In this paper a particle swarm optimization (PSO) algorithm is presented to design a compact stepped triangle shape antenna in order to obtain the proper UWB bandwidth as defined by FCC. By changing the various cavity dimensions of the antenna, data to develop PSO program in MATLAB is achieved. The results obtained from the PSO algorithm are applied to the antenna design to fine-tune the bandwi...

This paper presents an application of particle swarm optimization (PSO) to the grounding grid planning which compares to the application of genetic algorithm (GA). Firstly, based on IEEE Std.80, the cost function of the grounding grid and the constraints of ground potential rise, step voltage and touch voltage are constructed for formulating the optimization problem of grounding grid planning. ...

This article presents a hybrid evolutionary algorithm (PSABC) based on Artificial Bee Colony (ABC) and particle swarm optimization (PSO). Both of the algorithms are co-operative, population-based global search swarm intelligence met heuristics. The core of this algorithm is using PSO to optimize the fitness value of population in ABC. For Evaluation purpose, the proposed algorithm is tested on ...

Multilevel thresholding is one of the most popular image segmentation techniques. This paper presents a new multilevel maximum entropy thresholding method based on modified seeker optimization (MSO) algorithm. In the proposed method the thresholding problem is treated as an optimization problem and solved by using the MSO metaheuristics. Particle swarm optimization (PSO) algorithm is also imple...

How to keep a balance between exploitation and exploration in Particle Swarm Optimization (PSO) for efficiently solving various optimization problems is an important issue. In order to handle premature convergence in PSO search, this paper proposes a novel algorithm, called Particle Swarm Optimization with Diversive Curiosity (PSO/DC), that introduces a mechanism of diversive curiosity into PSO...

In this paper, a hybrid particle swarm optimization algorithm (HPSOM) that uses the mutation process to improve the standard particle swarm optimization (PSO) algorithm is presented. The main idea of the HPSOM is to integrate the PSO with genetic algorithm mutation method. As a result, the proposed algorithm has the automatic balance ability between global and local searching abilities. The val...

In light of the accuracy of particle swarm optimization-particle filter (PSO-PF) inadequate for multi-robot cooperative positioning, the paper presents population density particle swarm optimization-particle filter (PDPSO-PF), which draws cooperative coevolutionary algorithm in ecology into particle swarm optimization. By taking full account of the competitive relationship between the environme...

PSO is a robust and stochastic optimization technique based on the movement and the intelligence of the swarms. PSO is inspired with social behavior of bird flocking and fish schooling. PSO algorithm is applied to solve the real world complex optimization problems. So many real world optimization problems are solved by using different combinations and variations of PSO algorithms. In this paper...

Two important topics in Particle Swarm Optimization (PSO) research filed are trajectory analysis of particles and parameter selection method. Trajectory analysis is important because it can help to determine where the position of each particle is at each evolutionary step, and consequently it can help to clarify the running mechanism of PSO algorithm, so as to explain why and when PSO algorithm...

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...