The basic Particle Swarm Optimization (PSO) algorithm and its principle have been introduced, the Particle Swarm Optimization has low accelerate speed and can be easy to fall into local extreme value, so the Particle Swarm Optimization based on the improved inertia weight is presented. This method means using nonlinear decreasing weight factor to change the fundamental ways of PSO. To allow ful...

The particle swarm optimization algorithm was showed to converge rapidly during the initial stages of a global search, but around global optimum, the search process will become very slow. On the contrary, the gradient descending method can achieve faster convergent speed around global optimum, and at the same time, the convergent accuracy can be higher. So in this paper, a hybrid algorithm comb...

This Paper presents a comparative study of Genetic Algorithm method (GA) and Particle swarm optimization (PSO) method to determine the optimal proportional-integral-derivative (PID) controller parameters, for load frequency control in a single area power system. Comparing with conventional Proportional–Integral (PI) method and the proposed PSO the performance of the controller is improved for t...

In this paper, we analyze the behavior of particle swarm optimization (PSO) on the facet of particle interaction. We firstly propose a statistical interpretation of particle swarm optimization in order to capture the stochastic behavior of the entire swarm. Based on the statistical interpretation, we investigate the effect of particle interaction by focusing on the social-only model and derive ...

Many scientific and engineering applications require optimization methods to find more than one solution to multimodal optimization problems. This paper presents a new particle swarm optimization (PSO) technique to locate and refine multiple solutions to such problems. The technique, NichePSO, extends the inherent unimodal nature of the standard PSO approach by growing multiple swarms from an i...

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...

A hybrid particle swarm optimization (PSO) for multi-machine time scheduling problem (MTSP) with multicycles is proposed in this paper to choose the best starting time for each machine in each cycle under pre-described time window and a set of precedence machines for each machine; to minimize the total penalty cost. We developed hybrid algorithm by using a combination between PSO and Genetic Al...

Meta-heuristic search algorithms are developed to solve optimization problems. Such algorithms are appropriate for global searches because of their global exploration and local exploitation abilities. Swarm intelligence (SI) algorithms comprise a branch of meta-heuristic algorithms that imitate the behavior of insects, birds, fishes, and other natural phenomena to find solutions for complex opt...

This paper presents a new approach to particle swarm optimization (PSO) using digital pheremones to coordinate the movements of the swarm within an n-dimensional design space. In traditional PSO, an initial randomly generated population swarm propagates towards the global optimum over a series of iterations. Each particle in the swarm explores the design space based on the information provided ...

Particle Swarm Optimization (PSO) algorithm is a new optimization approach, which has been widely used to solve various and complex optimization problems. However, there are still some imperfections, such as premature convergence and low accuracy. To address such defects, an improved PSO is proposed in this paper. The improved PSO algorithm introduces a uniform search strategy that makes partic...