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In this article we propose the use of fuzzy systems for dynamic adjustment of parameters in the galactic swarm optimization (GSO) method. This algorithm is inspired by the movement of stars, galaxies and superclusters of galaxies under the force of gravity. GSO uses various cycles of exploration and exploitation phases to achieve a trade-off between the exploration of new solutions and exploitation...
In order to overcome the shortage of premature convergence caused by local optimization in the process of global optimization, an adaptive weight Particle Swarm Optimization algorithm with constriction factor is proposed combined with an analysis of convergence of Particle Swarm Optimization algorithm. The value of the inertia weight is set according to dynamic information about the changes in the...
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