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This paper presents a new improved algorithm of small-scale and multi-population glowworm swarm optimization (MPGSO).It is shown by simulation that, compared to GSO, the improved algorithm for solving multi-modal functions can not only obviously reduce the computing time, but also improve the computing accuracy.
Aiming at the phenomenon of premature convergence and later period oscillatory occurrences, an adaptive particle swarm optimization algorithm with the changes of the population diversity was proposed. In the algorithm, the adaptive exponent decreasing inertia weight and a dynamic adaptive changing threshold were proposed, the satisfied particle of threshold will be mutation by the average distance...
PSO has a few parameters to adjust such as inertia weight, velocity and constant factors. Among these parameters, inertia weight is very important and has a great potential to develop. During the last decade, various methods like fuzzy, constant, linear methods were proposed to adjust an inertia weight. This paper proposes a new strategy to calculate inertia weight based on decreasing exponential...
A preferable value for parameters proved to be crucial in enhancing the performance and efficiency of particle swarm optimization (PSO) algorithm. To provide good solution for reasonable choice of parameter values within fairly wide range for particle swarm optimization, this paper presents a novel parameter optimizing configuration strategy based on multi-order rhombus thought (MRT), which depends...
In this paper, a flexible tracking particle swarm optimization (FTPSO) for non-stationary optimal solutions is presented. The improved algorithm involves accurately detecting the changes all of the search space and reliably updating obsolete particle memories, which has been shown to be effective in locating a changing extrema. To simulate the dynamic environment, three different types of goal movement...
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