面向多肽的分子对接算法性能对比研究

蛋白质-多肽间相互作用是计算生物学中的热门方向,对其结合的精确预测对于了解体内复杂的信号通路以及蛋白质功能,及进一步进行药物设计有着至关重要的作用。蛋白质-多肽对接方法能够有效地模拟蛋白质-多肽间相互作用,其对接性能直接决定模拟实验的准确性和成功与否。针对当前主要的蛋白质-多肽对接方法,综合测试了其算法性能;同时将本实验室开发的基于全信息粒子群算法的分子对接工具FIPSDock（Fully-Informed-Particle-Swarm-Dock）延伸到蛋白质-多肽对接领域,并评估其性能。利用标准蛋白质-多肽对接数据集（peptiDB）的21个测试例及13个定制测试例进行性能测试,主要对比分析了各算法的成功率和对接精度。结果表明FIPSDock可用于蛋白质-多肽对接,并相对于其他对接算法展示出优异的性能。     

Hybrid crossover operators for real-coded genetic algorithms: an experimental study

Most real-coded genetic algorithm research has focused on developing effective crossover operators, and as a result, many different types have been proposed. Some forms of crossover operators are more suitable to tackle certain problems than others, even at the different stages of the genetic process in the same problem. For this reason, techniques which combine multiple crossovers have been suggested as alternative schemes to the common practice of applying only one crossover model to all the elements in the population. Therefore, the study of the synergy produced by combining the different styles of the traversal of solution space associated with the different crossover operators is an important one. The aim is to investigate whether or not the combination of crossovers perform better than the best single crossover amongst them. In this paper we have undertaken an extensive study in which we have examined the synergetic effects among real-parameter crossover operators with different search biases. This has been done by means of hybrid real-parameter crossover operators, which generate two offspring for every pair of parents, each one with a different crossover operator. Experimental results show that synergy is possible among real-parameter crossover operators, and in addition, that it is responsible for improving performance with respect to the use of a single crossover operator.     

A comparison study of self-adaptation in evolution strategies and real-coded genetic algorithms

This paper discusses the self-adaptive mechanisms of evolution strategies (ES) and real-coded genetic algorithms (RCGA) for optimization in continuous search spaces. For multi-membered evolution strategies, a self-adaptive mechanism of mutation parameters has been proposed by Schwefel. It introduces parameters such as standard deviations of the normal distribution for mutation into the genetic code and lets them evolve by selection as well as the decision variables. In the RCGA, crossover or recombination is used mainly for search. It utilizes information on several individuals to generate novel search points, and therefore, it can generate offspring adaptively according to the distribution of parents without any adaptive parameters. The present paper discusses characteristics of these two self-adaptive mechanisms through numerical experiments. The self-adaptive characteristics such as translation, enlargement, focusing, and directing of the distribution of children generated by the ES and the RCGA are examined through experiments.     

Classification of adaptive memetic algorithms: a comparative study.

Adaptation of parameters and operators represents one of the recent most important and promising areas of research in evolutionary computations; it is a form of designing self-configuring algorithms that acclimatize to suit the problem in hand. Here, our interests are on a recent breed of hybrid evolutionary algorithms typically known as adaptive memetic algorithms (MAs). One unique feature of adaptive MAs is the choice of local search methods or memes and recent studies have shown that this choice significantly affects the performances of problem searches. In this paper, we present a classification of memes adaptation in adaptive MAs on the basis of the mechanism used and the level of historical knowledge on the memes employed. Then the asymptotic convergence properties of the adaptive MAs considered are analyzed according to the classification. Subsequently, empirical studies on representatives of adaptive MAs for different type-level meme adaptations using continuous benchmark problems indicate that global-level adaptive MAs exhibit better search performances. Finally we conclude with some promising research directions in the area.     

Advanced fitness landscape analysis and the performance of memetic algorithms

Memetic algorithms (MAs) have demonstrated very effective in combinatorial optimization. This paper offers explanations as to why this is so by investigating the performance of MAs in terms of efficiency and effectiveness. A special class of MAs is used to discuss efficiency and effectiveness for local search and evolutionary meta-search. It is shown that the efficiency of MAs can be increased drastically with the use of domain knowledge. However, effectiveness highly depends on the structure of the problem. As is well-known, identifying this structure is made easier with the notion of fitness landscapes: the local properties of the fitness landscape strongly influence the effectiveness of the local search while the global properties strongly influence the effectiveness of the evolutionary meta-search. This paper also introduces new techniques for analyzing the fitness landscapes of combinatorial problems; these techniques focus on the investigation of random walks in the fitness landscape starting at locally optimal solutions as well as on the escape from the basins of attractions of current local optima. It is shown for NK-landscapes and landscapes of the unconstrained binary quadratic programming problem (BQP) that a random walk to another local optimum can be used to explain the efficiency of recombination in comparison to mutation. Moreover, the paper shows that other aspects like the size of the basins of attractions of local optima are important for the efficiency of MAs and a local search escape analysis is proposed. These simple analysis techniques have several advantages over previously proposed statistical measures and provide valuable insight into the behaviour of MAs on different kinds of landscapes.     

Low bit depth representation motion estimation algorithms: a comparative study

A comparative study of low complexity motion estimation algorithms is presented. The algorithms included in the study are the 1-bit transform, the 2-bit transform, the constrained 1-bit transform and the multiplication free 1-bit transform which are using different motion estimation strategies compared to standard exhaustive search algorithm-mean absolute difference or similar combinations. These techniques provide better performance in terms of computational load compared to traditional algorithms. Although the accuracy of motion compensation is only slightly lower comparing to the other techniques, results in terms of objective quality (peak signal-to-noise ratio) and entropy are comparable. This fact, nominates them as suitable candidates for inclusion in embedded devices applications where lower complexity translates to lower power consumption and consequently improved device autonomy.     

On shortest-path algorithms in the topological design of computer networks: a comparative study

Built into several heuristics available for the topological design of computer networks, and inherent in the multicommodity nature of flow, is the determination of the shortest paths between pairs of nodes. Owing to the repeated requirement for shortest-path analyses during the course of optimization, the computational complexity of the heuristics depends upon the computational complexity of the shortest-path problem. This paper studies critically six shortest-path algorithms which are considered to be highly efficient and elegant, and presents a comparison of their computational complexity, simplicity, accessibility, applicability, capacity and speed.     

Rethinking the design of real-coded evolutionary algorithms: Making discrete choices in continuous search domains

Although real-coded evolutionary algorithms (EAs) have been applied to optimization problems for over thirty years, the convergence properties of these methods remain poorly understood. We discuss the use of discrete random variables to perform search in real-valued EAs. Although most real-valued EAs perform mutation with continuous random variables, we argue that EAs using discrete random variables for mutation can be much easier to analyze. In particular, we present and analyze two simple EAs that make discrete choices of mutation steps.     

Evolutionary algorithms,simulated annealing and tabu search: a comparative study

Evolutionary algorithms, simulated annealing (SA), and tabu search (TS) are general iterative algorithms for combinatorial optimization. The term evolutionary algorithm is used to refer to any probabilistic algorithm whose design is inspired by evolutionary mechanisms found in biological species. Most widely known algorithms of this category are genetic algorithms (GA). GA, SA, and TS have been found to be very effective and robust in solving numerous problems from a wide range of application domains. Furthermore, they are even suitable for ill-posed problems where some of the parameters are not known before hand. These properties are lacking in all traditional optimization techniques. In this paper we perform a comparative study among GA, SA, and TS. These algorithms have many similarities, but they also possess distinctive features, mainly in their strategies for searching the solution state space. The three heuristics are applied on the same optimization problem and compared with respect to (1) quality of the best solution identified by each heuristic, (2) progress of the search from initial solution(s) until stopping criteria are met, (3) the progress of the cost of the best solution as a function of time (iteration count), and (4) the number of solutions found at successive intervals of the cost function. The benchmark problem used is the floorplanning of very large scale integrated (VLSI) circuits. This is a hard multi-criteria optimization problem. Fuzzy logic is used to combine all objective criteria into a single fuzzy evaluation (cost) function, which is then used to rate competing solutions.     

A comparative study of staff removal algorithms

This paper presents a quantitative comparison of different algorithms for the removal of stafflines from music images. It contains a survey of previously proposed algorithms and suggests a new skeletonization based approach. We define three different error metrics, compare the algorithms with respect to these metrics and measure their robustness with respect to certain image defects. Our test images are computer-generated scores on which we apply various image deformations typically found in real-world data. In addition to modern western music notation our test set also includes historic music notation such as mensural notation and lute tablature. Our general approach and evaluation methodology is not specific to staff removal, but applicable to other segmentation problems as well.     

A comparative study of image processing algorithms

We compared the performances of the well-known image processing algorithms run on a set of reference images. For this, the algorithm-distorted images are compared against “ground truth” images.     

Practical fusion algorithms for rotation matrices: A comparative study

Many computer vision, sensor fusion, and robotic applications require the estimation of a 3 × 3 rotation matrix from a set of measured or computed 3 × 3 noisy rotation matrices. This article classifies solution methods into three categories: nonlinear least squares, linear optimal, and linear suboptimal algorithms. Their performance is compared through simulation studies. It is shown that the linear suboptimal algorithms proposed in this article have an accuracy comparable to that of the optimal algorithms and are about five times faster. Furthermore, a particular nonlinear optimization algorithm is presented that has computational complexity similar to that of the linear optimal procedures. © 1992 John Wiley & Sons, Inc.     

Antiviral therapy using a fuzzy controller optimized by modified evolutionary algorithms: a comparative study

Hepatitis is usually caused by a viral infection or metabolic diseases. Hepatitis type B virus (HBV) infection is among the most common causes of hepatitis and can result in serious liver diseases. Several dynamic models have been developed to mathematically describe the HBV infection and antiviral therapy. In addition, different control strategies have been reported in the literature to deal with optimal antiviral therapy problem of infectious diseases. In this paper, a set of optimized closed-loop fuzzy controllers are employed for optimal treatment of basic HBV infection. To optimize the proposed scheme, five modified and modern optimization algorithms are investigated. After designing the controller, some parameters of the HBV infection model are considered to be unknown, and the robustness of the optimized controller is studied. Experimental results show that the covariance matrix adaptation–evolution strategy-based optimized closed-loop fuzzy controller has the best performance in terms of total cost of an objective function defined based on maximization of uninfected target cells, minimization of free HBVs and minimization of drug usage. In addition, the execution time of this optimization algorithm is only 8 % more than the execution time of imperialist competition algorithm as the investigated algorithm with the best convergence speed.     

How eco-driving training course influences driver behavior and comprehensibility: a driving simulator study

Eco-driving behavior has been treated as one of the most cost-effective way in reducing vehicle fuel consumption and emissions. In this study, the effects of eco-driving training on different driving behaviors in start, stop, speed choice and no-idling from different levels of training, from receiving only static information (EDUCATION) to guided practicing (COACHING after EDUCATION), were thoroughly evaluated and compared. The drivers’ comprehensibility with eco-driving information was examined through eco-driving questionnaire surveys. The results indicated that EDUCATION alone was effective to improve driver’s comprehensibility with the basic concepts of eco-driving, to reduce the percentages of vehicle idling and to help drivers to avoid rapid starts slightly. EDUCATION alone did not significantly improve the driving behaviors of stop and speed choice, while COACHING after EDUCATION was found to enhance the effectiveness of these two elements of eco-driving. COACHING after EDUCATION did not introduce much additional benefit for more fuel-efficient starting pattern. In addition, eco-driving training led to more consistent driving behavior as evidenced by smaller standard deviation values on all measures related to fuel efficiency.     

A comparative performance analysis of deadlock avoidance control algorithms for FMS

A deadlock condition for flexible manufacturing systems is characterized by a set of parts, which have been processed but cannot be discharged by a set of machines or buffers. To avoid such problems, it is necessary to adopt suitable control policies which limit the resource allocation in the system, thus affecting the overall system performance. In the present work, we address the problem of evaluating and comparing the performance of deadlock avoidance control policies applied to FMS. The problem is discussed for both untimed and timed models, and for models both with and without deadlock avoidance control policies. Different control algorithms, among the most common in the literature, have been considered. Imperfect deadlock avoidance control policies are also considered. In addition, some indices are proposed to assess the structural properties of FMS with respect to deadlock occurrence and their performance. Two different application examples are analyzed, with the help of a commercial simulation package. Finally, an adaptive algorithm which can learn from system evolution to avoid deadlocks is illustrated.     

The design of PID controllers for a Gryphon robot using four evolutionary algorithms: a comparative study

This paper compares performances of PID controllers designed for a Gryphon robot joints using four hybrid evolutionary algorithms. We try to reduce the transient state of the step response. To this end, a function of some specifications of the step response (i.e. overshoot, settling time, rise time and the steady state error) is defined. We minimize this function by using four approaches, i.e. Particle swarm optimization (PSO) algorithm, the queen-bee (QB) algorithm, the genetic algorithm (GA) (all hybridized with the Nelder-Mead (NM) algorithm), and the shuffled complex evolution (SCE), and the results are compared. For designing a PID controller we should test several methods and based on the obtained results we can select one of them as the best method. In our case, the queen-bee for joints 1, 2, and 4, the genetic algorithm for joint 3, and the shuffled complex evolution method for joint 5 produce better results.     

A comparative study of the multi-objective optimization algorithms for coal-fired boilers

Combustion optimization has been proved to be an effective way to reduce the NOx emissions and unburned carbon in fly ash by carefully setting the operational parameters of boilers. However, there is a trade-off relationship between NOx emissions and the boiler economy, which could be expressed by Pareto solutions. The aim of this work is to achieve multi-objective optimization of the coal-fired boiler to obtain well distributed Pareto solutions. In this study, support vector regression (SVR) was employed to build NOx emissions and carbon burnout models. Thereafter, the improved Strength Pareto Evolutionary Algorithm (SPEA2), the new Multi-Objective Particle Swarm Optimizer (OMOPSO), the Archive-Based hYbrid Scatter Search method (AbYSS), and the cellular genetic algorithm for multi-objective optimization (MOCell) were used for this purpose. The results show that the hybrid algorithms by combining SVR can obtain well distributed Pareto solutions for multi-objective optimization of the boiler. Comparison of various algorithms shows MOCell overwhelms the others in terms of the quality of solutions and convergence rate.     

Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach

Evolutionary algorithms (EAs) are often well-suited for optimization problems involving several, often conflicting objectives. Since 1985, various evolutionary approaches to multiobjective optimization have been developed that are capable of searching for multiple solutions concurrently in a single run. However, the few comparative studies of different methods presented up to now remain mostly qualitative and are often restricted to a few approaches. In this paper, four multiobjective EAs are compared quantitatively where an extended 0/1 knapsack problem is taken as a basis. Furthermore, we introduce a new evolutionary approach to multicriteria optimization, the strength Pareto EA (SPEA), that combines several features of previous multiobjective EAs in a unique manner. It is characterized by (a) storing nondominated solutions externally in a second, continuously updated population, (b) evaluating an individual's fitness dependent on the number of external nondominated points that dominate it, (c) preserving population diversity using the Pareto dominance relationship, and (d) incorporating a clustering procedure in order to reduce the nondominated set without destroying its characteristics. The proof-of-principle results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware-software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Pareto-optimal front and distributing the generated solutions over the tradeoff surface. Moreover, SPEA clearly outperforms the other four multiobjective EAs on the 0/1 knapsack problem     

A comparative study on face detection and tracking algorithms

Face localization is the first stage in many vision based applications and in human-computer interaction. The problem is to define the face location of a person in a color image. The four boosted classifiers embbeded in OpenCV, based on Haar-like features, are compared in terms of speed and efficiency. Skin color distribution is estimated using a non parametric approach. To avoid drifting in color estimate, this model is not updated during the sequence but renewed whenever the face is detected again, that gives the ability to our system to cope with different lighting conditions in a more robust way. Skin color model is then used to localize the face represented by an ellipse: connected component segmentation and a statistical approach, namely the coupled Camshift of Bradsky, are compared in terms of efficiency and speed. The pursuit algorithms are tested on various video sequences, corresponding to various scenarios in terms of illumination, face pose, face size and background complexity (distractor effects).