Adaptive Optimization Technique Research Articles

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM-PARTICLE SWARM OPTIMIZATION BASED STABILITY MAINTENANCE OF POWER SYSTEM NETWORKS

During faulty condition voltage instability is one of the major crisis in power system networks. This study proposes a hybrid learning algorithm to improve the stability performance of a power system with Distributed Generations (DGs). Here the distribution system stability is maintained with reduced power loss using an Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Particle Swarm Optimization (PSO) techniques. In this study distributed generations is considered as several types of DGS connected together which is called as Microgrid (MG). Initially ANFIS is trained by instability parameters to give the optimal power capacity of the microgrid and then PSO algorithm is applied to find the optimum bus for connecting microgrid in the system. The effective improvements in voltage profile and reduction in power loss of the proposed ANFIS-PSO controller is tested on IEEE-30 bus system and has been presented with few comparative results.

Cooperative adaptive optimization for a class of nonlinear systems

In this paper, we propose an adaptive optimization technique to solve large scale optimization problems for nonlinear systems composed of multiple interacting agents. The framework is based on the concept of cooperative optimization where each agent addresses a specific local optimization problem that requires information from some or all other agents. An inexact penalty function method is used to solve the overall cooperative optimization problem. The closed-loop follows an adaptive gradient flow that leads to the tracking of local critical points with guaranteed transient performance.

Contamination Source Identification in Water Distribution Systems Using an Adaptive Dynamic Optimization Procedure

Contamination source identification involves the characterization of the contaminant source based on observations that stream from a set of sensors in a water distribution system (WDS). The streaming data can be processed adaptively to provide an estimate of the source characteristics at any time once the contamination event is detected. In this paper, an adaptive dynamic optimization technique (ADOPT) is proposed for providing a real-time response to a contamination event. A new multiple population–based search that uses an evolutionary algorithm (EA) is investigated. To address nonuniqueness in the initial stages of the search and prevent premature convergence of the EA to an incorrect solution, the multiple populations are designed to maintain a set of alternative solutions that represent various nonunique solutions. As more observations are added, the EA solutions not only migrate to better solution states but the number of solutions decreases as the degree of nonuniqueness diminishes. This new algorithm adaptively converges to the solutions that best match the available observations. The use of the developed method is demonstrated for two WDS networks.

An adaptive optimization technique for dynamic environments

The use of evolutionary algorithms (EAs) is beneficial for addressing optimization problems in dynamic environments. The objective function for such problems changes continually; thus, the optimal solutions likewise change. Such dynamic changes pose challenges to EAs due to the poor adaptability of EAs once they have converged. However, appropriate preservation of a sufficient level of individual diversity may help to increase the adaptive search capability of EAs. This paper proposes an EA-based Adaptive Dynamic OPtimization Technique (ADOPT) for solving time-dependent optimization problems. The purpose of this approach is to identify the current optimal solution as well as a set of alternatives that is not only widespread in the decision space, but also performs well with respect to the objective function. The resultant solutions may then serve as a basis solution for the subsequent search while change is occurring. Thus, such an algorithm avoids the clustering of individuals in the same region as well as adapts to changing environments by exploiting diverse promising regions in the solution space. Application of the algorithm to a test problem and a groundwater contaminant source identification problem demonstrates the effectiveness of ADOPT to adaptively identify solutions in dynamic environments.

Improvement to the scanning electron microscope image colourization by adaptive tuning

An improvement to the previously proposed Canny optimization technique for scanning electron microscope image colorization is reported. The additional process is adaptive tuning, where colour tuning is performed adaptively, based on comparing the original luminance values with calculated luminance values. The complete adaptive Canny optimization technique gives significantly better mechanical contrast on scanning electron microscope grey-scale images than do existing methods.

Adaptive pattern nulling design of linear array antenna by phase‐only perturbations using memetic algorithms

AbstractIn this paper, the pattern nulling of a linear array for interference cancellation is derived by phase‐only perturbations using memetic algorithms (MAs). The MAs uses improvement procedures which is obtained by incorporating local search into the genetic algorithms. It is proposed to improve the search ability of genetic algorithms. MA is a kind of an improved type of the traditional genetic algorithms. By using local search procedure, it can avoid the shortcoming of the traditional genetic algorithms, whose termination criteria are set up by using the trial and error method. The MA is applied to find the pattern nulling of the proposed adaptive antenna. This design for radiation pattern nulling of an adaptive antenna can suppress interference by placing a null at the direction of the interfering source, i.e. to increase the signal to interference ratio. This proposed method is that an innovative adaptive antenna optimization technique is also able to solve the multipath problem which exists in practical wireless communication systems. Two examples are provided to justify the proposed phase‐only perturbations approach based on MAs. Computer simulation results are given to demonstrate the effectiveness of the proposed method. Copyright © 2007 John Wiley & Sons, Ltd.

Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training

BackgroundParticle Swarm Optimization (PSO) is an established method for parameter optimization. It represents a population-based adaptive optimization technique that is influenced by several "strategy parameters". Choosing reasonable parameter values for the PSO is crucial for its convergence behavior, and depends on the optimization task. We present a method for parameter meta-optimization based on PSO and its application to neural network training. The concept of the Optimized Particle Swarm Optimization (OPSO) is to optimize the free parameters of the PSO by having swarms within a swarm. We assessed the performance of the OPSO method on a set of five artificial fitness functions and compared it to the performance of two popular PSO implementations.ResultsOur results indicate that PSO performance can be improved if meta-optimized parameter sets are applied. In addition, we could improve optimization speed and quality on the other PSO methods in the majority of our experiments. We applied the OPSO method to neural network training with the aim to build a quantitative model for predicting blood-brain barrier permeation of small organic molecules. On average, training time decreased by a factor of four and two in comparison to the other PSO methods, respectively. By applying the OPSO method, a prediction model showing good correlation with training-, test- and validation data was obtained.ConclusionOptimizing the free parameters of the PSO method can result in performance gain. The OPSO approach yields parameter combinations improving overall optimization performance. Its conceptual simplicity makes implementing the method a straightforward task.

Optimized ensemble Monte Carlo simulations of dense Lennard-Jones fluids

We apply the recently developed adaptive ensemble optimization technique to simulate dense Lennard-Jones fluids and a particle-solvent model by broad-histogram Monte Carlo techniques. Equilibration of the simulated fluid is improved by sampling an optimized histogram in radial coordinates that shifts statistical weight towards the entropic barriers between the shells of the liquid. Interstitial states in the vicinity of these barriers are identified with unprecedented accuracy by sharp signatures in the quickly converging histogram and measurements of the local diffusivity. The radial distribution function and potential of mean force are calculated to high precision.

Adaptive Stimulus Optimization for Auditory Cortical Neurons

Despite the extensive physiological work performed on auditory cortex, our understanding of the basic functional properties of auditory cortical neurons is incomplete. For example, it remains unclear what stimulus features are most important for these cells. Determining these features is challenging given the considerable size of the relevant stimulus parameter space as well as the unpredictable nature of many neurons' responses to complex stimuli due to nonlinear integration across frequency. Here we used an adaptive stimulus optimization technique to obtain the preferred spectral input for neurons in macaque primary auditory cortex (AI). This method uses a neuron's response to progressively modify the frequency composition of a stimulus to determine the preferred spectrum. This technique has the advantage of being able to incorporate nonlinear stimulus interactions into a "best estimate" of a neuron's preferred spectrum. The resulting spectra displayed a consistent, relatively simple circumscribed form that was similar across scale and frequency in which excitation and inhibition appeared about equally prominent. In most cases, this structure could be described using two simple models, the Gabor and difference of Gaussians functions. The findings indicate that AI neurons are well suited for extracting important scale-invariant features in sound spectra and suggest that they are designed to efficiently represent natural sounds.

Adaptive error-constrained method for LMS algorithms and applications

An adaptive error-constrained least mean square (AECLMS) algorithm is derived and proposed using adaptive error-constrained optimization techniques. This is accomplished by modifying the cost function of the LMS algorithm using augmented Lagrangian multipliers. Theoretical analyses of the proposed method are presented in detail. The method shows improved performance in terms of convergence speed and misadjustment. This proposed adaptive error-constrained method can easily be applied to and combined with other LMS-type stochastic algorithms. Therefore, we also apply the method to constant modulus criterion for blind method and backpropagation algorithm for multilayer perceptrons. Simulation results show that the proposed method can accelerate the convergence speed by 2 to 20 times depending on the complexity of the problem.

Building Efficient Overlays

Peer-to-peer (P2P) and Grid computing systems have emerged as popular models aiming at further utilizing Internet information and resources, complementing the available client–server services. However, the mechanism of peers randomly choosing logical neighbors without any knowledge about underlying physical topology can cause a serious topology mismatch problems between the overlay network and the physical underlying network. The topology mismatch problem brings a great stress in the Internet infrastructure and greatly limits the performance gain from various search or routing techniques in P2P and Grid systems. Aiming at alleviating the mismatch problem and reducing the unnecessary traffic, we have proposed two approaches, adaptive overlay topology optimization (AOTO) and location-aware topology matching (LTM) techniques, to reduce the total traffic cost and average query response time. Both AOTO and LTM are scalable and completely distributed in the sense that they do not require any global knowledge of the whole overlay network when each node is optimizing the organization of its logical neighbors. This paper shows the effectiveness of AOTO and LTM and compares the performance of these two approaches through simulation studies.

An Adaptive Probe-Based Technique to Optimize Join Queries in Distributed Internet Databases

An adaptive probe-based optimization technique is developed and demonstrated in the context of an Internet-based distributed database environment. More and more common are database systems, which are distributed across servers communicating via the Internet where a query at a given site might require data from remote sites. Optimizing the response time of such queries is a challenging task due to the unpredictability of server performance and network traffic at the time of data shipment; this may result in the selection of an expensive query plan using a static query optimizer. We constructed an experimental setup consisting of two servers running the same DBMS connected via the Internet. Concentrating on join queries, we demonstrate how a static query optimizer might choose an expensive plan by mistake. This is due to the lack of a priori knowledge of the run-time environment, inaccurate statistical assumptions in size estimation, and neglecting the cost of remote method invocation. These shortcomings are addressed collectively by proposing a probing mechanism. Furthermore, we extend our mechanism with an adaptive technique that detects sub-optimality of a plan during query execution and attempts to switch to the cheapest plan while avoiding redundant work and imposing little overhead. An implementation of our run-time optimization technique for join queries was constructed in the Java language and incorporated into an experimental setup. The results demonstrate the superiority of our probe-based optimization over a static optimization.

Adaptive Optimization Technique of 3D Data Display for Each Terminal Performance

Adaptive Optimization Technique of 3D Data Display for Each Terminal Performance

A Probe-Based Technique to Optimize Join Queries in Distributed Internet Databases

An adaptive probe-based optimization technique is developed and demonstrated in the context of an Internet-based distributed database environment. More and more common are database systems which are distributed across servers communicating via the Internet where a query at a given site might require data from remote sites. Optimizing the response time of such queries is a challenging task due to the unpredictability of server performance and network traffic at the time of data shipment; this may result in the selection of an expensive query plan using a static query optimizer. We constructed an experimental setup consisting of two servers running the same database management system connected via the Internet. Concentrating on join queries, we demonstrate how a static query optimizer might choose an expensive plan by mistake. This is due to the lack of a priori knowledge of the run-time environment, inaccurate statistical assumptions in size estimation, and neglecting the cost of remote method invocation. These shortcomings are addressed collectively by proposing a probing mechanism. An implementation of our run-time optimization technique for join queries was constructed in the Java language and incorporated into an experimental setup. The results demonstrate the superiority of our probe-based optimization over a static optimization.

Adaptive quantum homodyne tomography

An adaptive optimization technique to improve precision of quantum homodyne tomography is presented. The method is based on the existence of so-called null functions, which have zero average for arbitrary state of radiation. Addition of null functions to the tomographic kernels does not affect their mean values, but changes statistical errors, which can then be reduced by an optimization method that "adapts" kernels to homodyne data. Applications to tomography of the density matrix and other relevant field-observables are studied in detail.

Nonlinear adaptive optimization of biomass productivity in continuous bioreactors

A novel on-line adaptive optimization algorithm is developed and applied to continuous biological reactors. The algorithm makes use of a simple nonlinear estimation model that relates either the cell-mass productivity or the cell-mass concentration to the dilution rate. On-line estimation is used to recursively identify the parameters in the nonlinear process model and to periodically calculate and steer the bioreactor to the dilution rate that yields optimum cell-mass productivity. Thus, the algorithm does not require an accurate process model, locates the optimum dilution rate online, and maintains the bioreactors at this optimum condition at all times. The features of the proposed new algorithm are compared with those of other adaptive optimization techniques presented in the literature [1–5]. A detailed simulation study using three different microbial system models [3, 6–7] was conducted to illustrate the performance of the optimization algorithm.

Motivation and framework for using genetic algorithms for microcode compaction

Genetic algorithms are a robust adaptive optimization technique based on a biological paradigm. They perform efficient search on poorly-defined spaces by maintaining an ordered pool of strings that...

Motivation and framework for using genetic algorithms for microcode compaction

Genetic algorithms are a robust adaptive optimization technique based on a biological paradigm. They perform efficient search on poorly-defined spaces by maintaining an ordered pool of strings that represent regions in the search space. New strings are produced from existing strings using the genetic-based operators of recombination and mutation. Combining these operators with natural selection results in the efficient use of hyperplane information found in the problem to guide the search. The searches are not greatly influenced by local optima or non-continuous functions. Genetic algorithms have been successfully used in problems such as the traveling salesperson and scheduling job shops. Microcode compaction can be modeled as these same types of problems, which motivates the application of genetic algorithms in this domain.

Adaptive extremum control using approximate process models

AbstractThe proposed on‐line adaptive optimization technique incorporates a priori knowledge in the form of approximate steady‐state models. The steady‐state geometric characteristics of the model are periodically recalculated using a Hammerstein system and recursive least squares. The algorithm is self‐tuning in the sense that it converges to the optimal performance provided that a matching condition is satisfied and that the data are persistently exited. Simulation and experimental studies performed on a continuous fermentation system have been conducted to illustrate the performance of the optimization algorithm and demonstrate the viability of adaptive extremum control.

Nonlinear Adaptive Optimization of Continuous Bioreactors

An on-line adaptive optimization technique is proposed and applied to a continuous biological reactor. The procedure involves simple steady-state models which contain the basic static nonlinearities of the process and a nonlinear least squares estimator which is used to periodically calculate the steady-state geometric characteristics of the process. Simulation experiments have been conducted to illustrate the performance of the optimization algorithm.