CISUC - Geometric Particle Swarm Optimisation
CISUC

Geometric Particle Swarm Optimisation

Authors

Abstract

Using a geometric framework for the interpretation of
crossover of recent introduction, we show an intimate connection between
particle swarm optimisation (PSO) and evolutionary algorithms.
This connection enables us to generalise PSO to virtually any solution
representation in a natural and straightforward way. The new Geometric
PSO (GPSO) applies naturally to both continuous and combinatorial
spaces. We demonstrate this for the cases of Euclidean, Manhattan
and Hamming spaces and report extensive experimental results. We
also demonstrate the applicability of GPSO to more challenging combinatorial
spaces. The Sudoku puzzle is a perfect candidate to test new
algorithmic ideas because it is entertaining and instructive as well as a
non-trivial constrained combinatorial problem. We apply GPSO to solve
the Sudoku puzzle.

Subject

Particle Swarm Optimization

Journal

Journal of Artificial Evolution and Applications, Hindawi, January 2008

Cited by

Year 2010 : 3 citations

 Sergio Consoli, José Andrés Moreno-Pérez, Kenneth Darby-Dowman, Nenad Mladenovi? (2010).Discrete Particle Swarm Optimization for the minimum labelling Steiner tree problem. Journal of Natural Computing, Vol 9, Number 1, pp. 29-46, Springer, 2010.

 S Narmadha, V Selladurai, G Sathish (2010). Efficient Inventory Optimization of Multi Product, Multiple Suppliers with Lead Time using PSO, Arxiv preprint arXiv:1002.2196, 2010.

 Mohammed El-Abda, Hassan Hassanb, Mohab Anisa, Mohamed S. Kamela and Mohamed Elmasrya (2010). Discrete cooperative particle swarm optimization for FPGA placement. Applied Soft Computing, Volume 10, Issue 1, January 2010, pp. 284-295, Elsevier.