained through metadata extraction templates. Finally, we compute semantic similarity to eliminate false relations and construct a semantic expansion knowledge base. The proposed method in this paper has been applied on the education grid.     

Nonlinear inverse modeling of sensor based on back-propagation fuzzy logical system

Objective To correct the nonlinear error of sensor output, a new approach to sensor inverse inodeling based on Back-Propagation Fuzzy Logical System （BP FS） is presented. Methods The BP FS is a computationaiiy efficient nonlinear universal approximator, which is capable of implementing complex nonlinear mapping from its input pattern space to the output with fast convergence speed. Results The neuro-fuzzy hybrid system, i.e. BP FS, is then applied to construct nonlinear inverse model of pressure sensor. The experimental results show that the proposed inverse modeling method automatically compensates the associated nonlinear error in pressure estimation, and thus the performance of pressure sensor is significantly improved. Conclusion The proposed method can be widely used in r nonlinearity correction of various kinds of sensors to compensate the effects of nonlinearity and temperature on sensor output.     

字典序多目标多阶段决策问题的嘉量解法

建立了字典序多目标多阶段决策模型，并用嘉量原理解决字典序多目标多阶段决策问题．在字典序多目标多阶段决策的赋嘉量有向图中，从任意出发点到终点的嘉量等于出发点经过中间状态的诸顶点到终点的嘉量摹和,这个摹和与出发点之前、终点之后的状态无关．在计算过程中，将合取作为摹和，常义加法作为摹乘，则字典序多目标多阶段决策问题的递推运算转换为多阶段的摹矩阵运算．     

回收物流设施多层选址模型及其算法

针对废弃物品的收集、预处理和再制造问题，建立了回收物流设施选址混合整数规划（MILP）模型，用分枝定界法求解，为提高分枝定界法的收敛速度、减少运算量，先用遗传算法为MILP找到较好的可行解，以其对应的目标函数值为分枝界定法上界，从而可以在分枝界定法的搜索中剪去大量分枝．用算例证明了算法的有效性和可行性．     

带回送和时间窗的车辆路径问题的模型及算法

在分析具有回送运输和时间窗的车辆路径问题特点的基础上，建立了该问题的优化数学模型，并通过设置与发货点距离为零的虚拟集货点使问题简化．在此基础上，构造了求解问题的改进遗传算法．在算法中，结合问题的特点设计了确保个体编码有效性的OX交叉算子，并采用基于Metropolis判别准则的复制算子，确保个体多样性和避免算法过早收敛．算例表明算法有效可行．     

自动事件检测算法的比较及评估

对现有的几种典型的高速公路事件自动检测算法的性能进行了评价,并给出各种算法的优缺点及比较评估标准,采用检测率(DR)和误报率(FAR)特征曲线及模拟退火算法进行了评价和优化,在比较、分析结果的基础上提出了智能检测算法的概念,并指出其今后的发展趋势。     

船舶航向非线性系统的模糊神经网络智能控制器设计

船舶航向控制系统具有典型的非线性和不确定性特性，并受自动舵执行能力的约束，这使得作为船舶智能化基础的航向控制极具挑战性。首先分析了船舶航向运动特性，给出带有舵约束的航向运动非线性数学模型；然后以模糊神经网络为控制器结构，在噪声加入和参考轨迹设置算法的支持下使用遗传算法对控制器参数进行自动搜索和优化，设计一种船舶航向智能控制器；最后对航向控制进行仿真。结果表明，所设计的航向智能控制器对船舶参数摄动和扰动具有良好的鲁棒性能。     

蚂蚁算法在交通网络设计问题中的应用

以路网的总走行时间最小为目标,考虑道路建设的投资约束,建立交通网络设计问题的数学模型,用蚂蚁算法对其进行求解,并用实例说明,这在交通网络设计问题中具有很好的应用价值.