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This work deals with the optimal control problem which has been proposed to solve using the discrete augmented lagrangian based non-linear programming approach. It is shown that this technique guarantee a satisfactory performance in the face of both optimality by minimizing the energy and maximizing the output. Later on, the optimization has been more effective by using PSO-GA-Based Optimization to...
With the development of network, users'services put forward diverse demands on the network QoS (Quality of Service), the QoS routing is the optimization problem under the satisfaction of multiple QoS constraints. This paper firstly sets up a multi-constrained QoS routing model and constructs the fitness value function by transforming the QoS constraints with a penalty function. Secondly, we merge...
Fractal image compression explores the self-similarity property of a natural image and utilizes the partitioned iterated function system (PIFS) to encode it. This technique is of great interest both in theory and application. However, it is time-consuming in the encoding process and such drawback renders it impractical for real time applications. The time is mainly spent on the search for the best-match...
This paper presents an intelligent binary particle swarm optimization (IBPSO) scheme with the Lambda-iteration method to solve the unit commitment problem (UCP). The UCP is considered as two linked optimization sub-problems: the unit-scheduled problem that can be solved by the BPSO method for the minimization of the transition cost, and the economic dispatch (ED) problem that can be solved by the...
The optimal placement of Distributed Generation (DG) has attracted many researchers' attention recently due to its ability to obviate defects caused by improper installation of DG units, such as rise in system losses, decline in power quality, voltage increase at the end of feeders and etc. This paper presents a new advanced method for optimal allocation of DG in distribution systems. In this study,...
A new particle swarm optimization (PSO) algorithm is presented based on three methods of improvement in original PSO. First, the iteration formula of PSO is changed and simplified by removal of velocity parameter that is unnecessary during the course of evolution. Second, the dynamically decreasing inertia weight is employed to enhance the balance of global and local search of algorithm. Finally,...
When basic particle swarm optimization algorithm (PSO) is used to resolve some complex problems, its global optimal model usually falls into local optimal value and its local model has slowest convergence velocity in the later stage of evolution. So, a simplified particle swarm optimization algorithm is proposed. Firstly, all particles in whole swarm are divided into three categories, denoted as the...
In this paper, by using the ergodicity of chaos to improve the traditional particle swarm optimization algorithm, a chaos-PSO based hybrid optimization method is proposed. The core of document classification is constructing the classification model, the chaos PSO algorithm is utilized to extract classification rules so as to build the model rapidly. Michigan scheme is introduced to encode the rule,...
The existing chaos optimization algorithms were almost based on Logistic map. However, the probability density function of chaotic sequences for Logistic map is a Chebyshev type function, which may affect the global searching capacity and computational efficiency of chaos optimization algorithm. In this paper, firstly, a new chaotic sequences with Skew Tent map (STM) is established, and is improved...
Gaussian mixture models (GMM) are widely used for unsupervised classification applications in remote sensing. Expectation-Maximization (EM) is the standard algorithm employed to estimate the parameters of these models. However, such iterative optimization methods can easily get trapped into local maxima. Researchers use population-based stochastic search algorithms to obtain better estimates. We present...
This paper presents an improved PSO algorithm (TPSO) with dynamic parameter selection, in which the inertia weight and acceleration coefficients are defined by trigonometric functions of the present iterative generation. Compared with some typical PSO models, TPSO shows its superiority in convergence, robustness and solution quality through experimental tests with 8 widely used benchmark functions.
In this paper, we apply simulation-based optimization methods to detect quadrature amplitude modulated (QAM) MIMO signals. The first one is the so-called Cross-Entropy (CE) method which enables the corresponding CE-based detector to provide bit-error-rate (BER) performance close to that achievable by the maximum-likelihood (ML) detector when the signal-to-noise ratio (SNR) is relatively low. Unfortunately,...
Aiming at the premature convergence problem of particle swarm optimization algorithm, a new particle swarm Optimization algorithm with dynamic adaptive inertia weigh was presented to solve the typical multi-peak, high dimensional function optimization problems. The dynamic adaptive strategy was introduced in this new algorithm and the change of inertia weight was formulated as an adjust function of...
A multi-objective particle swarm optimization algorithm based on Ε-dominance is proposed. The Ε-dominance is applied to update the external set in order to obtain the Pareto set with better distribution, and the dynamic adjustment strategy, which made the algorithm achieving the search and approximation to the Pareto set, is adopted in the iterative process of the Ε-Pareto solution set. Three benchmark...
During the iterative process of standard particle swarm optimization (PSO), the premature convergence of particles decreases the algorithm's searching ability. Through analyzing the reason of particle premature convergence during the renewal process, introducing the updating strategy based on cloning technique, cloning particle swarm optimization (CPSO) algorithm with hybrid discrete variables model...
Transcription factors (TFs) play an important role in regulating the expression of genes. The accurate measurement of transcription factor activities (TFAs) depends on a series of experimental technologies of molecular biology and is intractable in most practical situations. Some signal processing methods for blind source separation have been applied in the prediction of TFAs from gene expression...
Based on subarea division of the power system and multithread technology, a distributed parallel coordinated control strategy for automatic voltage control system is proposed. Using auxiliary problem principle (APP) method, a complex power system is decomposed into several logical independent subsystems geographically, which are coordinated via restrictions of the jointed borders. An iterative computation...
This paper presents an improved particle swarm optimization algorithm with feasibility- based rules (FRIPSO) to solve mixed-variable constrained optimization problems. Different kinds of variables are dealt in different ways in FRIPSO algorithm. Constraint handling is based on simple feasibility-based rules without the use of a penalty function which is frequently cumbersome to parameterize, nor need...
During the iterative process of standard particle swarm optimization (PSO), the premature convergence of particles decreases the algorithm's searching ability. Through analyzing the reason of particle premature convergence during the renewal process, by introducing the selection strategy based on antibody density and initiation based on equal probability chaos, chaos immune particle swarm optimization...
Particle Swarm Optimization (PSO) has shown its fast search speed and good search ability in many optimization problems. However, PSO easily suffers from local minima when dealing with complex problems. To enhance the basic PSO, this paper presents an improved PSO algorithm namely PMPSO, which employs a power mutation (PM) on the global particle. It is to hope that the mutation could help particles...
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