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This evolutionary method is a generation of articulation angles using a particle swarm optimization (PSO).
Core placement has been performed using a Simulated Annealing (SA) based technique with its initial solution generated using a Particle Swarm Optimization (PSO) based approach.
On the basis of the simulation residual stress, the exponentially damped cosine function is fitted using a particle swarm optimization method.
Moreover, the values of BELBIC and PID gains are optimized using a particle swarm optimization (PSO) technique with minimization of Integral Square Error (ISE) for all loops.
To solve this problem, we propose a mixed-integer non-linear optimization model, which we solve using a particle swarm optimization (PSO) algorithm.
In this paper, a decentralized adaptive backstepping excitation controller, tuned using a Particle Swarm Optimization technique (PSO), is designed for stability enhancement of multi-machine power systems.
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In this paper we use a Particle Swarm Optimization (PSO) algorithm to solve the SRFLP.
The developed software uses a particle swarm optimizer and runs a full ac Newton Raphson power flow analysis to evaluate the candidate solutions.
The proposed inverse model uses a particle swarm optimization (PSO) algorithm that is based on the social behavior of animals searching for food sources.
They include an integral equation method, an algorithm available in the MATLAB Optimization Toolbox, and recently developed in-house software that uses a particle swarm optimisation (PSO) approach.
In the presented work the subsystem for creation uses a particle swarm optimization method for the creation/modification of solution sets.
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