A particle swarm minimization algorithm with enhanced hill climbing capability

A particle swarm minimization algorithm with enhanced hill climbing capability

We propose a particle swarm minimization algorithm with enhanced hill climbing capability. In the algorithm, an inferior solution is accepted as a new local best if the current cost function value is lower than that of the previous iteration. Numerical results are presented for a popular test set an...

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Veröffentlicht in:South African journal of science 2006-11, Vol.102 (11), p.543-547
Hauptverfasser: Wood, Derren W., Kok, Schalk, Groenwold, Albert A.
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container_title South African journal of science
container_volume 102
creator Wood, Derren W.
Kok, Schalk
Groenwold, Albert A.
description We propose a particle swarm minimization algorithm with enhanced hill climbing capability. In the algorithm, an inferior solution is accepted as a new local best if the current cost function value is lower than that of the previous iteration. Numerical results are presented for a popular test set and two practical global optimization problems, which illustrate that the proposed algorithm may outperform the classical particle swarm algorithm for certain classes of problems.
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title A particle swarm minimization algorithm with enhanced hill climbing capability
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