Particle swarm optimization: surfing the waves (1999) [16 citations — 2 self]
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by Ender Ozcan, Selmanipak Cad, Tophanelioglu Sok No, Chilukuri K. Mohan
Proceedings of the Congress on Evolutionary Computation
http://ics.yeditepe.edu.tr/~eozcan/research/../pso/cec99final.ps.Z
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Abstract:
Abstract- A new optimization method has been proposed by Kennedy et. al. in [7, 8], called Particle Swarm Optimization (PSO). This approach combines social psychology principles in socio-cognition of human (and artificial) agents and evolutionary computation. It has been successfully applied to nonlinear function optimization and neural network training. Preliminary formal analyses for a simple PSO system show that a particle in a simple PSO system follows a path defined by a sinusoidal wave, randomly deciding on both its amplitude and frequency [12]. This paper takes the next step, generalizing to obtain closed form equations for trajectories of particles in a multi-dimensional search space. 1
Citations
| 868 | Handbook of Genetic Algorithms – Davis - 1991 |
| 340 | Particle swarm optimization – Kennedy, Eberhart - 1995 |
| 62 | Parameter selection in particle swarm optimization – Shi, Eberhart - 1998 |
| 48 | Evolutionary Optimization versus Particle Swarm Optimization: Philosophy and Performance Difference – Angeline - 1998 |
| 48 | b). Comparison between genetic algorithms and particle swarm optimization – Eberhart, Shi - 1998 |
| 34 | A discrete binary version of the particle swarm algorithm – Kennedy, Eberhart - 1997 |
| 33 | The Particle Swarm: Social Adaptation of Knowledge – Kennedy - 1997 |
| 14 | The behaviour of particle – Kennedy - 1998 |
| 8 | Partial shape matching using genetic algorithms – Ozcan, Mohan - 1997 |
| 7 | Analysis of a Simple Particle Swarm Optimization System – Ozcan, Mohan - 1998 |
