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In this paper, we present a new particle swarm optimization which is inspired by the migration of birds. When we contemplate the migration of the group of birds, there is always a leader bird existed in order to make their fly easier and reduce the whole resistance of air for its own formation of migration. We create a leader particle from choosing the global best position to make all the particles...
The particle swarm optimization is one of well known algorithms in the world with its performance and easy implementation. This algorithm is used for finding optimal values or regions of multi-dimension spaces throughout the interaction of each particle positions and its values. Originally, the PSO has two factors such as position and velocity vectors which are sources of next positions for particles,...
In this paper, we present a new particle swarm optimization. The original PSO has a weight term which is decreasing, increasing, or constant during iterations. In this paper, inertia terms are a vector instead of a scalar. Comparing a velocity and an updating term, the weight can be increased or decreased. That is, if the absolute value of velocity is larger or lesss than that of the update term,...
In this paper, we present PID controller design methods for automatic voltage regulators. We use three improved particle swarm optimization for PID controllers with which the step response is optimally regulated for automatic voltage controllers. We compare three different versions of particle swarm optimizations, i.e., the modified original PSO, the crazy PSO and the chaotic PSO. Among three PSOs,...
In this paper, we present a new third order particle swarm optimization. The original PSO has position and velocity vectors. However, the proposed algorithm has three vectors: i.e. a position vector, a velocity vector and an acceleration vector. From the proposed PSO, we obtain the third order difference equation and from the equation we obtain the convergence region for four parameters. By setting...
The particle swarm optimization is one of well known algorithms in the world with its performance and easy implementation. This algorithm is used for finding optimal values or regions of multi-dimensional spaces throughout the interaction of each particle positions and its values. Originally, the PSO has two factors such as position and velocity vectors which are sources for next positions of particles,...
In this paper, we present a new particle swarm optimization. We call it a fourth order particle swarm optimization. The original PSO has position and velocity vectors. However, the proposed algorithm has four terms: i.e. a position vector, a velocity vector, an acceleration vector and a jerk vector. From the proposed PSO, we obtain the fourth order difference equation and from the equation we obtain...
In this paper, we present a new particle swarm optimization. When we consider the convergence region for the original PSO during some iterations, the poles of the difference equation of the PSO system are located outside the convergence region. A proposed method has a strict condition that the poles of system always does not lie outside the convergence region. To satisfy the condition, the inertia...
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