This article proposes an improved particle swarm optimization (PSO) with suggested parameter setting “Selective Particle Regeneration”. To evaluate its reliability and efficiency, this approach is applied to multimodal function optimizing tasks. 12 benchmark functions were tested, and results are compared with those of PSO and GA-PSO. It shows the proposed method is both robust and suitable for...

The aim of this research is to design a PID Controller using particle swarm optimization (PSO) algorithm for multiple-input multiple output (MIMO) Takagi-Sugeno fuzzy model. The conventional gain tuning of PID controller (such as Ziegler-Nichols (ZN) method) usually produces a big overshoot, and therefore modern heuristics approach such as PSO are employed to enhance the capability of tradition...

Given the relative limitations of BP and GA based leaning algorithms, Particle Swarm Optimization (PSO) is proposed to train Artificial Neural Networks (ANN) for the diagnosis of unexplained syncope. Compared with BP and GA based training techniques, PSO based learning method improves the diagnosis accuracy and speeds up the convergence process. Experimental results show that PSO is a robust tr...

A Particle Swarm Optimization (PSO) algorithm is examined to solve the inverse problem in structural health monitoring. The damage detection problem is formulated as a PSO problem to find a damaged stiffness matrix that satisfies the structure’s eigenequation and satisfying the necessary symmetry, sparsity, positive definiteness, and damage localization constraints. Finally, the PSO technique i...

Keywords: PSO QPSO Mean best position Weight parameter WQPSO a b s t r a c t Quantum-behaved particle swarm optimization (QPSO) algorithm is a global convergence guaranteed algorithms, which outperforms original PSO in search ability but has fewer parameters to control. In this paper, we propose an improved quantum-behaved particle swarm optimization with weighted mean best position according t...

This paper proposes a novel approach based on the training of the Neural Network method with Particle Swarm Optimization (PSO-NN) for identification of a hydraulic servo robot. The robot is considered to have two degrees of freedom; one is rotational and the other is translational. A feed forward NN is designed for the problem and the weights of the network are trained using Particle Swarm Opti...

In this paper a novel technique for Neighbor embedding single image super resolution (SR) is proposed. Given a low-resolution image, its high-resolution image is reconstructed from a set of training images, which are composed of one or more low-resolution and corresponding highresolution image pairs. In this paper we propose a new approach to a single image super-resolution through neighbor emb...

Due to many classical identification methods cannot be directly used for closed-loop control system, an improved identification method is proposed to simultaneously identify model parameters and the structure. The improved identification method used the genetic algorithm to estimate the initial search scope for the PSO algorithm, and then used the search result as the initial value of the Rosen...

Inertia weight is a most important parameter of particle swarm optimization (PSO), which can keep a right balance between the global search and local search. In this paper, a novel PSO with non-linear inertia weight based on the tangent function is provided. The paper also presents the method of determining a control parameter in our proposed method, saving the user from a tedious trial and err...

Image entropy thresholding approach has drawn the attentions in image segmatation. The endeavor of this paper is focused on multilevel thresholding using the minimum cross enrtop criterion. In the literature, the particle swarm optimization (PSO) had been applied to conducting the thresold selection. The adopted algorithm used in this paper is the honey bee mating optimization (HBMO). In experi...