This paper introduces the Particle Swarm Optimization (PSO) algorithm to solve the optimal network reconfiguration problem for power loss reduction. The PSO is a relatively new and powerful intelligence evolution method for solving optimization problems. It is a population-based approach. The PSO was inspired from natural behavior of the bees on how they find the location of most flowers. The p...

A number of algorithms based on the evolutionary processing have been proposed forcommunication networks backbone such as Genetic Algorithm (GA). However, there has beenlittle work on the SWARM optimization algorithms such as Particle Swarm Optimization(PSO) for backbone topology design. In this paper, the performance of PSO on GA isdiscussed and a new algorithm as PSOGA is proposed for the net...

In this paper an extensive theoretical and empirical analysis of recently introduced Particle Swarm Optimization algorithm with Convergence Related parameters (CR-PSO) is presented. The convergence of the classical PSO algorithm is addressed in detail. The conditions that should be imposed on parameters of the algorithm in order for it to converge in mean-square have been derived. The practical...

This article introduces a model-based reinforcement learning (RL) approach for continuous state and action spaces. While most RL methods try to find closed-form policies, the approach taken here employs numerical on-line optimization of control action sequences. First, a general method for reformulating RL problems as optimization tasks is provided. Subsequently, Particle Swarm Optimization (PS...

in this paper power system stability enhancement through static synchronous compensator (statcom)based controller is investigated. the potential of the statcom supplementary controllers to enhance thedynamic stability is evaluated. the design problem of statcom based damping controller is formulatedas an optimization problem according to the eigenvalue based objective function that is solved by...

This paper proposes a novel population-based evolution algorithm named grouping-shuffling particle swarm optimization (GSPSO) by hybridizing particle swarm optimization (PSO) and shuffled frog leaping algorithm (SFLA) for continuous optimization problems. In the proposed algorithm, each particle automatically and periodically executes grouping and shuffling operations in its flight learning evo...

Adaptive Particle Swarm Optimization (PSO) variants have become popular in recent years. The main idea of these adaptive PSO variants is that they adaptively change their search behavior during the optimization process based on information gathered during the run. Adaptive PSO variants have shown to be able to solve a wide range of difficult optimization problems efficiently and effectively. In...

Optimization is one of the most important aspects in power system study fields, especially in power system operation, and a verity of models and approaches has been presented in this area. Each model tries to get the best solution as an optimal point, without trapping in local optima. As a powerful tool, modeling based on expert systems and simulation of natural process has an importance and no...

This paper presents a modified Iteration Particle Swarm Optimization (IPSO) algorithm to tune optimal gains of a Proportional Integral Derivative (PID) type multiple stabilizers and non-smooth nonlinear parameters (such as saturation limits) for multi machine power system, simultaneously. The problem of robustly tuning of PID based multiple stabilizer design is formulated as an optimization pro...

Particle swarm optimization (PSO) is a recently developed optimization method that has attracted interest of researchers in various areas. PSO has been shown to be effective in solving a variety of complex optimization problems. With properly chosen parameters, PSO can converge to local optima. However, conventional PSO does not have global convergence. Empirical evidences indicate that the PSO...