PROJECT TITLE :
A Study of Collapse in Bare Bones Particle Swarm Optimization
The dynamic update rule of particle swarm optimization is formulated as a second-order stochastic difference equation and general relations are derived for search focus, search spread, and swarm stability at stagnation. The relations are applied to a few explicit particle swarm optimization (PSO) implementations, the quality PSO of Clerc and Kennedy, a PSO with discrete recombination, and also the Bare Bones swarm. The simplicity of the Bare Bones swarm facilitates theoretical analysis and a more no-collapse condition is derived. A series of experimental trials confirms that Bare Bones situated at the edge of collapse is reminiscent of alternative PSOs, and that performance will be still any improved with the utilization of an adaptive distribution. It's conjectured that, subject to unfold, stability and no-collapse, there's a single encompassing particle swarm paradigm, and that an vital side of parameter tuning at intervals any particular manifestation is to get rid of any deleterious behavior that ensues from the dynamics.
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