Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

IEEE Transactions on Pattern Analysis and Machine Intelligence

Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm motivated by intelligent collective behavior of some animals such as flocks of birds or schools of fish. Since presented in 1995, it has experienced a multitude of enhancements. As researchers have learned about the technique, they derived new versions aiming to different demands, developed new applications in a host of areas, published theoretical studies of the effects of the various parameters and proposed many variants of the algorithm. This paper introduces its origin and background and carries out the theory analysis of the PSO. Then, we analyze its present situation of research and application in algorithm structure, parameter selection, topology structure, discrete PSO algorithm and parallel PSO algorithm, multi-objective optimization PSO and its engineering applications. Finally, the existing problems are analyzed and future research directions are presented.

Particle swarm optimization algorithm: an overview

The book aims to provide a broad overview of various topics of the Internet of Things (IoT) from the research and development priorities to enabling technologies, architecture, security, privacy, interoperability and industrial applications. It is intended to be a standalone book in a series that covers the Internet of Things activities of the IERC ? Internet of Things European Research Cluster from technology to international cooperation and the global "state of play". The book builds on the ideas put forward by the European research Cluster on the Internet of Things Strategic Research Agenda and presents global views and state of the art results on the challenges facing the research, development and deployment of IoT at the global level.

Internet of Things: Converging Technologies for Smart Environments and Integrated Ecosystems

Particle swarm optimization (PSO) is a stochastic population-based algorithm motivated by intelligent collective behavior of some animals. The most important advantages of the PSO are that PSO is easy to implement and there are few parameters to adjust. The inertia weight (w) is one of PSO's parameters originally proposed by Shi and Eberhart to bring about a balance between the exploration and exploitation characteristics of PSO. Since the introduction of this parameter, there have been a number of proposals of different strategies for determining the value of inertia weight during a course of run. This paper presents the first comprehensive review of the various inertia weight strategies reported in the related literature. These approaches are classified and discussed in three main groups: constant, time-varying and adaptive inertia weights. A new adaptive inertia weight approach is also proposed which uses the success rate of the swarm as its feedback parameter to ascertain the particles' situation in the search space. The empirical studies on fifteen static test problems, a dynamic function and a real world engineering problem show that the proposed particle swarm optimization model is quite effective in adapting the value of w in the dynamic and static environments.

A novel particle swarm optimization algorithm with adaptive inertia weight

This paper presents "A novel adaptive inertia weight with stability condition for particle swarm optimization (SAIW)". This approach determines the inertia weight in different dimensions for each particle on: (1) its performance and (2) distance from its best position, and by considering the stability condition, the acceleration parameters of PSO are adaptively determined. Presents an adaptive method for finding inertia weight in different dimensions for each particle.The success of the particle and displacement in particle's best position are used as the feedback.Stability analysis of proposed model indicates that its performance is usually optimal.The results clearly show the superiority of the proposed model over the existing methods. Particle swarm optimization (PSO) is a stochastic population-based algorithm motivated by intelligent collective behavior of birds. The performance of the PSO algorithm highly depends on choosing appropriate parameters. Inertia weight is a parameter of this algorithm which was first proposed by Shi and Eberhart to bring about a balance between the exploration and exploitation characteristics of PSO. This paper presents an adaptive approach which determines the inertia weight in different dimensions for each particle, based on its performance and distance from its best position. Each particle will then have different roles in different dimensions of the search environment. By considering the stability condition and an adaptive inertia weight, the acceleration parameters of PSO are adaptively determined. The corresponding approach is called stability-based adaptive inertia weight (SAIW). The proposed method and some other models for adjusting the inertia weight are evaluated and compared. The efficiency of SAIW is validated on 22 static test problems, moving peaks benchmarks (MPB) and a real-world problem for a radar system design. Experimental results indicate that the proposed model greatly improves the PSO performance in terms of the solution quality as well as convergence speed in static and dynamic environments.

A novel stability-based adaptive inertia weight for particle swarm optimization

Model-based human gait recognition using leg and arm movements

Correlational Convolutional LSTM for human action recognition

We focus on visually meaningful color image watermarks, we construct a new digital watermarking scheme based on the discrete cosine transformation. The proposed method uses the sensitivity of human eyes to adoptively embed a watermark in a color image. In addition, to prevent tampering or unauthorized access, a new watermark permutation function is proposed, which causes a structural noise over the extracted watermark. Also, we have proposed a procedure to eliminate this noise to decrease false positives and false negatives in the extracted watermark. The experimental results show that embedding the color watermark adapted to the original image produces the most imperceptible and the most robust watermarked image under geometric and volumetric attacks.

Reza Safabakhsh

Papers

A novel particle swarm optimization algorithm with adaptive inertia weight

A novel stability-based adaptive inertia weight for particle swarm optimization

Model-based human gait recognition using leg and arm movements

Correlational Convolutional LSTM for human action recognition

A novel DCT-based approach for secure color image watermarking