Nature-inspired algorithms are a very promising tool for solving the hardest problems in computer sciences and mathematics. These algorithms are typically inspired by the fascinating behavior at display in biological systems, such as bee swarms or fish schools. So far, these algorithms have been applied in many practical applications. In this paper, we present a simple particle swarm optimizati...

The Particle Swarm Optimisation (PSO) algorithm has been established as a useful global optimisation algorithm for multi-dimensional search spaces. A practical example is its success in training feed-forward neural networks. Such successes, however, must be judged relative to the complexity of the search space. In this paper we show that the effectiveness of the PSO algorithm breaks down when e...

A new formulation of bi-objective optimization for the general capacitor placement and sizing problem is presented. The objectives include energy loss reduction, investment cost minimization, and maximum voltage deviation improvement. The operating and expansion constraints of the system are considered for practical needed. Also, both fixed and switched types of capacitors are included. A parti...

An efficient Particle Swarm Optimization (PSO) technique, employed to solve Economic Dispatch (ED) problems in power system is presented in this paper. With practical consideration, ED will have nonsmooth cost functions with equality and inequality constraints that makes the problem, a large-scale highly constrained nonlinear optimization problem.The proposed method expands the original PSO to ...

| Inverse problems are ill-posed. Posterior sampling is the way of providing an estimate of the uncertainty based on a finite set of the family of models that fit the observed data within the same tolerance. Monte Carlo methods are used for this purpose but they are highly inefficient. Global optimization methods are able to address the sampling problem. Particle Swarm is a very interesting alg...

In this paper, an Interactive Compromise Approach with Particle Swarm Optimization(ICA-PSO) is presented to solve the Economic Emission Dispatch(EED) problem. The cost function and emission function are modeled as the nonsmooth functions, respectively. The bi-objective including both the minimization of cost and emission is formulated in this paper. ICA-PSO is proposed to solve EED problem for ...

Machine-to-machine (M2M) communications emerge to autonomously operate to link interactions between Internet cyber world and physical systems. We present the technological scenario of M2M communications consisting of wireless infrastructure to cloud, and machine swarm of tremendous devices. Related technologies toward practical realization are explored to complete fundamental understanding and ...

An interactive pareto optimal with advantage (IPOA) approach for bi-objective programming is proposed and applied on capacitor placement and sizing problem. Two main contradictory concerned which including cost and quality properties are considered as bi-objective programming formulation. The IPOA approach can provide a valuable trade-off pareto-optimal solution by following the intention of de...

The prediction of silicon content in hot metal has been a major study subject as one of the most important means for the monitoring state in ferrous metallurgy industry. A prediction model of silicon content is established based on the support vector regression (SVR) whose optimal parameters are selected by chaos particle swarm optimization. The data of the model are collected from No. 3 BF in ...

Extended Kalman filter (EKF) is widely used for speed estimation in sensorless vector control of induction motor. The major and unsolved issue in the practical implementation of the EKF is the choice of the process and measurement noise covariance matrices. In this paper, a speed estimation method using EKF optimized by improved particle swarm optimization (IPSO) is proposed. By combining the a...