Distribution Network Structure Planning Based on Multi-population Immune Genetic Algorithm

Distribution Network Structure planning is a complex combinatorial optimization problem, which is difficult to solve properly by using traditional optimization methods. The authors put forward Multiple Population Immune Genetic Algorithm (MPIGA)for optimal planning of distribution network structure, and do optimal search to different aspects of optimization goals. During the genetic evolution process, biologic immune mechanism is introduced to do some immune operator operation on chromosomes of each population, which can interact mutually by the shift of excellent units. By this way, it can effectively prevent population retrogression, promote diversity and the whole optimal searching ability of genetic algorithm. In order to minimize network annual expenditure, a mathematic model is established. The optimal solution is obtained by this algorithm, which has been illustrated effectively by specific examples at the same time.     

改进遗传算法在饲料配方设计中的应用

针对现有饲料配方软件的局限性,以白绒山羊饲料配方为例,提出一种基于改进遗传算法的求解方法。与标准遗传算法相比,采用随机联赛选择替代轮盘赌模型,优化了选择策略;采用不同的随机数交叉和高斯变异,改进了交叉算子与变异算子。测试结果表明,该方法具有良好的运算效率,拓宽了搜索空间,提高了对重点区域的搜索能力,降低了成本,为复杂问题的优化提供了一种新的思路。     

Distribution Network Structure Planning Based on Improved Immune Genetic Algorithm

Distribution Network Structure planning is a complex combinatorial optimization problem,which is difficult to solve properly by using traditional optimization methods.In order to solve this problem,Improved Immune Genetic Algorithm is introduced to the distribution network optimal planning. Improved Immune Genetic Algorithm draws into the immune diversity and antibody's density mechanism to maintain the individual's diversity and remains evolution algorithm's global stochastic searching ability,so it can promote diversity and the whole optimal-searching ability of genetic algorithm.The optimal module takes the minimum annual cost as its object,and the capacity and voltage drop of feeder and the radiation of distribution network as its restrictions.According to the require of radiation of distribution network,the spanning tree of the alternative network is taken as the initial solution to speed up the calculation.And the branch-exchange method is used in designing crossover operator and mutation operator to avoid the radiation checking and enhance the optimizing ability.This algorithm has been illustrated effectively by examples,at the same time,the calculation example demonstrates that,the algorithm has higher calculation speed than the traditional immune genetic algorithm.     

Chaotic clone optimization algorithm based on equivalent division strategy

The way of clonal selection algorithm randomly generated population that will easily lead to numbers of non uniform distribution of values in the solution space, thus increasing the data redundancy phenomenon.To overcome the shortcomings of clonal selection algorithm, a chaotic clonal optimization algorithm for function optimizing is proposed by combining clonal selection algorithm, chaos optimization. This algorithm uses chaotic characteristics randomness, ergodicity and regularity to avoid trapping around local optimal. Equivalent division strategy is introduced by reducing the possible data redundancy phenomenon. The simulation results show that the proposed algorithm can converge to the global optimum at quicker rate in a given range.     

Avoiding Obstacle Planning for Robots on Molecular Optimization Algorithm

Interest in DNA computing has increased overwhelmingly since Adleman successfully demonstrated its capability to solve Hamiltonian Path Problem. This article introduces the improving method in virtue of the biological thery of DNA technology, a new molecular algorithm is advanced. After a numerical simulation, the result shows that it avoids the prematurely and lower convergent speed of the classic genetic algorithm, and inherits global search capability, the validity and the speed of the genetic algorithm have been increased. The best result can be obtained in few iterative times. It is fit for solving path planning problem.     

Fast Algorithm About Unit Commitment Based on Revised BP Artificial Neural Network and Dynamic Search

In order to reduce the operation cost and optimize the unit commitment,the fast algorithm about unit commitment based on revised BP ANN(Artificial Neural Network) and dynamic search is discussed.The BP ANN is trained with Levenberg-Marquardt algorithm,which aiming at its drawback of the storage of some matrices that can be quite large for certain problems,and a revised algorithm is presented.The BP ANN is used to generate a pre-schedule according to the input load profile.Then the dynamic search is performed some stages where the commitment states of some of the units are not certain.The experimental results indicate that the proposed algorithm can reduce the execution time and memory space without degrading the quality of the generation schedule.     

A novel shuffled frog leaping algorithm for ICPT power programming

A new mode of inductively coupled power transfer (ICPT) is presented to city electrization traffic vehicle power supply optimization. The power supply distribution plan and ICPT technology are investigated. To avoid the local optimal of shuffled frog leaping algorithm (SFLA),a novel algorithm based on mutative scale chaos search and SFLA is presented. It is applied to inductively coupled power transfer substation optimal planning. The advantages of global and local search strategies for SFLA are combined with the proposed algorithm. In order to implement local refined search to improve local chaotic search ability and to enhance the solution accuracy, mutative scale chaos search is introduced to the proposed algorithm. The minimum annual expense of the proposed algorithm is 2.39% less than that of SFLA, which shows its advantage.     

Analysis of fast optimization algorithm for inverse detecting faulty insulators

In order to improve the computational efficiency of the insulator electric field inverse problem, the fast multi-pole method has been introduced to the traditional simulation charge method. Tikhonov regularization is used to process the ill-posed characteristic of inverse problem solution caused by the interference. Newton method is used to search the optimum solution, and then the actual field source parameters of insulators and the practical voltage distribution on the surface of the insulators can be obtained. The fast optimization algorithm has been verified in point charge model. And 110 kV insulator string is carried out to demonstrate that the fast optimization algorithm is much faster and more efficient than traditional method.     

用遗传算法进行RFD装置优化设计

免维修流体输送装置RFD由压缩空气做动力,通过气液换能驱动被输送液体间歇性出料。该装置不含运动部件,其优化设计是关系到装置高效稳定运行的关键理论。文中分析了传统枚举法和边际效用法在方案搜索中存在的问题,重点讨论了遗传算法在多目标高维优化问题中的应用,通过计算实例比较了3种方法的计算结果,得出遗传算法在本问题求解中的优势。最后,针对流量和扬程需求,采用遗传算法进行了装置的系列化设计,并分析了有关结构参数和运行参数的变化规律,进一步给出了不同密度和粘度下,RFD装置输送流量的换算关系,为RFD装置的工业设计打下坚实的基础。     

A new pattern recognition method of gas sensor array detection

BP neural network based gas sensor array detection pattern recognition has some disadvantages, such as slow convergence and local minimum problem. A modified immune neural network model which combines BP algorithm and immune algorithm is proposed to enhance global search capability and improve the performance of the neural network model. Orthogonal test is adopted to design the study samples of neural network. This ensures the accuracy of neural network while reducing the number of samples. The simulation results show that the proposed pattern recognition method solves the cross sensitivity of gas sensor effectively, overcomes the disadvantages of traditional BP neural network and improves the learning speed and detection accuracy.     

Organically Combinatorial Inference and Optimization in Intelligence System

The inference algorithm is the most important part in intelligence system because the level of intelligence in the system is decided by it. By means of the mutual benefit for inference algorithm of expert system ,fuzzy logic and neural network ,the combinatorial inference technology which is organically composed of these three parts is put forward for inference mechanism in intelligence system. The optimization decision model is also set up. In order to bring all the advantage of every inference algorithm into play and overcome the disadvantage of single inference algorithm the common expert knowledge base is applied to organic combination and concurrent operation of all inference algorithm. In order to realize knowledge share the inference results are optimized by decision technology optimization. The research results show that the organized combinatorial inference and optimization can be applied in engineering practice effectively and is benefit for raising the inference level.     

Optimization Method of Turbine Impeller''s Stress and Deformation Calculation

Taking into the feature of computer computing method, using Stress solution of elasticity theory and according to different method of solving of system of second order ordinary differential equations , this article provides two numerical value computing method as for the stress and deformation of the turbine impeller which is within the scope of elasticity . One combines the initial value computing method of system of ordinary differential equations (Runge-Kutta Method) with optimization method, another combines the boundary value computing method of system of ordinary differential equations difference method-with optimization method and three points interpolation method . The proposed method can eliminate the deficiencies of Secondary Calculation method and is particularly suited for programmable computer-based solution . The sample show that results gotten by two methods is nearly equal to the precise results,so they are practicable. They completes the quick and precise calculating of stress and deformation . They have some general meaning , large commonality and the project employing value.     

Evaluation of strategies to optimize training populations for genomic prediction in oat (Avena sativa)

Genomic selection is a promising breeding methodology that could increase selection accuracy and intensity and reduce generation interval. As the cost of genotyping decreases, it will be important to optimize training populations for costly phenotypic experiments for many complex traits. The aim of this research was to evaluate different optimization strategies, by using historical data from the Norwegian oat breeding programme at Graminor. In this paper, we focus on the optimization criteria: genetic diversity, phenotypic variance and genetic similarity between the training and testing populations. The four training population strategies—prediction core, diversity core, phenotypic selection and random selection—were applied to an oat candidate population of 1124 lines. An independent testing population was used to calculate the mean prediction abilities for the traits days to heading and plant height. Moreover, the strategies were tested in three independent wheat populations. The results showed that prediction core was the most promising strategy to select training populations with high genetic similarity to the testing set, high genetic diversity, and high phenotypic variance, which resulted in higher prediction ability across population sizes and traits.     

A New Fast Algorithm for the Discrete Cosine Transform

We prepose a new fast algorithm for computing discrete cosine transform and its inverse on two dimensional input sizes which are powers of 2.Because the integer operation is more faster than the float operation in computer,the integer operation is used in this algorithm.Through the matrix linear transform the number of operation for multiplications and additions is reduced,so that the speed is greatly raised.This algorithm is applied to our developmented JPEG image coding,and yields very good results.     

Cylindricity error evaluation using artificial bee colony algorithm with tabu strategy

For cylindricity error evaluation, the Least Squares Method (LSM) is not good enough because of the big error, while the bionics algorithms such as Genetic and Ant Colony Algorithm need to set many parameters and converge slowly, an Artificial Bee Colony Optimization Algorithm is proposed to evaluate the minimum zone cylinder (MIC). This Algorithm refers the tabu strategy for tabu search algorithm to use the Tabu table to save the local optimization results. It enhances the control effect of parameter limit and improves the global convergence ability. Experiment results indicate that this method can converge to the global optimization very quickly. The average runtime is nearly 1.2 s. It is applicable to the real time processing system such as Three Coordinate Measuring Machine.     

Hybrid Intelligent Method for Dynamic Reactive Power Optimization

In dynamic reactive power optimization problem(DRPO), action number constraints of discrete variables should be considered.By integrating immune genetic algorithm(IGA) and nonlinear interior point method(NIPM), a hybrid method for DRPO is proposed.First,the original DRPO problem is converted to a continuous optimization problem by relaxing the discrete variables,and the solution is obtained by NIPM.Then,according to the feature of control variables,the original DRPO problem is decomposed into a continuous optimization sub-problem and a discrete optimization sub-problem.The discrete optimization sub-problem is solved by IGA,and the continuous one is solved by NIPM.By solving the two sub-problems alternately,the optimal solution of the DRPO can be obtained.The proposed hybrid method combines advantages of IGA and NIPM,and finds the approximate optimal solution of DRPO.Numerical simulations on the IEEE 14 bus system illustrate that the proposed hybrid method is effective.     

Particle swarm optimization with oscillating parameter strategy

A novel oscillating parameter strategy (OPS) is proposed for the particle swarm optimization algorithm to improve its performance after a predefined number of generations.To efficiently control the local search and convergence to the global optimum solution, the OPS method alternates exploration and exploitation many times during the whole optimization course.For implementing the alternative of exploration and exploitation, the inertia weight and acceleration coefficients are oscillated during the search process.The oscillating inertia weight and acceleration coefficients can enhance the global search in the early part and not fall into premature status.This also encourages the particle to converge toward the global optima at the end of the search.Empirical simulations showed that the OPS method outperformed all the methods considered in this investigation for most of the functions.     

TRUST REGION METHOD FOR UNCONSTRAINED OPTIMIZATION

A new trust region algorithm is proposed for solving unconstrained optimization problems. According to the quadratic approximate model of the original optimization problem,the trust region algorithm uses directions,a convex combination of the quasi-newton direction and the steepest descent direction. This algorithm with new strategy is analyzed and the global and local quadratic convergent theorems are proved. At last,the implementation and computational results of the algorithm are demonstrated.     

A Study on the Mathematical Model of Postal Transportation Problem

The Postal Transportation Problem(PTP)is a very complex transportation problem. It has not any very effective solving method at present. This paper systemically analyze and define the problem according to its range, capability and time limit restrictions, random factors, dynamic factors, preferential factors, optimization objectives, cost composing, and so on. The multiobjective optimization mathematical modelof PTP is established and its relational problems are also analyzed. And the model is made up of 12 kinds of restraint conditions and three optimization objectives which are the whole transportation cost, average transportation time of mail and average use ratio of cars. This will be in favor of further study of PTP. The successful development of a logistics scheduling and dispatching optimization software based on the model and its corresponding algorithm proves the correctness of the model.[WT5HZ]     

改进的粒子群算法及在数值函数优化中应用

为提高粒子群算法的优化能力，提出了一种改进的粒子群优化算法。在该算法中，采用Beta分布初始化种群，采用逆不完全伽马函数更新惯性权重，在速度更新式中，引入了基于差分进化的新算子，对于粒子的越界处理，采用了基于边界对称映射的新方法。以50个不同类型的数值函数作为优化实例，基于威尔柯克斯符号秩检验的测试结果表明，该算法明显优于普通粒子群优化算法、差分进化算法、人工蜂群优化算法和量子行为粒子群算法。