The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In a MMSE Ultra-WideBand(UWB) selective rake receiver, the selection of optimal subset of multipath is important for the performance of system. As the optimal solution is NP hard, the Discrete Particle Swarm Optimization Multipath Selection algorithm (DPSO-MSa) was proposed. The DPSO-MSa can get near optimal performance after certain iterations. Simulation results are presents to compare with the...
Reservoir optimal operation is a nonlinear, multi-stage and strong constraint combinatorial optimization problem. As the standard particle swarm optimization (SPSO) easily trapped into local optima, this paper proposes a hybrid algorithm combining particle swarm optimization algorithm with chaotic search algorithm, also referred to as CPSO algorithm. Making use of the stochastic property and ergodicity...
A novel approach for short-term hydrothermal power systems with cascaded reservoirs using cultural algorithm (CA) is presented in this paper. The large scale hydrothermal scheduling involves optimization of a non-linear objective function with a set of system and hydraulic constraints. The approach takes the water transport delay time between connected reservoirs and complicated hydraulic coupling...
The image segmentation of a robot binocular stereo vision system is the key issue in imaging processing. In this paper, the method of 2-d maximum entropy threshold image segmentation with Chaos PSO algorithm is used to segment the images information collected by a robot vision system, and the algorithm is checked by a real robot binocular stereo vision system. Moreover, compared with previous research,...
Adopt the concept of mutation in Genetic Algorithm to improve Particle Swarm Optimization. The experimental results show tow advantages for the mutation strategies. Firstly, It's workable for the algorithm and the performance was superior to fuzzy PSO and hybrid PSO; Secondly, the agitation strategy was more flexible.
Excitation system plays a key role in realistic simulation and analysis of the dynamic performance of electrical power systems to a large extent. Therefore, it is desire to obtain the real parameter of the excitation system in order to study the dynamic performance of power systems. This paper presents a practical technique to address the excitation system identification. The measurement was performed...
The constrained hydroelectric unit commitment problem is turned into an unconstrained optimization problem by means of penalty function method, and to improve the diversity and search ability, the classical particle swarm optimization (PSO) approach is enhanced by incorporating a genetic crossover operator and a self-adaptive decreasing inertia weight. Then the enhanced PSO is used to solve the hydroelectric...
This paper presents a novel algorithm for multiobjective training of Radial Basis Function (RBF) networks based on least-squares and Particle Swarm Optimization methods. The formulation is based on the fundamental concept that supervised learning is a bi-objective optimization problem, in which two conflicting objectives should be minimized. The objectives are related to the empirical training error...
When a local optimal solution is reached with classical Particle Swarm Optimization (PSO), all particles in the swarm gather around it, and escaping from this local optima becomes difficult. To avoid premature convergence of PSO, we present in this paper a novel variant of PSO algorithm, called MPSOM, that uses Metropolis equation to update local best solutions (lbest) of each particle and uses mutation...
According to the traditional morphological classification divide the quality of traditional Chinese medicine White Peony Root into first grade second grade and the third grade. Discrete the chromatography data of the White Peony Root which obtained under the condition of standard test and also make the information reduction. Obtaining the great peaks of linear independent vectors and obtaining every...
Particle swarm optimization (PSO) has shown to be an efficient, robust and simple optimization algorithm. Recently, the mono-objective version of the PSO algorithm was adapted and used to optimize only one performance of RF circuits, mainly the voltage gain of low noise amplifiers. In this work, we propose to optimize more than one performance function of LNAs while satisfying imposed and inherent...
Particle Swarm Optimization (PSO) modified to solve image processing problem with reference to enhancement technique is proposed in this paper. The enhancement process is an optimization problem with several constraints. The objective of the proposed PSO is to maximize an objective fitness criterion in order to improve the contrast and detail in an image by adapting the parameters of a novel extension...
A method for recognizing the emotion states of subjects based on 30 features extracted from their Galvanic Skin Response (GSR) signals was proposed. GSR signals were acquired by means of experiments attended by those subjects. Next the data was normalized with the calm signal of the same subject after being de-noised. Then the normalized data were extracted features before the step of feature selection...
Particle swarm optimization (PSO) is an optimization algorithm that has received much attention in recent years. PSO is a simple and computationally inexpensive algorithm inspired by social behavior of bird flocks and fish schools. However, PSO suffers from premature convergence, especially in high dimensional multimodal functions. To improve PSO performance on global optimization problems, this paper...
Based on FPTA-2, an adaptive binary particle swarm optimization (ABPSO) is proposed to overcome the shortcomings of binary particle swarm optimization (BPSO). Two circuits, an amplifier and an integrator, are evolved to compare ABPSO with genetic algorithm (GA) and Hereboy. Simulation results show ABPSO is a simple and powerful algorithm, well suited for evolvable hardware. It not only has satisfying...
In the few years, several neural networks are proposed to image classification. Support vector machine classifier employs the structural risk minimization principles, which make support vector machine classifier have good generalization ability. In order to solve the problem of parameters selection of support vector machine, particle swarm optimization is applied to select the parameters of support...
Optimal stochastic signaling is studied under second and fourth moment constraints for the detection of scalar-valued binary signals in additive noise channels. Sufficient conditions are obtained to specify when the use of stochastic signals instead of deterministic ones can or cannot improve the error performance of a given binary communications system. Also, statistical characterization of optimal...
Fuzzy clustering is an important problem which is the subject of active research in several real world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement. However FCM is sensitive to initialization and is easily trapped in local optima. Particle swarm optimization (PSO) is a stochastic global...
Motivated by the growing demand of accuracy and low computational time in optimizing functions in various fields of engineering, an approach has been presented using the technique of parallel computing. The parallelization has been carried out on one of the simplest and flexible optimization algorithms, namely the particle swarm optimization (PSO) algorithm. PSO is a stochastic population global optimizer...
Particle Swarm Optimization (PSO) algorithms represent a new approach for optimization. In this paper image enhancement is considered as an optimization problem and PSO is used to solve it. Image enhancement is mainly done by maximizing the information content of the enhanced image with intensity transformation function. In the present work a parameterized transformation function is used, which uses...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.