Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Self-adaptive learning based particle swarm optimization
Information Sciences: an International Journal
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Parameter selection and adaptation in Unified Particle Swarm Optimization
Mathematical and Computer Modelling: An International Journal
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Particle swarm optimization (PSO) has been used to solve a wide variety of optimization problems. The basic PSO algorithm contains a number of control parameters, including the inertia weight, w, and the acceleration coefficients, c1 and c2. The PSO, as an optimization algorithm, is ideally suited to optimize its own parameters. This paper proposes that the control parameters of PSO be optimized in a secondary swarm where each position vector component of each particle contains a prospective PSO control parameter (i.e. w, c1 and c2) of the main swarm. This approach relieves the user from specifying appropriate parameters when using PSO. Application of the self-adaptive particle swarm optimizer (SAPSO) to 12 well known test functions shows that SAPSO managed to reach pre-specified values quicker than an adaptive PSO using fitness rank to update the inertia weight.