基于RCGA优化GRNN的实时动态目标追踪模型

针对三维环境中导弹追踪目标时制导和控制算法复杂而导致计算量非常大的问题,提出了一种基于隐性交叉遗传算法优化广义回归神经网络的实时动态目标追踪模型。通过将导弹防御区离散化为多个小模块生成输入数据,并针对每个可接受的目标参数数据集,使用RCGA估算导航常量和导弹注意时间;利用输入和输出的目标参数集生成GRNN所需的训练数据集;针对任意位置的目标轨道,将训练后的GRNN应用于实时导弹导引系统的实现中。通过战术目标仿真模型验证了所提算法的有效性及可靠性,仿真结果表明,相比其他几种目标追踪算法,算法取得了更好的实时性和更高的目标定位精度,脱靶率接近零。     

Differential evolution based 3-D guidance law for a realistic interceptor model

This paper presents a novel, soft computing based solution to a complex optimal control or dynamic optimization problem that requires the solution to be available in real-time. The complexities in this problem of optimal guidance of interceptors launched with high initial heading errors include the more involved physics of a three dimensional missile–target engagement, and those posed by the assumption of a realistic dynamic model such as time-varying missile speed, thrust, drag and mass, besides gravity, and upper bound on the lateral acceleration. The classic, pure proportional navigation law is augmented with a polynomial function of the heading error, and the values of the coefficients of the polynomial are determined using differential evolution (DE). The performance of the proposed DE enhanced guidance law is compared against the existing conventional laws in the literature, on the criteria of time and energy optimality, peak lateral acceleration demanded, terminal speed and robustness to unanticipated target maneuvers, to illustrate the superiority of the proposed law.     

防空导弹拦截目标的建模与仿真

利用simulink构造防空导弹抗击目标的模型.在比例导引的基础上,建立导弹和目标的运动轨迹方程,模拟两者运动轨迹对抗击过程进行仿真.模型结构简单,能够实现对航向角和航迹角的实时跟踪,同时可以利用简单的参数调整模拟不同类型目标.最后,对拦截过程多个相关因素的影响给予对比,仿真结果理想可靠.     

一种快速收敛的混合遗传算法

利用遗传算法早熟的特点 ,构造出一种快速收敛的混合算法来求解优化问题 ,并分析了它的收敛性。它是使用遗传算法来生成搜索方向 ,从而保证了算法的收敛性。该算法利用遗传算法的全局搜索能力 ,并采用 Nelder- Mead单纯形法来加强算法的局部搜索能力 ,加快了算法的收敛速率。模拟实验表明 ,该方法具有高效性和鲁棒性     

一种基于许瓦兹不等式的最优末制导律设计

针对导弹和目标相对运动学关系,利用许瓦兹不等式,提出了一种新的基于制导品质最优的末制导律。在导弹和目标的三维相对运动关系的基础上,忽略三维制导平面的耦合因素,同时考虑二阶的弹体响应环节,建立了导弹制导系统的数学模型。基于在有限时间内零化弹目相对距离和零化弹目视线角速率,以及导弹机动能量最小的优化制导品质的思想,利用许瓦兹不等式推导出一种用解析形式表示的最优末制导律。这种最优制导律通过估算剩余时间,实现对目标的有效拦截。最后通过数字仿真,验证了在脱靶量、弹目视线角速率和机动能量的制导品质方面,所提出的最优制导律要优于一般的比例制导律。     

Solving minimum distance problems with convex or concave bodies using combinatorial global optimization algorithms.

Determining the minimum distance between convex objects is a problem that has been solved using many different approaches. On the other hand, computing the minimum distance between combinations of convex and concave objects is known to be a more complicated problem. Most methods propose to partition the concave object into convex subobjects and then solve the convex problem between all possible subobject combinations. This can add a large computational expense to the solution of the minimum distance problem. In this paper, an optimization-based approach is used to solve the concave problem without the need for partitioning concave objects into convex pieces. Since the optimization problem is no longer unimodal (i.e., has more than one local minimum point), global optimization techniques are used. Simulated Annealing (SA) and Genetic Algorithms (GAs) are used to solve the concave minimum distance problem. In order to reduce the computational expense, it is proposed to replace the objects' geometry by a set of points on the surface of each body. This reduces the problem to an unconstrained combinatorial optimization problem, where the combination of points (one on the surface of each body) that minimizes the distance will be the solution. Additionally, if the surface points are set as the nodes of a surface mesh, it is possible to accelerate the convergence of the global optimization algorithm by using a hill-climbing local optimization algorithm. Some examples using these novel approaches are presented.     

Cooperative Multiple Pursuers against a Single Evader

This paper considers a pursuit-evasion game for non-holonomic systems where a group of pursuers attempts to capture an evader in a bounded connected domain. The problem is challenging because all vehicles have the same maneuvering capability in terms of speed and turn radius constraint. The paper initially discusses a simple approach for holonomic systems that is based on the minimization of the safe-reachable area (the area containing the set of points to where an evader can travel without being caught). This idea is then extended to develop a pursuit-evasion strategy for non-holonomic systems. However, solving such a problem is computationally intractable. Therefore, we propose a computationally efficient algorithm to obtain approximate solutions. This paper also proposes an alternative approach to obtain a simple yet effective solution to the cooperative pursuit problem that is based on missile guidance laws. As there is no analytical proof of capture, we empirically evaluate the performance of the algorithms and perform a comparative study using solutions obtained from umpteen simulations. A total of four different cooperative pursuit strategies and three different evader strategies are taken into account for the comparative study. In the process, an evader strategy which is superior to that based on the optimization of safe-reachable area is also identified.     

基于ADEMO/D-ENS的飞控系统性能指标分配方法

针对目前飞行控制系统设计中部件/组件性能参数的确定存在反复多次迭代的问题,对飞控系统性能指标的分配进行了研究。通过对性能指标分配过程进行建模,确定了分配过程属于多目标优化问题。基于Tchebycheff方法将多目标优化问题转化为单目标优化子问题集合,基于自适应差分进化算法得到的单目标优化子问题集合的最优解即为多目标优化问题Pareto最优解,同时采用惩罚因子保持差分进化算法种群的多样性。通过仿真与性能指标未分配的系统进行对比,结果表明分配后的系统具有更好的动态性和跟踪性,说明所提出的分配方法是正确的、可行的,并能够为工程应用提供一定的理论指导。     

Empirical investigation of the benefits of partial Lamarckianism

Genetic algorithms (GAs) are very efficient at exploring the entire search space; however, they are relatively poor at finding the precise local optimal solution in the region in which the algorithm converges. Hybrid GAs are the combination of improvement procedures, which are good at finding local optima, and GAs. There are two basic strategies for using hybrid GAs. In the first, Lamarckian learning, the genetic representation is updated to match the solution found by the improvement procedure. In the second, Baldwinian learning, improvement procedures are used to change the fitness landscape, but the solution that is found is not encoded back into the genetic string. This paper examines the issue of using partial Lamarckianism (i.e., the updating of the genetic representation for only a percentage of the individuals), as compared to pure Lamarckian and pure Baldwinian learning in hybrid GAs. Multiple instances of five bounded nonlinear problems, the location-allocation problem, and the cell formation problem were used as test problems in an empirical investigation. Neither a pure Lamarckian nor a pure Baldwinian search strategy was found to consistently lead to quicker convergence of the GA to the best known solution for the series of test problems. Based on a minimax criterion (i.e., minimizing the worst case performance across all test problem instances), the 20% and 40% partial Lamarckianism search strategies yielded the best mixture of solution quality and computational efficiency.     

拦截导弹自适应滑模制导律

针对平面拦截问题,提出了一种具有强鲁棒性的自适应滑模制导律。首先从一般意义上进行自适应控制律的推导,并给出了稳定性证明。该控制律可适用于系统存在有界外部干扰和结构摄动的情形。将拦截导弹的控制系统动态考虑到制导律的设计当中,进行了弹目相对运动关系的建模。该模型可适用于所推导的一般控制律结果,满足相应的非奇异条件。针对所推导的自适应滑模制导律,并进了数字仿真和分析。结果表明该制导律具有优良的弹道特性,可实现对连续高机动目标的有效拦截,同时具有较低的机动性能要求。     

基于多种群并行遗传算法的原料库存的优化

库存控制是现代企业，特别是连续生产企业物流管理的一项重要内容，针对有色冶金企业原料库存的实际情况，建立了一个以资金损耗最小为直接性能指标的原料库存优化模型，并提出了一种多种群并行遗传算法对该模型进行优化，仿真及实际运行结果表明：多种群并行遗传算法不仅能有效地克服传统遗传算法容易早熟收敛的缺点，而且改进了进化效率和加快了进化速度，从而得到令人满意的全局最优解。     

Synthesis of zero miss distance missile guidance via solution of an optimal tuning problem

In this paper, a new approach to guidance of homing missiles is considered. Instead of solving a dynamic optimization problem, which results in complex guidance laws that require the estimation of target maneuver and time-to-go, the guidance law is a priori chosen to be proportional navigation (PN). Then, an optimal tuning problem is solved. That is, the PN constant and the coefficients of the guidance transfer function are optimized to yield zero miss distance (ZMD) against any deterministic or random target maneuver subject to the constraint of limited missile maneuverability. It is shown that when the overall guidance transfer function is positive real, and the PN constant is some given function of the missile–target maneuver ratio, ZMD is obtained. A considerable part of this paper is devoted to a comprehensive treatment of the practical aspects of the theory. Implementation of the new guidance law is illustrated using real-life missile models, and its performance is compared to PN and optimal guidance (derived from dynamic optimization) using both deterministic and statistical tests. The results obtained are promising.     

基于RPSO的分布式电梯群控系统调度算法的优化

为了获取电梯群控系统调度问题的全局优化解,必须要找到一种具有全局优化功能的智能算法。对电梯群控系统不同的客流模型及控制机制进行了分析,提出了一种实时粒子群算法,用来优化电梯群控系统的动态调度问题。仿真结果表明,该算法不仅可以有效地调度分布式电梯群控系统,并且在电梯忙碌或者超载情况下,实现了对任务进行再分派的功能。基于RPS()算法的分布式电梯群控系统可以使乘客平均候梯时间减少一半,使乘客的电梯使用时间减少三分之一。     

Parameter optimization of improved fuzzy c-means clustering algorithm for brain MR image segmentation

A traditional approach to segmentation of magnetic resonance (MR) images is the fuzzy c-means (FCM) clustering algorithm. The efficacy of FCM algorithm considerably reduces in the case of noisy data. In order to improve the performance of FCM algorithm, researchers have introduced a neighborhood attraction, which is dependent on the relative location and features of neighboring pixels. However, determination of degree of attraction is a challenging task which can considerably affect the segmentation results.This paper presents a study investigating the potential of genetic algorithms (GAs) and particle swarm optimization (PSO) to determine the optimum value of degree of attraction. The GAs are best at reaching a near optimal solution but have trouble finding an exact solution, while PSO’s-group interactions enhances the search for an optimal solution. Therefore, significant improvements are expected using a hybrid method combining the strengths of PSO with GAs, simultaneously. In this context, a hybrid GAs/PSO (breeding swarms) method is employed for determination of optimum degree of attraction. The quantitative and qualitative comparisons performed on simulated and real brain MR images with different noise levels demonstrate unprecedented improvements in segmentation results compared to other FCM-based methods.     

Longitudinal control algorithm for automated vehicle merging

This paper considers longitudinal control of automated vehicle merging in a mathematical approach for automated highway systems. Merging manoeuvre is defined as one vehicle in the merging lane to be inserted in the middle between two vehicles in the main lane at fixed merging point which is the intersection of those two lanes. The main lane vehicles can change speed. To achieve this, the merging vehicle must properly adjust its speed and acceleration such that it reaches the merging point at the right time with the same speed and acceleration as the main lane vehicles. This problem is a little similar to but different from the well-known missile interception problem. The longitudinal control problem is proposed for different road layouts, based on which a unified mathematical model is established. Then a new concept, virtual platooning, is introduced, which effectively avoids a two-point boundary value problem . Based on this concept, an analytic solution with mathematical proof is provided. It is also discretized as a recursive algorithm for real-time use. A dynamic real-time simulation is published at PATH website. This algorithm has been successfully implemented with automated cars.     

面向空间目标交汇的多拦截器最优部署算法

对空间目标的交汇拦截是现代空天防御体系部署的关键环节, 而其核心优化控制问题为实现交汇次数的最大化. 本文针对一类多个拦截器与空间目标交汇的最优问题, 提出了同时优化拦截器初始部署位置和机动过程中加速度的最优控制策略. 首先, 建立了同时考虑了部署地域和动力学约束的交汇次数最大化的最优控制模型. 进一步, 给出了拦截器与目标可交汇的必要条件以及最优加速度输入设计, 进而使得将最优控制问题转为拦截器部署位置的优化问题. 最后, 严格给出了各个拦截器最优部署位置的具体计算过程.     

Multi-objective genetic local search algorithm using Kohonen’s neural map

Genetic Algorithms (GAs) are population based global search methods that can escape from local optima traps and find the global optima regions. However, near the optimum set their intensification process is often inaccurate. This is because the search strategy of GAs is completely probabilistic. With a random search near the optimum sets, there is a small probability to improve current solution. Another drawback of the GAs is genetic drift. The GAs search process is a black box process and no one knows that which region is being searched by the algorithm and it is possible that GAs search only a small region in the feasible space. On the other hand, GAs usually do not use the existing information about the optimality regions in past iterations.In this paper, a new method called SOM-Based Multi-Objective GA (SBMOGA) is proposed to improve the genetic diversity. In SBMOGA, a grid of neurons use the concept of learning rule of Self-Organizing Map (SOM) supporting by Variable Neighborhood Search (VNS) learn from genetic algorithm improving both local and global search. SOM is a neural network which is capable of learning and can improve the efficiency of data processing algorithms. The VNS algorithm is developed to enhance the local search efficiency in the Evolutionary Algorithms (EAs). The SOM uses a multi-objective learning rule based-on Pareto dominance to train its neurons. The neurons gradually move toward better fitness areas in some trajectories in feasible space. The knowledge of optimum front in past generations is saved in form of trajectories. The final state of the neurons determines a set of new solutions that can be regarded as the probability density distribution function of the high fitness areas in the multi-objective space. The new set of solutions potentially can improve the GAs overall efficiency. In the last section of this paper, the applicability of the proposed algorithm is examined in developing optimal policies for a real world multi-objective multi-reservoir system which is a non-linear, non-convex, multi-objective optimization problem.     

GUIDANCE LAW EVALUATION FOR MISSILE GUIDANCE SYSTEMS

In missile guidance system, to reduce the interception “miss distance,” it is important to choose a suitable guidance law and navigation constant. This paper investigates and compares the system behavior of guidance laws under different navigation constants. Based on use of the adjoint technique, miss distance sensitivity analyses which consider the system noise, target step maneuver, initial heading error and system parameters for different guidance laws and navigation constants are presented. Based on these analyses, some suggestions for choosing a suitable guidance law and navigation constant are given for the design of missile guidance systems. Also, a suggestion for the optimal escape time for pilots of fighter planes is given.     

MIJ2K Optimization using evolutionary multiobjective optimization algorithms

This paper deals with the multiobjective definition of video compression and its optimization. The optimization will be done using NSGA-II, a well-tested and highly accurate algorithm with a high convergence speed developed for solving multiobjective problems. Video compression is defined as a problem including two competing objectives. We try to find a set of optimal, so-called Pareto-optimal solutions, instead of a single optimal solution. The two competing objectives are quality and compression ratio maximization. The optimization will be achieved using a new patent pending codec, called MIJ2K, also outlined in this paper. Video will be compressed with the MIJ2K codec applied to some classical videos used for performance measurement, selected from the Xiph.org Foundation repository. The result of the optimization will be a set of near-optimal encoder parameters. We also present the convergence of NSGA-II with different encoder parameters and discuss the suitability of MOEAs as opposed to classical search-based techniques in this field.     

A modified distance method for multicriteria optimization, using genetic algorithms

The common application areas of Genetic Algorithms (GAs) have been to single criterion difficult optimization problems. The GA selection mechanism is often dependent upon a single valued scalar objective funtion. In this paper, we present results of a modified distance method. The distance method was proposed earlier by us, for solving multiple criteria problems with GAs. The Pareto set estimation method, which is fundamental to multicriteria analysis, is used to perform the multicriteria optimization using GAs. First, the Pareto set is found out from the population of the initial generation of the GA. The fitness of a new solution, is calculated by a distance measure with reference to the Pareto set of the previous runs. We calculate the distances of a solution from all the Pareto solutions found since the previous run, but the minimum of these distances is taken under consideration while evaluating the fitness of the solution. Thus the GA tries to maximize the distance of future Pareto solutions from present Pareto solutions in the positive Pareto space of the given problem. Here we modify distance method, by using an improved algorithm to assign and make use of the latent potential of the Pareto solutions which are found during the runs. Two detailed numerical examples and computer generated results are also presented.