Dynamic Search With Charged Swarms
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Tracking Extrema in Dynamic Environments
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
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
Don't push me! Collision-avoiding swarms
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A Hooke-Jeeves Based Memetic Algorithm for Solving Dynamic Optimisation Problems
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Multi-swarm particle swarm optimiser with Cauchy mutation for dynamic optimisation problems
International Journal of Innovative Computing and Applications
Compound particle swarm optimization in dynamic environments
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
An analysis of particle properties on a multi-swarm PSO for dynamic optimization problems
CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
Tracking multiple targets with adaptive swarm optimization
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Information Sciences: an International Journal
A modified particle swarm optimizer for tracking dynamic systems
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Adaptive swarm optimization for locating and tracking multiple targets
Applied Soft Computing
Performance of bacterial foraging optimization in dynamic environments
ANTS'12 Proceedings of the 8th international conference on Swarm Intelligence
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Charged particle swarm optimization (CPSO) is well suited to the dynamic search problem since inter-particle repulsion maintains population diversity and good tracking can be achieved with a simple algorithm. This work extends the application of CPSO to the dynamic problem by considering a bi-modal parabolic environment of high spatial and temporal severity. Two types of charged swarms and an adapted neutral swarm are compared for a number of different dynamic environments which include extreme 'needle-inthe-haystack' cases. The results suggest that charged swarms perform best in the extreme cases, but neutral swarms are better optimizers in milder environments.