Swarm intelligence
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
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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In recent years a swarm-based optimization methodology called Particle Swarm Optimization (PSO) has developed. If one wants to apply PSO one has to specify several parameters as well as to select a neighborhood topology. Several topologies being widely used can be found in literature. This raises the question, which one fits best to your application at hand. To get rid of this topology selection problem, a new concept called Polymorphic Particle Swarm Optimization (PolyPSO) is proposed. PolyPSO generalizes the standard update rule by a polymorphic update rule. The mathematical expression of this polymorphic update rule can be changed on symbolic level. This polymorphic update rule is an adaptive update rule changing symbols based on accumulative histograms and roulette-wheel sampling. PolyPSO is applied to four typical benchmark functions known from literature. In most cases it outperforms the other PSO variants under consideration. Since PolyPSO performs either as best or second best it can be used as alternative to solve this way the topology selection problem. Additionally, PolyPSO significantly outperforms the standard PSO methods in higher dimensional problems.