Citation

Kheng, Cheng Wai and Ku, Day Chyi and Ng, Hui Fuang and Khattab, Mahmoud and Chong, Siang Yew (2016) Curvature Flight Path for Particle Swarm Optimisation. In: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference - GECCO '16. ACM DL - Digital Library, pp. 29-36. ISBN 978-1-4503-4206-3

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Abstract

An optimisation is a process of finding maxima or minima of the objective function. Particle Swarm Optimisation (PSO) is a nature-inspired, meta-heuristic, black box optimisation algorithm used to search for global minimum or maximum in the solution space. The sampling strategy in this algorithm mimics the flying pattern of a swarm, where each sample is generated randomly according to uniform distribution among three different locations, which marks the current particle location, the individual best found location, and the best found location for the entire swam over all generation. The PSO has known disadvantage of premature convergence in problems with high correlated design variables (high epistatis). However, there is limited research conducted in finding the main reason why the algorithm fails to locate better solutions in these problems. In this paper, we propose to change the traditional triangular flight trajectory of PSO to an elliptical flight path. The new flying method is tested and compared with the traditional triangular flight trajectory of PSO on five high epistatis benchmark problems. Our results show that the samples generated from the elliptical flight path are generally better than the traditional triangular flight trajectory of PSO in term of average fitness and the fitness of best found solution.

Item Type:	Book Section
Uncontrolled Keywords:	Particle Swarm Optimisation, Curvature Flight Path, Geometry, Multi-dimensional Ellipsoid
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	07 Feb 2018 11:18
Last Modified:	07 Feb 2018 11:18
URII:	http://shdl.mmu.edu.my/id/eprint/6664

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