This paper presents a control system design method for an autonomous helicopter using the Particle Swarm Optimization (PSO) method. In this paper, the nonlinear dynamic model of a miniature helicopter is directly used to design a control system without linearization process. The nonlinear dynamics of model helicopter is represented by sixteen state variables including flapping dynamics, engine ...

Training classifiers can be seen as an optimization problem. With this view, we have developed a method to train a type of nearest centroid classifier with PSO. Results showed an improvement on most of the datasets tested. Additionally, we have developed a method to utilize the developed classifier with datasets containing both numeric and categorical data by integrating the centroid algorithm ...

In this paper a new method based on the support vector machine (SVM) combined with particle swarm optimization (PSO) is proposed to analyze signals of wound infection detection based on electronic nose (enose). Owing to the strong impact of sensor array optimization and SVM parameters selection on the classification accuracy of SVM, PSO is used to realize a synchronization optimization of senso...

This paper presents a novel and efficient method to estimate the equivalent circuit parameters of three-phase induction motor from its manufacturer data for steady state analysis using improved particle swarm optimization (IPSO). The IPSO integrates the particle swarm optimization (PSO) with the chaotic sequences. The optimization problem is based on minimizing the error between the computed pe...

In this paper, the optimization of architectural parameters for a class of translational parallel kinematic machine (PKM) is performed with the particle swarm optimization (PSO) to achieve the best accuracy characteristics. The conventional error transformation matrix (ETM) is derived based on the differentiation of kinematic equations, and a new error amplification index (EAI) over a usable wo...

The traditional methods such as critical path method (CPM) and linear programming (LP) have difficulty solving more general scheduling problems such as resource constrained scheduling problems. Emerging techniques such as particle swarm optimisation (PSO) have shown advantages in addressing this problem. However, the performance of simple PSO is greatly dependent on its parameters, and bad sele...

This paper presents Chemo-tactic PSO-DE (CPSO-DE) optimization algorithm combined with Lagrange Relaxation method (LR) for solving Unit Commitment (UC) problem. The proposed approach employs Chemo-tactic PSO-DE algorithm for optimal settings of Lagrange multipliers. It provides high-quality performance and reaches global solution and is a hybrid heuristic algorithm based on Bacterial Foraging O...

This paper proposes a new multilevel thresholding method segmenting images based on particle swarm optimization (PSO). In the proposed method, the thresholding problem is treated as an optimization problem, and solved by using the principle of PSO. The algorithm of PSO is used to find the best values of thresholds that can give us an appropriate partition for a target image according to a fitne...

The prediction of chaotic time series is an important research issue. To improve the prediction accuracy, a hybrid approach called WNN-PSO is proposed, which based on the self-learning ability of wavelet neural network, whose parameters are optimized by particle swarm optimization. The WNN-PSO method has higher prediction accuracy, fast convergence, and heightens the ability of jumping the loca...

This chapter introduces a novel technique to evaluate composite power system reliability indices and their sensitivities with respect to the control parameters using a dynamically directed binary Particle Swarm Optimization (PSO) search method. A key point in using PSO in power system reliability evaluation lies in selecting the weighting factors associated with the objective function. In this ...