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This is an Algorithm for Global Optimization (GO) of objective functions. Belonging to the class of stochastic evolutionary global optimizers, it is a variant of Particle Swarm Optimization (PSO).

There are several different realizations of RPSO. Common to all realizations is the repulsion between particles. This can prevent the swarm beeing trapped in local minima, which would cause a premature convergence and would lead the optimization algorithm to fail to find the global optimum.

In RPSO1 the future velocity of a particle at position with a recent velocity is calculated by

where

• : random numbers
• : inertia weight
• : best position of a particle
• : best position of a randomly chosen other particle from within the swarm
• : a random velocity vector
• : constants

The main difference between PSO and RPSO is the propagation mechanism to determine new positions for a particle in the search space. RPSO is capable to find global optima in more complex search spaces. On the other hand, compared to PSO it may be slower on certain types of optimization problems.

See Also:

• Genetic algorithm
• Swarm Intelligence

External Links

• Particle Swarm Optimization toolbox An open source pso toolbox written in Matlab. (sourceforge home for the project)
• Particle Swarm Central

--22:36, 27 Aug 2004 (UTC)

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RPSORPSO stands for R epulsive P article S warm O ptimization. This is an Algorithm for Global Optimization (GO) of objective functions. Belonging to the class of stochastic evolutionary global optimizers, it is a variant of Particle Swarm Optimization (PSO).