Modification of the particle swarm optimizer for locating all the global minima
KE Parsopoulos, MN Vrahatis - … and Genetic Algorithms: Proceedings of the …, 2001 - Springer
Artificial Neural Nets and Genetic Algorithms: Proceedings of the …, 2001•Springer
In many optimization applications, escaping from the local minima as well as computing all
the global minima of an objective function is of vital importance. In this paper the Particle
Swarm Optimization method is modified in order to locate and evaluate all the global minima
of an objective function. The new approach separates the swarm properly when a candidate
minimizer is detected. This technique can also be used for escaping from the local minima
which is very important in neural network training.
the global minima of an objective function is of vital importance. In this paper the Particle
Swarm Optimization method is modified in order to locate and evaluate all the global minima
of an objective function. The new approach separates the swarm properly when a candidate
minimizer is detected. This technique can also be used for escaping from the local minima
which is very important in neural network training.
Abstract
In many optimization applications, escaping from the local minima as well as computing all the global minima of an objective function is of vital importance. In this paper the Particle Swarm Optimization method is modified in order to locate and evaluate all the global minima of an objective function. The new approach separates the swarm properly when a candidate minimizer is detected. This technique can also be used for escaping from the local minima which is very important in neural network training.
Springer
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