Srinivas Pasupuleti
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.
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Index Terms
- The gregarious particle swarm optimizer (G-PSO)
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