Eigenvalue spread criteria in the particle swarm optimization algorithm for solving of constraint parametric problems

This paper presents an alternative and efficient method for solving a class of constraint parametric optimization problems using particle swarm optimization algorithm (PSO). In this paper, for the first time PSO is used for solving convex parametric programming, but PSO must be adaptive for doing it. So, for obtaining particles velocities, adaptation weight and velocity boundaries in the updating of velocities are calculated in the recursive form. Computational complexity of the PSO algorithm is decreased based on uniformity of population of particles, the uniformity of which is obtained using eigenvalue spread of covariance of population. Two simple examples are provided for showing the efficiency of the proposed method. For solving these examples, eigenvalue spread criteria applied in the PSO algorithm decrease 78% of computations.

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