新型粒子群优化算法在多序列联配中的应用

将粒子群优化算法应用于序列联配,提出了一种改进的粒子群优化算法,该算法在粒子群的进化过程中根据粒子的适应值动态地调整粒子群的惯性权重与粒子群飞行速度范围,提高了算法的收敛速度和收敛精度;针对PSO算法可能出现的早熟现象,引入重新初始化机制,增强了算法的搜索能力,实验表明该算法是有效的。     

肿瘤亚型识别研究中智能算法的应用

目的:为解决肿瘤亚型识别过程中易出现的维数灾难和过拟合问题,提出了一种改进的粒子群BP神经网络集成算法。方法:算法采用欧式距离和互信息来初步过滤冗余基因,之后用Relief算法进一步处理,得到候选特征基因集合。采用BP神经网络作为基分类器,将特征基因提取与分类器训练相结合,改进的粒子群对其权值和阈值进行全局搜索优化。结果:当隐含层神经元个数为5时,候选特征基因个数为110时,QPSO/BP算法全局优化和搜索,此时的分类准确率最高。结论:该算法不但提高了肿瘤分型识别的准确率,而且降低了学习的复杂度。     

“检验助理”教学模式在微生物学检验实训教学中的应用

为了解决湘潭医卫职业技术学院微生物学检验实训教学中存在的问题，湘潭医卫职业技术学院将“检验助理”教学模式应用于实训教学，该教学模式有效地解决了实训教学中的问题，提高了学生的实验积极性、优化了实验效果，且可以将此模式推广运用至医学类的其他实训教学课堂中。     

脉冲耦合神经网络的并行加速优化算法研究

并行编程技术可以有效提高算法的执行效率。文中分别利用CPU的单指令多数据流扩展指令集（Streaming SIMD Extensions,SSE）技术和多核并行编程技术,对脉冲耦合神经网络（Pulse Coupled Neural Network,PCNN）分割算法进行并行编程优化,以减少算法的运行时间。实验结果表明,SSE技术以及多核并行编程技术大大加快了PCNN分割算法的运行速度,有效提高了算法的执行效率,在一定程度上解决了该方法计算量大、耗时多的问题,具有应用于医学图像处理的潜在价值。     

基于视觉编码的图象处理研究──Ⅰ．原理、算法及实现

提出Ｇａｂｏｒ小波表达的概念及其在图象处理中实现的数学模型和算法框架,主要解决了运算中非正交性问题、收敛性问题,使得该算法及模型可以实现图象的分析和重建,并且可以应用于对视觉编码理论的解释。     

基于视觉编码的图象处理研究──Ⅰ．原理、算法及实现

提出Ｇａｂｏｒ小波表达的概念及其在图象处理中实现的数学模型和算法框架,主要解决了运算中非正交性问题、收敛性问题,使得该算法及模型可以实现图象的分解和重建,并且可以应用于对视觉编码理论的解释。     

基于Progressive多序列比对方法的求解多序列比对的启发式算法

在生物信息学研究中,生物序列比对问题占有重要的地位。多序列比对问题是一个NPC问题,由于时间和空间的限制不能够求出精确解。文中简要介绍了Feng和Doolittle提出的多序列比对算法的基本思想,并改进了该算法使之具有更好的比对精度。实验结果表明,新算法对解决一般的progressive多序列比对方法中遇到的局部最优问题有较好的效果。     

基于微粒群算法的辽宁省可持续农业产业结构优化配置研究

针对辽宁省农业产业结构中存在的问题,从经济、生态、社会三方面综合考察,建立了该区域的可持续农业产业结构优化模型,并利用改进的微粒群多目标优化算法对模型进行了求解,为辽宁省以及相似区域的农业产业结构调整提供了理论依据.     

一种新的基于蒙特卡罗模拟和光子传输模型的图像分割算法

本文以蒙特卡罗模拟方法为基础,结合组织光学的光子传输模型,提出了一种新的图像分割算法,该算法将复杂的图像分割问题简化为大量简单的光子传输随机实验,通过分析传输规律来获取目标区域.在随后的实验中,结合细胞核提取这一问题建立了一个简单的光学传输模型,并依据此模型分别对人造图和实际图进行了分割.人造图的分割结果表明了该算法的可行性,说明了该算法的一些优点;而实际图的分割结果则反映了该算法的不足之处,文章针对其中存在的问题和算法待改进之处进行了分析.     

基于局部茎搜索的RNA二级结构预测算法

RNA的二级结构预测是生物信息学中一个已经有30多年历史的经典问题,基于最小自由能模型(MFE)的优化算法是使用最为广泛的方法．但RNA结构中假结的存在使MFE问题理论上成为一个NP-hard问题,即使采用动态规划等优化算法也会面临时间复杂度高的困难,同时研究还发现,由于受RNA折叠动力学机制以及环境因素的影响,真实的RNA二级结构往往并不处于自由能最小状态．根据RNA折叠的特点,提出了一种启发式搜索算法来预测带假结的RNA二级结构．该算法以RNA的茎为基本单元,采用启发式搜索策略在茎的组合空间中搜索自由能最小并且出现频率最高的RNA二级结构,该算法不仅能显著降低搜索RNA二级结构的时间复杂度,还有助于弥补单纯依赖能量预测RNA二级结构的不足．在多种类型的RNA标准数据集上进行了检验,结果表明,该算法在预测的精度上优于目前国际上几个著名的RNA二级结构预测算法并且具有较高的运行效率．     

Evaluation of a particle swarm algorithm for biomechanical optimization

Optimization is frequently employed in biomechanics research to solve system identification problems, predict human movement, or estimate muscle or other internal forces that cannot be measured directly. Unfortunately, biomechanical optimization problems often possess multiple local minima, making it difficult to find the best solution. Furthermore, convergence in gradient-based algorithms can be affected by scaling to account for design variables with different length scales or units. In this study we evaluate a recently-developed version of the particle swarm optimization (PSO) algorithm to address these problems. The algorithm's global search capabilities were investigated using a suite of difficult analytical test problems, while its scale-independent nature was proven mathematically and verified using a biomechanical test problem. For comparison, all test problems were also solved with three off-the-shelf optimization algorithms--a global genetic algorithm (GA) and multistart gradient-based sequential quadratic programming (SQP) and quasi-Newton (BFGS) algorithms. For the analytical test problems, only the PSO algorithm was successful on the majority of the problems. When compared to previously published results for the same problems, PSO was more robust than a global simulated annealing algorithm but less robust than a different, more complex genetic algorithm. For the biomechanical test problem, only the PSO algorithm was insensitive to design variable scaling, with the GA algorithm being mildly sensitive and the SQP and BFGS algorithms being highly sensitive. The proposed PSO algorithm provides a new off-the-shelf global optimization option for difficult biomechanical problems, especially those utilizing design variables with different length scales or units.     

Estimation-based ant colony optimization and local search for the probabilistic traveling salesman problem

The use of ant colony optimization for solving stochastic optimization problems has received a significant amount of attention in recent years. In this paper, we present a study of enhanced ant colony optimization algorithms for tackling a stochastic optimization problem, the probabilistic traveling salesman problem. In particular, we propose an empirical estimation approach to evaluate the cost of the solutions constructed by the ants. Moreover, we use a recent estimation-based iterative improvement algorithm as a local search. Experimental results on a large number of problem instances show that the proposed ant colony optimization algorithms outperform the current best algorithm tailored to solve the given problem, which also happened to be an ant colony optimization algorithm. As a consequence, we have obtained a new state-of-the-art ant colony optimization algorithm for the probabilistic traveling salesman problem.     

Multi-layered greedy network-growing algorithm: extension of greedy network-growing algorithm to multi-layered networks

In this paper, we extend our greedy network-growing algorithm to multi-layered networks. With multi-layered networks, we can solve many complex problems that single-layered networks fail to solve. In addition, the network-growing algorithm is used in conjunction with teacher-directed learning that produces appropriate outputs without computing errors between targets and outputs. Thus, the present algorithm is a very efficient network-growing algorithm. The new algorithm was applied to three problems: the famous vertical-horizontal lines detection problem, a medical data problem and a road classification problem. In all these cases, experimental results confirmed that the method could solve problems that single-layered networks failed to. In addition, information maximization makes it possible to extract salient features in input patterns.     

Research on contaminant sources identification of uncertainty water demand using genetic algorithm

Urban water supply network is easily affected by intentional or occasional chemical and biological pollution, which threatens the health of consumers. In recent years, drinking water contamination happens occasionally, which seriously harms social stabilization and safety. Placing sensors in water supply pipes can monitor water quality in real time, which may prevent contamination accidents. However, how to reversely locate pollution sources through the detecting information from water quality sensors is a challengeable issue. Its difficulties lie in that limited sensors, massive pipe network nodes and dynamic water demand of users lead to the uncertainty, large-scale and dynamism of this optimization problem. This paper mainly studies the uncertainty problem in contaminant sources identification (CSI). The previous study of CSI supposes that hydraulic output (e.g., water demand) is known. Whereas, the inherent variability of urban water consumption brings an uncertain problem that water demand presents volatility. In this paper, the water demand of water supply network nodes simulated by Gaussian model is stochastic, and then being used to solve the problem of CSI, simulation–optimization method finds the minimum target of CSI and concentration which meet the simulation value and detected value of sensors. This paper proposes an improved genetic algorithm to solve the CSI problem under uncertainty water demand and comparative experiments are placed on two water distribution networks of different sizes.     

Set-based particle swarm optimization applied to the multidimensional knapsack problem

Particle swarm optimization algorithms have been successfully applied to discrete/valued optimization problems. However, in many cases the algorithms have been tailored specifically for the problem at hand. This paper proposes a generic set-based particle swarm optimization algorithm for use in discrete-valued optimization problems that can be formulated as set-based problems. A detailed sensitivity analysis of the parameters of the algorithm is conducted. The performance of the proposed algorithm is then compared against three other discrete particle swarm optimization algorithms from literature using the multidimensional knapsack problem and is shown to statistically outperform the existing algorithms.     

Bilinear state space systems for nonlinear dynamical modelling

Summary We discuss the identification of multiple input, multiple output, discrete-time bilinear state space systems. We consider two identification problems. In the first case, the input to the system is a measurable white noise sequence. We show that it is possible to identify the system by solving a nonlinear optimization problem. The number of parameters in this optimization problem can be reduced by exploiting the principle of separable least squares. A subspace-based algorithm can be used to generate initial estimates for this nonlinear identification procedure. In the second case, the input to the system is not measurable. This makes it a much more difficult identification problem than the case with known inputs. At present, we can only solve this problem for a certain class of single input, single output bilinear state space systems, namely bilinear systems in phase variable form.     

Modeling and multi-criteria optimization of an industrial process for continuous lactic acid production

The key feature of this paper is the optimization of an industrial process for continuous production of lactic acid. For this, a two-stage fermentor process integrated with cell recycling has been mathematically modeled and optimized for overall productivity, conversion, and yield simultaneously. Non-dominated sorting genetic algorithm (NSGA-II) was applied to solve the constrained multi-objective optimization problem as it is capable of finding multiple Pareto-optimal solutions in a single run, thereby avoiding the need to use a single-objective optimization several times. Compared with traditional methods, NSGA-II could find most of the solutions in the true Pareto-front and its simulation is also very direct and convenient. The effects of operating variables on the optimal solutions are discussed in detail. It was observed that we can make higher profit with an acceptable compromise in a two-stage system with greater efficiency.     

A Discrete Fruit Fly Optimization Algorithm for the Traveling Salesman Problem

The fruit fly optimization algorithm (FOA) is a newly developed bio-inspired algorithm. The continuous variant version of FOA has been proven to be a powerful evolutionary approach to determining the optima of a numerical function on a continuous definition domain. In this study, a discrete FOA (DFOA) is developed and applied to the traveling salesman problem (TSP), a common combinatorial problem. In the DFOA, the TSP tour is represented by an ordering of city indices, and the bio-inspired meta-heuristic search processes are executed with two elaborately designed main procedures: the smelling and tasting processes. In the smelling process, an effective crossover operator is used by the fruit fly group to search for the neighbors of the best-known swarm location. During the tasting process, an edge intersection elimination (EXE) operator is designed to improve the neighbors of the non-optimum food location in order to enhance the exploration performance of the DFOA. In addition, benchmark instances from the TSPLIB are classified in order to test the searching ability of the proposed algorithm. Furthermore, the effectiveness of the proposed DFOA is compared to that of other meta-heuristic algorithms. The results indicate that the proposed DFOA can be effectively used to solve TSPs, especially large-scale problems.     

Simulated parallel annealing within a neighborhood for optimization of biomechanical systems

Optimization problems for biomechanical systems have become extremely complex. Simulated annealing (SA) algorithms have performed well in a variety of test problems and biomechanical applications; however, despite advances in computer speed, convergence to optimal solutions for systems of even moderate complexity has remained prohibitive. The objective of this study was to develop a portable parallel version of a SA algorithm for solving optimization problems in biomechanics. The algorithm for simulated parallel annealing within a neighborhood (SPAN) was designed to minimize interprocessor communication time and closely retain the heuristics of the serial SA algorithm. The computational speed of the SPAN algorithm scaled linearly with the number of processors on different computer platforms for a simple quadratic test problem and for a more complex forward dynamic simulation of human pedaling.