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We show how to construct a one-pass optimizing transformation by fusing a non-optimizing transformation with an optimization pass. We state the transformation in build form and the optimization pass in cata form, i.e., as a catamorphism; and we use cata/build fusion to combine them. We illustrate the method by fusing Plotkin's call-by-value and call-by-name CPS transformations with a reduction-free normalization function for the λ-calculus, thus obtaining two new one-pass CPS transformations. [ABSTRACT FROM AUTHOR]

In this paper, optimization methods are provided in order to solve the collision problems. Exactly collision time and reaction pulse could be solved using optimization methods which greatly improve the animation quality and computation time. And these methods also could simulate Newtonian physics and Coulomb friction. Moreover, unlike other papers, this one also tries to elaborate how to practically use these methods in some concrete examples. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a parallel hardware methodology employing the modified Gaussian elimination algorithm to efficiently solve linear system of equations (LSEs). Two parallel operators are issued in the hardware-optimized algorithm. Moreover, to be the proof-of-concept, the proposed parallel methodology is implemented to hardware structures in cases to address solving LSEs over GF(2) (primarily are bits operation) and LSEs with floating-point (IEEE-754 standard, 32-bit single precision) coefficient matrix. The corresponding hardware is mainly composed of uniformly distributed basic cells which store and register data, yielding a standalone worst case time complexity O(n2) opposed to O(n3) of the software replication. Finally, the given experimental result inosculated with the theory analysis. [ABSTRACT FROM AUTHOR]

In this paper, the optimal design of fuzzy image filtering is adopted for inspecting the perfection of pre-amplifier circuits, an important part of a hard disk drive. A high resolution X-ray camera is applied for capturing the image. Then the proposed automatic visual inspection which mainly consists of the image processing algorithms is applied to find the interesting features. The technique used in this paper enhances the ability to reduce noise by investigating the design of optimal fuzzy filtering. Particle Swarm Optimization (PSO) is adopted to find the optimal fuzzy filter. As our results indicate, our technique is superior to conventional methods such as the mean filter, the median filter, and the Adaptive Wiener filter. [ABSTRACT FROM AUTHOR]

In this paper, a new study on the applications of the optimization and computer techniques for optimal determination of partial ratios of four-step helical gearboxes for getting their minimal cross section dimension is presented. In the paper, basing on the moment equilibrium condition of a mechanic system including four gear units and their regular resistance condition, models for calculation of the partial ratios of the gearboxes are proposed. In particular, explicit models for prediction of the partial ratios are introduced by using regression analysis technique. These models allow determining the partial ratios accurately and simply. [ABSTRACT FROM AUTHOR]

In this paper we study some optimization problems resulting from image processing tasks in medical applications. These problems are solved using a coordinate-free approach which not only reduces the computational effort in finding the solutions and enhances conceptual clarity, but also leads to closed-form solutions. [ABSTRACT FROM AUTHOR]

The objective of this paper is to study how algorithms of optimization affect the parametersestimation of Autoregressive AR(1)Models. In our research we have represented the AR(1) models in linear state space form and applied the Kalman Filters to estimate the different unknown parameters of the model. Many methods have been proposed by researchers for the estimation of the parameters in the case of the linear state space models. In our work we have emphasized on the estimation through the Maximum Likelihood (ML). Statisticians have used many algorithms to optimise the likelihood function and they have proposed many filters; publishing their results in many papers. In spite of the fact that this field is so extended, we have emphasized our study in the financial field. Two quasi-Newton algorithms: Berndt, Hall, Hall, and Hausman (BHHH) and Broyden-Fletcher-Goldfarb-Shanno (BFGS), and the Expectation-Maximization (EM) algorithm have been chosen for this study. A practical study of these algorithms applied to the maximization of likelihood by means of the Kalman Filter have been done. The results are focused on efficiency in time of computation and precision of the unknown parameters estimation. A simulation study has been carried out, using as true values the parameters of this model published in the literature, in order to test the efficiency and precision of our implemented algorithms. [ABSTRACT FROM AUTHOR]

This paper reviews a framework for numerically analyzing dynamic interactions in imperfectly competitive industries. The framework dates back to Ericson and Pakes [1995. Review of Economic Studies 62, 53-82], but it is based on equilibrium notions that had been available for some time before, and it has been extended in many ways by different authors since. The framework requires as input a set of primitives which describe the institutional structure in the industry to be analyzed. The framework outputs profits and policies for every incumbent and potential entrant at each possible state of the industry. These policies can be used to simulate the distribution of sample paths for all firms from any initial industry structure. The sample paths generated by the model can be quite different depending on the primitives, and most of the extensions were designed to enable the framework to accommodate empirically relevant cases that required modification of the initial structure. The sample paths possess similar properties to those observed in (the recently available) panel data sets on industries. These sample paths can be used either for an analysis of the likely response to a policy or an environmental change, or as the model's implication in an estimation algorithm. We begin with a review of an elementary version of the framework and a report on what is known about its analytic properties. Much of the rest of the paper deals with computational issues. We start with an introduction to iterative techniques for computing equilibrium that are analogous to the techniques used to compute the solution to single agent dynamic programming problems. This includes discussions of the determinants of the computational burden of these techniques, and the mechanism implicitly used to select an equilibrium when multiple equilibria are possible. We then outline a number of techniques that might be used to reduce the computational burden of the iterative algorithm. This section includes discussions of both the implications of differences in modeling assumptions used in the alternative techniques, and a discussion of the likely relevance of the different techniques for different institutional structures. A separate section reports on a technique for computing multiple equilibria from the same set of primitives. The paper concludes with a review of applications of the framework and a brief discussion of areas where further development of the framework would seem warranted. [ABSTRACT FROM AUTHOR]

By minimizing the expected cost rate per unit time over a finite time span, an optimal preventive maintenance (PM) policy for a deteriorating system is proposed in this paper. It is assumed that the periodic PM is performed for the deteriorating system with a minimal repair at each failure. It has been shown that the imperfect preventive maintenance can reduce the wear and aging effects of deteriorating systems (or machines) to a certain level between the conditions of as good as new and of as bad as old [1-15]. The concept of the improvement factor is used to measure the extent of the restoration for a deteriorating system in this paper. The proposed improvement factor is considered as a variable depending upon the system's age (or the operating time) (t), the number of PM performed in the specified finite time span (N), and the cost ratio of each PM to the replacement (Cpm / Cpr). By applying Lie and Chun's model [7], the proposed improvement factor model includes three different functions to measure various age restoration situations. In this paper, it is considered that a deteriorating system undergoes N times of periodic PM with a minimal repair at each failure during the specified finite time span (T) and is replaced at T. The expression to compute the expected cost rate per unit time is derived and the optimal number N for the periodic PM is also obtained for the Weibull failure case. [ABSTRACT FROM AUTHOR]

This paper presents a wrapper method for feature selection that combines Lazy Learning, racing and subsampling techniques. Lazy Learning (LL) is a local learning technique that, once a query is received, extracts a prediction by locally interpolating the neighboring examples of the query which are considered relevant according to a distance measure. Local learning techniques are often criticized for their limitations in dealing with problems with high number of features and large samples. Similarly wrapper methods are considered prohibitive for large number of features, due to the high cost of the evaluation step. The paper aims to show that a wrapper feature selection method based on LL can take advantage of two effective strategies: racing and subsampling. While the idea of racing was already proposed by Maron and Moore, this paper goes a step further by (i) proposing a multiple testing technique for less conservative racing (ii) combining racing with sub-sampling techniques. [ABSTRACT FROM AUTHOR]

The design problem of PID regulator under poles placement and steady covariance constraints for discrete stochastic systems has been studied. Most of the existing papers are mainly focus on the frequency domain and obtain the parameters of PID regulator by some optimization arithmetic or some known experiences, these methods are difficult to investigate the covariance which is one key index of the stochastic system, also, they are lack of robustness when some new performance indices are introduced to improve the capability of the system, which is often occurring in the design and function process. In the paper, the problem has been transformed to partial state feedback problem by choosing proper state variables firstly, then the parameters of PID regulator to guarantee that the closed-loop can satisfy the circular regional pole and the steady state output covariance indices simultaneously can be found by solving a series of linear matrix inequalities (LMI), and thus can be solved by the LMI toolbox of MATLAB conveniently. The design example shows the efficiency of the method proposed in this paper. [ABSTRACT FROM AUTHOR]

In this paper an algorithm for minimization of a nondifferentiable function is presented. The algorithm uses the Moreau-Yoshida regularization of the objective function and its second order Dini upper directional derivative. It is proved that the algorithm is well defined, as well as the convergence of the sequence of points generated by the algorithm to an optimal point. An estimate of the rate of convergence is given, too. [ABSTRACT FROM AUTHOR]

The motivation for this paper is to develop an approach to optimization of beam design. Under given loading and support conditions, the comparative strength of three (3) common structural shapes was determined. This led to the conclusion that a particular structural shape together with its dimensions will give the optimal solution in beam design in terms of the least cross-sectional area to support the given load, which would then translate to savings in cost and reduction in weight of the structural member. An investigation was also conducted to take into consideration the effect in the dimensions of the structural shapes of uncertainties due to manufacturing limitations and tolerances. This resulted in an assessment of the order of magnitude of this effect on the design variables. In solving the resulting optimization problems, MATLAB's Genetic Algorithm and Direct Search Toolbox was employed. [ABSTRACT FROM AUTHOR]

Since the evaluation of each individual is based on the time-consuming structural analysis, the computational efficiency of truss topology optimization using genetic algorithm is very low. The paper focuses on this challenging problem. It is observed that there are a number of duplicate individuals appearing repetitively in the evolutionary process. Therefore, an individual identification technique is introduced to avoid evaluating the duplicate individuals by the time-consuming structural analysis but by searching the evolutionary history data to save computing time, the computational complexity of this technique is deduced. The results of two truss examples verify that the technique can effectively improve the efficiency of the algorithm. Based on this identification technique, numeric experiments are implemented to study the influence of several factors, i.e., the population size, the max generation, and the scale of problems, on the proportion of duplicate individuals. Results show that the population size has a significant impact on the proportion, and that both the max generation and the scale of problems have little influence. [ABSTRACT FROM AUTHOR]

Shape optimization is numerically a very demanding process, where several traditionally separate numerical processes meet together in a heterogeneous multidisciplinary environment. Typical elements involved are (1) the optimizer itself that implements the selected search strategy, (2) the parameterization procedure that provides the bidirectional mapping interface between the 3D geometric shape of the object and a limited number of parameters that control the parametric model of the geometry, (3) the simulation program that provides the results of the simulated interaction between the object and its environment (such as finite element analysis, FEA), and (4) control procedures that monitor the overall workflow and invoke mutual application calls (process flows and data flows) and handle data mining as well as acceptance or rejection of individual instances of candidate designs which are numerically generated and analyzed. In this paper, the set-up of this complex process, combined with evolutionary optimization, is developed with the objectives of numerical flexibility, computational efficiency in terms of combining sequential and parallel execution of individual applications, and low-cost implementation within an unmanaged computer cluster based on the Windows Communication Foundation, WCF. [ABSTRACT FROM AUTHOR]

In order to implement a wireless sensor network in process automation system it is needed to specify the sample number for the sensors. Because of the hardware and power supply limitations, wireless sensors are applied to discrete event control system. In wireless sensor network the highest sample number is restricted in comparison to the wired one. Moreover, the lowest sample number is also constrained by the limitations imposed by the control limits values. In this paper, relations between sample number and actuator's frequency drift in discrete domain is formulated and presented. The central and autonomous sensor network structures are introduced. In addition, ways to compromise the sample number with the actuator's frequency and control limits value are acknowledged. An approach to find the optimal sample number is proposed. It is shown when the sensor network becomes larger, autonomous network can partly compensate energy consumption increase by adding the sample number whereas central network does not support this feature. [ABSTRACT FROM AUTHOR]

Minimization of power consumption is a critical design goal for wireless-relay networks comprised of battery-powered sensor devices. In the paper, we consider a source-initiated (one-to-all) broadcast scenario in wirelessly-relay sensor networks (WSNs) with a high delivery probability requirement. We analyze both the transmission and the reception power to gain insights into the optimization of the overall power consumption of WSNs at network planning stage. Specifically, at the network planning stage, one wishes to maximize the network lifetime of the network through a proper selection of three key design parameters including the transmission range, the sleep period, and the broadcast probability. As a rule of thumb, the optimal number of nodes awaken to broadcast messages within a transmission range is constant with a given network size and path loss exponent for a target delivery probability requirement. Given the optimal settings above, we also present the trade-off between the delay requirement and power consumption. Our results can serve as general guidelines at the network planning stage. [ABSTRACT FROM AUTHOR]

This paper proposes a model of adding relations in multi-levels to an organization structure which is a complete binary tree such that the communication of information between every member in the organization becomes the most efficient. When edges between every pair of nodes with the same depth in L(L = 1; 2; ..., H ) levels are added to a complete binary tree of height H, an optimal set of depths {N1, N2 ..., NL} (H ≥ N1 > N2 > ... > NL ≥ 1) is obtained by maximizing the total shortening path length which is the sum of shortening lengths of shortest paths between every pair of all nodes in the complete binary tree. It is shown that {N1, N2, ..., NL}* = {H, H - 1, ..., H - L + 1}. [ABSTRACT FROM AUTHOR]

In this paper we present an application of robust optimization to inventory optimization under uncertainty. We represent uncertainty in a constraint based framework derived from basic economic principles. This approach offers the ability to use information theoretic concepts to quantify the amount of information used in the optimization. The results are shown to correspond to classical models such as EOQ in simple cases. Not only this, the presented model easily incorporates more realistic constraints, which are complicated and are not easily incorporated by the classical models. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a novel method to match buyers and suppliers in B2B e-marketplace based priority and multi-objective optimization. Analyzed matchmaking in a stable bilateral market, where each buyer or supplier is matched with trade partners. A mathematical model on many-to-many matching system as two objective optimization was developed to solve the multi-attribute matching problem. We attempted to apply priority based multi-objective genetic algorithm to seek optimal matching solutions, and illustrated the proposed matchmaking method. The results of simulation experiment demonstrated the proposed algorithm was flexible, it provided a set of solutions which including multiple optimal match solutions for decision-maker. [ABSTRACT FROM AUTHOR]

QoS is of paramount importance for Web services to function. However, it is a challenging problem to construct services that meet user's QoS expectation. This paper proposes an optimization method for service selection in constructing applications through service composition. In order to make quality rating accurate, ingredients of service reputation and expectation similarity are included in quality evaluation. Optimization of Web services selection is performed at both basic service level and composite service level, which results in optimized service applications to satisfy user's QoS expectation. A case study demonstrates feasibility of the method. [ABSTRACT FROM AUTHOR]

Constrained optimal control can be remarkably challenging to control. In this paper, optimization based approach for constrained optimal control of the integrating process is investigated through inventory control from using of the PI controller in the servo problem. The Lagrangian multiplier method is applied to find the PI parameters by solving the constrained optimal problem. The developed method explicitly deals with the important control constraints as well as minimizes the optimal performance measure. The method can be applied for any general integrating process. [ABSTRACT FROM AUTHOR]

This paper discusses a new adaptive ant colony optimization algorithm and its characteristics are as follows: (1) a novel cranky ant who behaves strangely is introduced to prevent from trapping at the local optima, (2) a new observation technique for searching status is adopted to judge whether it is trapping at local optima. Experimental results using benchmark data prove that the proposed algorithm with the cranky ants and the observation technique enables to control the trade-off between intensification and diversification, in comparison with conventional ACO. [ABSTRACT FROM AUTHOR]

Parameter estimation technique is an indispensable computational tool not only in aerospace research activities but also industrial activities such as control law design, handling qualities evaluation, model validation, and flight-vehicle design and certification. The estimation methods yield different levels of associated error with the estimated parameters. The primary reason for this is linked to the presence of measurement and process noise with the real flight data. Equation Error Method cannot handle either process noise or measurement noise. Output Error Method can handle measurement noise but not process noise. Filter Error Method, a special case of Output Error method, can be advantageously used to estimate parameter from flight data having both process and measurement noise. This paper presents issues related to the application of FEM with Gauss Newton (GN) and Levenberg-Marquardt (LM) optimization in estimating aerodynamic parameters from in-house generated flight data. [ABSTRACT FROM AUTHOR]

This paper focuses on multiobjective linear programming problems involving fuzzy random variable coefficients. A new decision making model and Pareto optimal solution concept are proposed using α-level cuts of membership function. It is shown that the problem including both randomness and fuzziness is equivalently transformed into a deterministic problem. An interactive algorithm is proposed in order to obtain a satisficing solution for a decision maker through interaction. [ABSTRACT FROM AUTHOR]

In this paper authors indicate, by means of a concrete example, that it is possible to apply the method of multiple criteria integer linear programming method in dealing with the problem of determining an optimal plan for fabric cutting optimization. The procedure involves the following stages: 1) Selection of the criteria for optimization of fabric cutting, 2) Setting the problem of determining an optimal plan of fabric cutting, 3) Setting the model of multiple criteria, linear integer programming in finding solution to a given problem (4) The solutions are obtained applying the method of satisfactory goals. [ABSTRACT FROM AUTHOR]

"Efficiency" has been the key word for Intel last 2006 with focus of improving Total Utilization (TU) of tools in ATM. Total Utilization (TU) gives a clearer understanding of how much our capital is truly being used. In order for us to realize a higher total utilization, we need to ensure that our tools are productive majority of the time. In Chipsets assembly, Wire Bond is considered the constraint tool of the production floor. Cavite Chipsets assembly has the largest fleet of wire bonders across virtual factories and contributes to 20% of the total Intel Chipsets assembly output. Furthermore, internal assembly cost is cheaper compared to external assembly. Thus, it is critical that this station is maximized and ensure that it is meeting POR (Plan of Record) goals. However, in WW 01'06 to WW 26'06, Wire Bond Total Utilization (TU) Performance is struggling to meet POR (Plan of Records) utilization of 81% and only averaging 68%. There is a need to get Wire Bond tool performance on track to meet the Goal Utilization (GU) as soon as possible to prevent additional cost to Intel operations. In this paper, the author will discuss the method used in identifying the root cause of the problem. The author will also demonstrate how an assignment model can be an alternative solution in optimizing Wire Bond Total Utilization leading to an improved idle time from 35 hours to 9 hours per week. [ABSTRACT FROM AUTHOR]

As the needs for fast, efficient and high quality production increases, it is important for every company or manufacturing facility to be able to optimize the resources needed. This paper focuses and resolves the problem of best tool selection for a rough milling operation on 3-axis machines, especially for the production of sculptured surfaces. The tool selection methodology is based on a genetic algorithm optimization procedure. The objective of the algorithm is to achieve quicker machining parameters. Direct comparisons between CAM software simulations and real time experiments have been performed to verify the results. Tool type comparison between flat and ball end cutters is also conducted. [ABSTRACT FROM AUTHOR]

In this paper, we propose the model of Scalable Joint Source/Channel Coding for wireless data transmission where channel is inherently time varying and subject to Rayleigh fading nature. We consider the problem of still image data transmission over time varying channels with two Channel State Information's (CSI) and three bit rates (1, ½ and ¼) for efficient transmission of data. Here, we analyze the effect of CSI availability on the optimal performance analysis of proposed scheme. Our source model is based on two level wavelet image decomposition using Haar mother wavelet with Set Partition In Hierarchical Tree (SPIHT) for significant coefficients for dynamic thresholding and channel coding will be Rate-Compatible Punctured Convolutional (RCPC) Codes. We simulated the new scheme of SJSCC and tried for optimal performance for wireless channels. Further, we show how our optimized SJSCC scheme out performs over Optimized Joint Source/Channel Coder (OJSCC) for channel states as well for various bit rates [2] [3]. In our optimization of SJSCC Scheme, marginal improvement in PSNR of 0.14dB is obtained for both average and worst channels and significant improvements of PSNR values in the order of 2dB is also obtained for all three bit rates over existing OJSCC system. Finally, our proposed SJSCC system performance is compared with RCPC and RCPT for wide range of SNRs and noticed marginal is noticed with RCPC channel coder or RCPT. [ABSTRACT FROM AUTHOR]

With the development of digital cameras, the digital photographs were flooded in our life. How to classify images efficiently in huge image database becomes an important research topic. In recent years, the related researches of the image classification are based on semantics. The scene image classification has received much attention especially because it contains plenty semantics. It is a difficult challenge to classify the scene images accurately. This paper tries to use particle swarm optimization (PSO) algorithm that has biological characteristic, and to train with the scene images of semantics. We can get a scene transformation matrix during the process. The scene transformation matrix can be used to classify scene images, which are close to human's semantics. The experiment shows our proposed method has great correct classification rate. [ABSTRACT FROM AUTHOR]

In this paper a new method for optimization of Elastic Bunch Graph Matching (EBGM) algorithm in frontal face recognition is presented. In EBGM algorithm, some pre-determined wavelength of Gabor wavelet is used to extract features from face image. For optimization of EBGM algorithm, Genetic Algorithm (GA) is used to select the best wavelengths of Gabor wavelet. For evaluation, algorithm has been tested on 300 classes of FERET face database. In training phase, only one image per class is trained. The recognition rate of optimized EBGM is about 91%. Also the optimized EBGM can run 1.5 times faster than original EBGM. [ABSTRACT FROM AUTHOR]

In recent years, the chip leakage power may be larger than the chip dynamic power because the semiconductor process technology progresses quickly. Therefore, leakage power reduction becomes an important issue for low power circuit designers. In this paper, we propose a heuristic cell replacement algorithm to reduce the leakage power of a logic design. The algorithm contains two procedures. The first one uses a conventional optimization approach and the second one uses a new optimization approach. In the first procedure, the algorithm uses high Vt, normal Vt, and low Vt cells to do cell replacement. In the second procedure, the algorithm employs hybrid threshold voltage standard cell libraries (HTVSCLs) to do cell replacement. The experimental results show that our technique can further reduce the leakage power up to 14.186% by using the second procedure after invoking the first procedure. [ABSTRACT FROM AUTHOR]

The adaptive immune algorithm (AIA) developed from immune algorithm (IA), owns faster computation speed and better convergence than that of GA and other stochastic type algorithms, due to its characteristic of having two layers optimization. The adaptive immune algorithm automatically adjusts the parameters to achieve fast convergence without falling in the local minimum point, according to the value of the distance between the antibodies. It leads to great reduction of the computation time, compared with other methods. The paper proposes to apply adaptive immune algorithm for reactive power optimization. The coding method for the control variables based on decimal system is introduced in detail. The test results of example systems demonstrate that the proposed reactive power optimization based on AIA method has advantages in terms of computation speed and convergence, and has great potential to be applied in practical power systems. [ABSTRACT FROM AUTHOR]

This paper discusses a new dedicated hardware architecture for hybrid optimization based on Genetic algorithm (GA) and Simulated Annealing (SA). The hybrid optimization of GA and SA in the proposed architecture realized searching not only globally but also locally. To keep general purpose, the proposed architecture is flexible for many genetic operations on GA and achieves high speed processing by adopting dedicated hardware. Simulation result evaluating the proposed architecture is shown to the effectiveness. [ABSTRACT FROM AUTHOR]

In this paper a new method based on the Ant-Miner algorithm is proposed to discover sets of unstructured classification rules. This method, called the Tree-Miner, creates a directed graph made up of nodes representing operators and operands. Each ant in a colony of artificial ants traverses this graph to find routes that represent the best unstructured rule antecedents. These antecedents are used to classify the given data and are also interpreted as knowledge hidden in the training data. The performance of the Tree-Miner algorithm was evaluated against that of the Ant-Miner according to the accuracy and the simplicity of the constructed rules. The results showed that our method has an acceptable predictive accuracy while discovering rules that are simpler and more comprehensive. [ABSTRACT FROM AUTHOR]

In this paper we present a new algorithm based on the Bee Colony Optimization (BCO) meta-heuristic for the Multidimensional Knapsack Problem (MKP), the goal of which is to find a subset of objects that maximizes a given objective function while satisfying some resource constraints. We show that our new algorithm obtains better results than Ant Colony Optimization algorithms and on most instances it reaches best known solutions. Especially we propose an efficient algorithm to produce randomly new solutions. [ABSTRACT FROM AUTHOR]

Robust loop shaping control is a feasible method for designing a robust controller; however, the controller designed by this method is complicated and difficult to implement practically. To overcome this problem, in this paper, a new design technique of a fixed-structure robust loop shaping controller for a highly maneuverable airplane, HIMAT, is proposed. The performance and robust stability conditions of the designed system satisfying H∞ loop shaping control are formulated as the objective function in the optimization problem. Particle Swarm Optimization (PSO) technique is adopted to solve this problem and to achieve the control parameters of the proposed controller. Simulation results demonstrate that the proposed approach is numerically efficient and leads to performance comparable to that of the other method. [ABSTRACT FROM AUTHOR]

A hybrid population based Ant Colony Optimization (ACO) algorithm PFold-P-ACO for protein folding in the HP model is proposed in this paper. This is the first population based ACO algorithm in the bioinformatics. It is shown experimentally that the algorithms achieves on nearly all test sequences at least comparable results to other state of the art algorithms. Compared to the state of the art ACO algorithm PFold-P-ACO slightly better results and is faster on long sequences. [ABSTRACT FROM AUTHOR]

Feature selection is a useful technique for increasing classification accuracy. The primary objective is to remove irrelevant features in the feature space and identify relevant features. Binary particle swarm optimization (BPSO) has been applied successfully in solving feature selection problem. In this paper, chaotic binary particle swarm optimization (CBPSO) with logistic map for determining the inertia weight is used. The K-nearest neighbor (K-NN) method with leave-one-out cross-validation (LOOCV) serves as a classifier for evaluating classification accuracies. Experimental results indicate that the proposed method not only reduces the number of features, but also achieves higher classification accuracy than other methods. [ABSTRACT FROM AUTHOR]

How to keep a balance between exploitation and exploration in Particle Swarm Optimization (PSO) for efficiently solving various optimization problems is an important issue. In order to handle premature convergence in PSO search, this paper proposes a novel algorithm, called Particle Swarm Optimization with Diversive Curiosity (PSO/DC), that introduces a mechanism of diversive curiosity into PSO for preventing premature convergence and ensuring exploration. A crucial idea here is to monitor the status of behaviors of swarm particles in PSO search by an interior indicator, and when swarm particles dropped into local minimum, they will be spontaneously reinitialized to start on finding other new solutions in search space. Applications of the proposal to a 2-dimensional optimization problem well demonstrate its effectiveness. Our experimental results indicate that the performance (90%) of the proposed method is superior in terms of success ratio to that (60%) of the PSO model optimized by EPSO. [ABSTRACT FROM AUTHOR]

Applications of the Particle Swarm Optimization (PSO) to solve image processing problem with a reference to a new automatic enhancement technique based on real-coded particle swarms is proposed in this paper. The enhancement process is a non-linear optimization problem with several constraints. The objective of the proposed PSO is to maximize an objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The feasibility of the proposed method is demonstrated and compared with Genetic Algorithms (GAs) based image enhancement technique. The obtained results indicate that the proposed PSO yields better results in terms of both the maximization of the number of pixels in the edges and the adopted objective evaluation. Computational time is also relatively small in the PSO case compared to the GA case. [ABSTRACT FROM AUTHOR]

In the design process of complex systems, the designer is solving an optimization problem, which involves different disciplines and where all design criteria have to be optimized simultaneously. Mathematically this problem can be reduced to a vector optimization problem. The solution of this problem is not unique and is represented by a Pareto surface in the space of the objective functions. Once a Pareto solution is obtained, it may be very useful for the decision-maker to be able to perform a quick local approximation in the vicinity of this Pareto solution in order to explore its sensitivity. In this paper, a method for obtaining linear and quadratic local approximations of the Pareto surface is derived. The concept of a local quick Pareto analyser is proposed. This concept is based on a local sensitivity analysis, which provides the relation between variations of the different objective functions under constraints. A few examples are considered. [ABSTRACT FROM AUTHOR]

It is generally conjectured that the Mann iteration converges faster than the Ishikawa iteration for any operator defined on an arbitrary closed convex subset of a Banach space. The recent result of Babu et al [1] shows that this conjecture can be proved for a class of quasi-contractive operators called the Zamfirescu operators[10]. In this paper it is shown that the proof can indeed be generalised to that of quasicontraction maps. [ABSTRACT FROM AUTHOR]

The major infrastructure component required to develop an underground mine is a decline, which is a system of tunnels used for access and haulage. In this paper we study the problem of designing a decline of minimum cost where cost is a combination of development and haulage costs over the life of the mine. A key design consideration is that the decline must be navigable to trucks and mining equipment, hence must satisfy a gradient and turning circle constraint. The decline is modelled as a mathematical network that captures the operational constraints and costs of a real mine, and is optimised using geometric techniques for constrained path optimisation. This procedure to find the optimal decline has been automated in a new version of a software tool, Decline Optimisation Tool, DOT™. A case study is described comparing this version with the earlier one. [ABSTRACT FROM AUTHOR]

In this paper, hybrid particle swarm optimization (PSO) is proposed for solving the challenging multi-contingency transient stability constrained optimal power flow (MC-TSCOPF) problem. The objective of this nonlinear optimization problem is to minimize the total fuel cost of the system and at the same time fulfil the transient stability requirements. The optimal power flow (OPF) with transient stability constraints considered is re-formulated as an extended OPF with additional rotor angle inequality constraints, which is suitable for hybrid PSO to solve. Comparison between various existing hybrid PSO techniques is carried out by solving the New England 39-bus system. Experimental results indicate that the hybrid PSO integrated with the mutation operation of genetic algorithms is better than the other existing hybrid PSO methods in both solution quality and stability. [ABSTRACT FROM AUTHOR]

This paper purposes a population based adaptive tuning for dymanic position control of robot manipulators. The dynamic behavior of a robot manipulator is highly nonlinear, and the positional control is conventionally achieved by inverse dynamics feed forward and PID feedback controllers. The proposed method tunes the PID controller parameters using cross-entropy optimization to minimize the error in tracking a repeated desired trajectory in real-time. The stability of the system is granted by snitching the inappropriate settings to a stable default using a real-time cost evaluation function. The proposed tunning method is tested on a two-joint planar manipulator, and on a planar inverted pendulum. The test results indicated that the proposed method improves the settling time and reduces the position error over the repeated paths. [ABSTRACT FROM AUTHOR]

In recent years, a high speed and accurate optimization method has been expected for large scale nonlinear programming problems. As a new solution method for nonlinear programming problems, a particle swarm optimization (PSO) method was proposed by Kennedy et al. and has attracted considerable attention. However, there exist few reports with respect to successful results for constrained nonlinear programming problems by PSO-based methods. Furthermore, PSO-based methods have a shortcoming that the search is liable to stopping at a certain local solution. In this paper, incorporating the bisection method and a homomorphous mapping to carry out the search considering constraints as well as the multiple stretching technique and modified move schemes of particles to restraining the stopping around local solutions. Furthermore, we show the efficiency of the proposed revised PSO method (rPSO) by comparing it with an existing method through the application of them into the numerical examples. [ABSTRACT FROM AUTHOR]

A quality selection problem generally identifies the process parameters such as the initial process means of a production process. The joint determination of the initial process means and the production run has been reported in the literature. Most of these studies considered a process with multiple independent nominal-the-better type of quality characteristics. In a real industrial situation, the quality characteristics may depend on each other. In addition, the process could have other types of quality characteristics. In this paper, a mathematical model is developed that jointly determines the optimum initial process means and production run of a process with multiple dependent smaller-the-better type of quality characteristics. The genetic algorithm, a global optimisation algorithm, is used as an optimisation algorithm in this study, whereas local optimisation algorithms have commonly been used in preceding studies. [ABSTRACT FROM AUTHOR]

This paper further extends the existing cost model for a fixed-life perishable product in a two-echelon inventory problem into the net profit model. The extended model is subsequently evaluated by comparing its results with those obtained from OptQuest, a commercial simulation-optimization package. Within a limited but reasonable time, the comparative results do not show a significant improvement of using OptQuest, but do confirm that the proposed model can already provide quite good quality of inventory control parameters for this system in a much shorter computational time. With some simple modifications to the proposed model, the model for slow-moving Poisson demand distribution for the two-echelon system is also developed and subsequently evaluated. The comparative results show that for the slow-moving demand, the Poisson model is more effective than the model with normality assumption. [ABSTRACT FROM AUTHOR]

This paper outlines the optimization problem of economic dispatch for real and reactive power by linear programming method. This model is further improved by optimal allocation of reactive power for minimizing the system losses and for improving the voltage profile. This is achieved by changing the generator bus voltage, transformer tap settings and varying the number of capacitor units. Mixed integer programming is used for solving this problem. The validity and effectiveness of the algorithm is verified by means of two examples: a IEEE 6 bus system,14 bus system and the 30 --Bus systems. Result for 6 bus systems are presented and discussed. The problem falls in the category of Mixed Integer Linear Programming (M.I.L.P.) and is solved by decomposing the problem into two smaller sub problems viz., a pure integer programming problem in binary variables and a linear programming problem. The solutions of these problems are coordinated to get the solution of the original problem. Very fast convergence is obtained by preventing zigzagging of the solution about the optimal point. All these result in considerable savings in computer time and memory. The Proposed model has been applied to sample systems and the results presented and discussed. [ABSTRACT FROM AUTHOR]