基于模糊遗传的模糊模拟退火算法研究

定义了模糊集的质心概念，并据此来确定模糊集的大小。针对模糊寻优问题，提出了模糊模拟退火算法ＦＳＡ。基于所提出的模糊遗传算法ＦＧＡ，还提出了ＦＳＡ的有效改进算法ＦＧＳＡ。     

模拟退火算法与遗传算法的结合

模拟退火算法与遗传算法的结合王雪梅，王义和（哈尔滨工业大学计算机科学与工程系哈尔滨１５０００１）ＴＨＥＣＯＭＢＩＮＡＴＩＯＮＯＦＳＩＭＵＬＡＴＥＤＡＮＮＥＡＬＩＮＧＡＮＤＧＥＮＥＴＩＣＡＬＧＯＲＩＴＨＭＳ￥ＷＡＮＧＸｕｅｍｅｉ；ＷＡＮＧＹｉｈｅ（Ｄｅ...     

基于遗传微粒群混合算法的灰度图像增强

文中提出了一种基于遗传算法和微粒群算法的混合算法,该算法兼有遗传算法和微粒群算法的优点.混合算法以微粒群算法为主体,同时应用遗传算子操作来优化参数搜索,并引进了摒弃因子来调整微粒的随机性,最终得到最优值.本算法中交叉和变异算子采用了概率自适应策略,微粒群算法使用了动态惯性因子来控制微粒的速度更新.通过对标准试验函数的测试,与标准遗传算法及微粒群算法的结果比较,证明了该混合算法的有效性,并应用于图像增强处理,获得了较为满意的结果.     

基于改进型CLAFIC学习子空间算法的有限汉字集识别

采用改进型ＣＬＡＦＩＣ（Ｃｌａｓｓ－Ｆｅａｔｕｒｉｎｇ Ｉｎｆｏｒｍａｔｉｏｎ Ｃｏｍｐｒｅｓｓｉｏｎ）算法可以为学习子空间ＬＳＭ（Ｌｅａｒｎｉｎｇ Ｓｕｂｓｐａｃｅ Ｍｅｔｈｏｄ）算法提供更好的初始向量子空间,并通过ＬＳＭ算法对各类样本子空间按不同的旋转方式训练,来提高ＯＣＲ的识别率,该文的特点在于首先采用了学习子空间算法来实现字符在灰度图像上的识别,它克服了传统的基于二值化图像进行特征提取和识     

RBF神经网络参数估计的两种混合优化算法

基于全局搜索的进化算法和一种局部搜索算法———结构化的非线性参数优化方法（ＳＮＰＯＭ）,提出两种混合的优化算法来估计ＲＢＦ神经网络中的参数：１）初始化一定数目的种群作为ＳＮＰＯＭ 的初始值得到其适应值,通过选择、交叉和替换策略来更新种群;２）采用进化算法运行一定的代数,从最终群体中选取一些个体进一步用ＳＮＰＯＭ来优化．这两种混合优化算法的本质是用进化算法为ＳＮＰＯＭ 搜寻最优初始值,以得到全局最优解．仿真实验结果表明,该混合算法比单独使用进化算法或ＳＮＰＯＭ 更优,且优于其他一些算法．

     

智能交通中的高效多准最短路径混合算法*

针对智能交通系统（ITS）中求解多条准最短路径的问题,提出了一种混合算法。该算法以Floyd算法和A*算法为基础,主要运用遗传算法来求解多条准最短路径。实验的结果表明了该混合算法的可行性和比其他算法的高效性。     

一种变步长的非约束频域LMS自适应滤波算法

基于非约束频域自适应滤波器的结构，本文提出一种变步长自适应算法，即采用最小二乘法选取最优变步长收敛因子，计算机仿真结果表明，该算法比非约束频域ＬＭＳ算法（ＵＦＬＭＳ）具有更快的收敛速度和更好的收敛精度。     

基于差分和状态空间遗传混合算法的信号配时优化

为了最大限度地挖掘现有道路的承载能力,提出了一种基于差分进化算法和状态空间模型遗传算法的两阶段混合优化算法,建立以车辆平均等待时间最小为目标的数学模型进行优化。为了解决差分进化算法在后期收敛速度变慢,容易陷入局部最优的缺点,引入改进后的状态空间模型遗传算法形成一种混合算法。然后,用所提出的混合算法对5个经典测试函数进行寻优测试,并与定时控制、差分进化算法以及状态空间模型遗传算法进行对比,实验结果表明该混合算法不仅提高了收敛速度,并且在保证了算法收敛精度的前提下缩短了迭代次数。最后,以单交叉路口为例,验证该混合算法在求解信号灯配时问题时的优化效果。     

双Beta样条曲面造型系统的研究

该文介绍了双参数控制的双Ｂｅｔａ样条曲面的算法原理、边界条件处理、投影变换，以此为基础设计了自由曲面造型系统（ＦＳＭＳ），并用ＦＳＭＳ开发的实例做了验证分析。     

有源自适应声噪声对消技术研究：引入随机变μ系数的AANC算…

引入随机变μ系数对ＡＡＮＣ系统的ＦＬＭＳ算法分析表明，变μ系数ＦＬＭＳ算法有较快的收敛速度，跟踪能力并允许大的输入动态范围，这有利于提高宽带噪的消声能力和跟踪声通道的时变特性，文末给出了计算机模拟结果。     

Genetic algorithms for match-up rescheduling of the flexible manufacturing systems

Scheduling plays a vital role in ensuring the effectiveness of the production control of a flexible manufacturing system (FMS). The scheduling problem in FMS is considered to be dynamic in its nature as new orders may arrive every day. The new orders need to be integrated with the existing production schedule immediately without disturbing the performance and the stability of existing schedule. Most FMS scheduling methods reported in the literature address the static FMS scheduling problems. In this paper, rescheduling methods based on genetic algorithms are described to address arrivals of new orders. This study proposes genetic algorithms for match-up rescheduling with non-reshuffle and reshuffle strategies which accommodate new orders by manipulating the available idle times on machines and by resequencing operations, respectively. The basic idea of the match-up approach is to modify only a part of the initial schedule and to develop genetic algorithms (GAs) to generate a solution within the rescheduling horizon in such a way that both the stability and performance of the shop floor are kept. The proposed non-reshuffle and reshuffle strategies have been evaluated and the results have been compared with the total-rescheduling method.     

一种快速均值漂移图像分割算法

图像分割是图像分析及图像理解的关键步骤。与其他图像分割算法相比,均值漂移(Mean Shift)算法具有原理简单、无需先验知识、可以处理灰度图像及复杂的自然彩色图像等优点。但该算法需要对图像中每个像素点进行迭代计算,因此分割所需要的时间较长。本文提出了一种快速Mean Shift图像分割算法(Fast mean shift,FMS),将少量像素点作为初始点进行迭代计算,而出现在高维球区域内的其他像素点根据其到已有类中心的距离进行归类,从而减少Mean Shift算法的迭代次数,缩短分割时间。实验结果表明,本文提出的快速Mean Shift图像分割算法可以获得良好的分割结果且具有较高的分割效率。     

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.     

Hybrid symbiotic genetic optimisation for robust edge-based stereo correspondence.

This work proposes a novel algorithm for performing robust feature-based stereo-matching, without the ordering constraint. The calculation of the disparity map is decomposed to a set of disjoint intra-row subproblems, each one having two objectives: the search for a high confidence intra-row matching and the enforcement of figural continuity at the inter-row level. A separate genetic algorithm (GA) is allocated at each epipolar to search the feasible solution space. All GAs evolve parallely in a symbiotic fashion and continuously exchange currently available solution information to enable optimisation of figural continuity. To accelerate the search, we adapt a deterministic solver to seed the GAs and design problem-specific genetic operators for greater efficiency.     

Filtered-beam-search-based algorithm for dynamic rescheduling in FMS

In the practical production process of a flexible manufacturing system (FMS), unexpected disturbances such as rush orders arrival and machine breakdown may inevitably render the existing schedule infeasible. This makes dynamic rescheduling necessary to respond to the disturbances and to improve the efficiency of the disturbed FMS. Compared with the static scheduling, the dynamic rescheduling relies on more effective and robust search approaches for its critical requirement of real-time optimal response. In this paper, a filtered-beam-search (FBS) -based heuristic algorithm is proposed to solve the dynamic rescheduling problem in a large and complicated job shop FMS environment with realistic disturbances. To enhance its performance, the proposed algorithm makes improvement in the local/global evaluation functions and the generation procedure of branches. With respect to a due date-based objective (weighted quadratic tardiness), computational experiments are studied to evaluate the performance of the proposed algorithm in comparison with those of other popular methods. The results show that the proposed FBS-based algorithm performs very well for dynamic rescheduling in terms of computational efficiency and solution quality.     

遗传算法在网络在线智能组卷中的应用研究

速度和质量是网络在线实时组卷的两个核心要求,常用二进制编码遗传算法组卷时间受题库试题总量影响大,且后期收敛效率低;通过对上述问题的分析．提出了一种基于分段实数代号编玛和微量变异算子的GAs组卷算法．详细描述了新算法的设计思想和实现过程;结果验征了该算法运用于网培在线实时快速组卷的可行性,新算法的收敛时间（组卷时间）不受题库题量影响,可短至1.56sa。     

Combining genetic algorithms with BESO for topology optimization

This paper proposes a new algorithm for topology optimization by combining the features of genetic algorithms (GAs) and bi-directional evolutionary structural optimization (BESO). An efficient treatment of individuals and population for finite element models is presented which is different from traditional GAs application in structural design. GAs operators of crossover and mutation suitable for topology optimization problems are developed. The effects of various parameters used in the proposed GA on the optimization speed and performance are examined. Several 2D and 3D examples of compliance minimization problems are provided to demonstrate the efficiency of the proposed new approach and its capability of obtaining convergent solutions. Wherever possible, the numerical results of the proposed algorithm are compared with the solutions of other GA methods and the SIMP method.     

Harmony search algorithm for minimum cost design of steel frames with semi-rigid connections and column bases

Harmony search-based algorithm is developed to determine the minimum cost design of steel frames with semi-rigid connections and column bases under displacement, strength and size constraints. Harmony search (HS) is recently developed metaheuristic search algorithm which is based on the analogy between the performance process of natural music and searching for solutions of optimum design problems. The geometric non-linearity of the frame members, the semi-rigid behaviour of the beam-to-column connections and column bases are taken into account in the design algorithm. The results obtained by semi-rigid connection and column base modelling are also compared to one developed by rigid connection modelling. The efficiency of HS algorithm, in comparison with genetic algorithms (GAs), is verified with three benchmark examples. The results indicate that HS could obtain lighter frames and less cost values than those developed using GAs.     

Multi-objective topology optimization through GA-based hybridization of partial solutions

In a recent project the authors have developed an approach to assist the identification of the optimal topology of a technical system, capable of overcoming geometrical contradictions that arise from conflicting design requirements. The method is based on the hybridization of partial solutions obtained from mono-objective topology optimization tasks. In order to investigate efficiency, effectiveness and potentialities of the developed hybridization algorithm, a comparison among the proposed approach and traditional topology optimization techniques such as Genetic Algorithms (GAs) and gradient-based methods is presented here. The benchmark has been performed by applying the hybridization algorithm to several case studies of multi-objective optimization problems available in literature. The obtained results demonstrate that the proposed approach is definitely less expensive in terms of computational requirements, than the conventional application of GAs to topology optimization tasks, still keeping the same effectiveness in terms of searching the global optimum solution. Moreover, the comparison among the hybridized solutions and the solutions obtained through GAs and gradient-based optimization methods, shows that the proposed algorithm often leads to very different topologies having better performances.     

Optimal job sequence determination and operation machine allocation in flexible manufacturing systems: an approach using adaptive hierarchical ant colony algorithm

In this paper, an Adaptive Hierarchical Ant Colony Optimization (AHACO) has been proposed to resolve the traditional machine loading problem in Flexible Manufacturing Systems (FMS). Machine loading is one of the most important issues that is interlinked with the efficiency and utilization of FMS. The machine loading problem is formulated in order to minimize the system unbalance and maximize the throughput, considering the job sequencing, optional machines and technological constraints. The performance of proposed AHACO has been tested over a number of benchmark problems taken from the literature. Computational results indicate that the proposed algorithm is more effective and produces promising results as compared to the existing solution methodologies in the literature. The evaluation and comparison of system efficiency and system utilization justifies the supremacy of the algorithm. Further, results obtained from the proposed algorithm have been compared with well known random search algorithm viz. genetic algorithm, simulated annealing, artificial Immune system, simple ant colony optimization, tabu search etc. In addition, the algorithm has been tested over a randomly generated problem set of varying complexities; the results validate the robustness and scalability of the algorithm utilizing the concepts of ‘heuristic gap’ and ANOVA analysis.