Niching methods for genetic algorithms
Niching methods for genetic algorithms
Elements of applied bifurcation theory (2nd ed.)
Elements of applied bifurcation theory (2nd ed.)
Numerical methods for bifurcations of dynamical equilibria
Numerical methods for bifurcations of dynamical equilibria
Swarm intelligence
A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
Particle swarm with speciation and adaptation in a dynamic environment
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Multimodal Optimization by Decomposition of the Search Space in Regions
ISDA '07 Proceedings of the Seventh International Conference on Intelligent Systems Design and Applications
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
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A dynamic system is represented as a set of equations that specify how variables change over time. The equations in the system specify how to compute the new values of the state variables as a function of their current values and the values of the control parameters. If those parameters change beyond certain values, the system exhibits qualitative changes in its behavior. Those qualitative changes are called bifurcations, and the values for the parameters where those changes occur are called bifurcation points. In this contribution, we present an application of particle swarm optimization methods for dynamic environments for plotting bifurcation diagrams used in the analysis of dynamical systems. The use of particle swarm optimization methods presents various advantages over traditional methods.