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- W2157880518 abstract "This paper presents a gregarious particle swarm optimization algorithm (G-PSO) in which the particles explore the search space by aggressively scouting the local minima with the help of only social knowledge. To avoid premature convergence of the swarm, the particles are re-initialized with a random velocity when stuck at a local minimum. Furthermore, G-PSO adopts a reactive determination of the step size, based on feedback from the last iterations. This is in contrast to the basic particle swarm algorithm, in which the particles explore the search space by using both the individual cognitive component and the social knowledge and no feedback is used for the self-tuning of algorithm parameters. The novel scheme presented, besides generally improving the average optimal values found, reduces the computation effort." @default.
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- W2157880518 date "2006-07-08" @default.
- W2157880518 modified "2023-09-23" @default.
- W2157880518 title "The gregarious particle swarm optimizer (G-PSO)" @default.
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- W2157880518 doi "https://doi.org/10.1145/1143997.1144007" @default.
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