In this paper, an effective combination of two Metaheuristic algorithms, namely Invasive Weed Optimization and the Particle Swarm Optimization, has been proposed. This hybridization called as HIWOPSO, consists of two main phases of Invasive Weed Optimization (IWO) and Particle Swarm Optimization (PSO). Invasive weed optimization is the natureinspired algorithm which is inspired by colonial beha...

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...

Particle Swarm Optimization (PSO) with elite PSO parameters has been developed for power flow analysis under practical constrained situations. Multiple solutions of the power flow problem are useful in voltage stability assessment of power system. A method of determination of multiple power flow solutions is presented using a hybrid of Particle Swarm Optimization (PSO) and local search techniqu...

The Darwinian Particle Swarm Optimization (DPSO) is an evolutionary algorithm that extends the well-known Particle Swarm Optimization (PSO) using natural selection, or survival-of-the-fittest, to enhance the ability to escape from local optima. In this paper, it is explored the effectiveness of using a modified version of both PSO and DPSO, respectively named as R-PSO and R-DPSO, on groups of s...

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...

The parts optimization are very important for scroll compressor design. According to existing problems of current optimization algorithm and actual optimization problems, the improved optimization algorithm—genetic-particle swarm optimization (GA-PSO) is proposed for scroll plate optimization. The optimization method integrates crossover of genetic algorithm (GA) and evolutionary mechanism of p...

This paper presents an optimal design of linear phase digital low pass finite impulse response (FIR) filter using Novel Particle Swarm Optimization (NPSO). NPSO is an improved particle swarm optimization (PSO) that proposes a new definition for the velocity vector and swarm updating and hence the solution quality is improved. The inertia weight has been modified in the PSO to enhance its search...

This paper presents particle swarm optimization (PSO) method to find the prime factors of a composite number. Integer factorization is a well known NP hard problem and security of many cryptosystem is based on difficulty of integer factorization. A particle swarm optimization algorithm for integer factorization has been devised and tested on different 100 numbers. It has been found that the PSO...

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...

This paper proposes an improved particle swarm optimization (PSO). In order to increase the efficiency, suggestions on parameter settings is made and a mechanism is designed to prevent particles fall into the local optimal. To evaluate its effectiveness and efficiency, this approach is applied to multimodal function optimizing tasks. 16 benchmark functions were tested, and results were compared...