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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]

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]

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]

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]

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]

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]

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]

These The Diameter-Constrained Minimum Spanning Tree Problem is about finding a minimum cost spanning tree, subjected to pre-defined constraints on the number of edges that can integrate the path between any pair of nodes. This problem typically models network design applications where all vertices must communicate with each other at a minimum cost, while meeting a given quality requirement. This is the typical situation addressed in an environment of Multi-Protocol Label Switching networks over Wavelength Division Multiplexing network design, in which an IP travelling pack goes through a delay queuing, proportional to the number of Label Switching Routers that it crosses. In this paper we describe different types of heuristics that were implemented in order to find optimal or near-optimal solutions to solve this problem. [ABSTRACT FROM AUTHOR]