Swarm intelligence
Adaptive particle swarm optimization: detection and response to dynamic systems
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Swarms in dynamic environments
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
A diversity-guided particle swarm optimizer for dynamic environments
LSMS'07 Proceedings of the Life system modeling and simulation 2007 international conference on Bio-Inspired computational intelligence and applications
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The paper proposes a modified particle swarm optimizer for tracking dynamic systems. In the new algorithm, the changed local optimum and global optimum are introduced to guide the movement of each particle and avoid making direction and velocity decisions on the basis of the outdated information. An environment influence factor is put forward based on the two optimums above, which dynamically decide the change of the inertia weight. The combinations of the different local optimum update strategy and local inertia weight update strategy are tested on the parabolic benchmark function. The results on the benchmark function with various severities suggest that modified particle swarm optimizer performs better in convergence speed and aggregation accuracy.