In this paper, a recurrent functional neural fuzzy network (RFNFN) with symbiotic particle swarm optimization (SPSO) is proposed for solving identification and prediction problems. The proposed RFNFN model has feedback connections added in the membership function layer that can solve temporal problems. Moreover, an efficient learning algorithm, called symbiotic particle swarm optimization (SPSO...

Electric power line overhaul plan is an important issue on power system and engineering practice. As particle swarm optimization is to be a new intelligent algorithm. It is gradually applied into power system these years. This paper provides a relative mathematical model to solve the problems in power line overhaul. Particle swarm optimization algorithm has advantages of less parameters setting...

This paper presents a new optimization model – EPSO, Evolutionary Particle Swarm Optimization, inspired in both Evolutionary Algorithms and in Particle Swarm Optimization algorithms. The fundamentals of the method are described, and an application to the problem of Loss minimization and Voltage control is presented, with very good results.

This paper presents a new variant of Particle Swarm Optimization algorithm named QPSO for solving global optimization problems. QPSO is an integrated algorithm making use of a newly defined, multiparent, quadratic crossover operator in the Basic Particle Swarm Optimization (BPSO) algorithm. The comparisons of numerical results show that QPSO outperforms BPSO algorithm in all the twelve cases ta...

Two modern optimization methods including Particle Swarm Optimization and Differential Evolution are compared on twelve constrained nonlinear test functions. Generally, the results show that Differential Evolution is better than Particle Swarm Optimization in terms of high-quality solutions, running time and robustness.

Particle swarm optimization comes under lot of changes after James Kennedy and Russell Eberhart first proposes the idea in 1995. The changes has been done mainly on Inertia parameters in velocity updating equation so that the convergence rate will be higher. We are proposing a novel approach where particle’s movement will not be depend on its velocity rather it will be decided by constrained bi...

The biologically inspired world comprising of social insect metaphor for solving out wide range of dilemma has become potentially promising area in most recent duration focusing on indirect or direct coordination’s among diverse artificial agents. Swarm [8] apparently is a disorganized collection / population of moving individual that tends to cluster together while each individual seems to be ...

Presented a new hybrid particle swarm algorithm based on P systems, through analyzing the working principle and improved strategy of the elementary particle swarm algorithm. Used the particles algorithm combined with the membrane to form a community, particles use wheel-type structure to communicate the current best particle within the community. The best particles, as Representative, compete f...

In this work Artificial Intelligence (AI) techniques like Fuzzy logic, Genetic Algorithms and Particle Swarm Optimization have been used to improve the performance of the Automatic Generation Control (AGC) system. Instead of applying Genetic Algorithms and Particle swarm optimization independently for optimizing the parameters of the conventional AGC with PI controller, an intelligent tuned Fuz...