SAPPO: A Simple, Adaptive, Predator Prey Optimiser

Authors

Arlindo Silva
Ana Paula Neves F. da Silva
Ernesto Costa

Abstract

The balance of exploration and exploitation in particle swarm
optimisation is closely related to the choice of the algorithm's parame-
ters. Achieving the right balance is essential for the success of a given
optimisation task. This choice is a di±cult task, since for di®erent func-
tions being optimised the ideal parameter sets can also bee very di®erent.
In this paper we try to deal with this issue by introducing two new mech-
anisms in the basic particle swarm optimiser: a predator-prey strategy
to help maintain diversity in the swarm and a symbiosis based adaptive
scheme to allow the co-evolution of the algorithm parameters and the
parameters of the function being optimised.

Subject

Particle Swarm, Optmization

Conference

EPIA\'03, December 2003

Cited by

Year 2009 : 2 citations

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Year 2008 : 1 citations

Weigang Jiang, Yuanbiao Zhang, Jianwen Xie (2008). A Particle Swarm Optimization Algorithm Based on Diffusion-Repulsion and Application to Portfolio Selection. 2008 International Symposium on Information, Science and Engieering, vol. 2, pp.498-501, 2008.

Year 2007 : 1 citations

Mitsuharu Higashitani, Atsushi Ishigame, Keiichiro Yasuda, Pursuit-Escape Particle Swarm Optimization, IEEJ Transactions on Electrical and Electronic Engineering, Volume 3, Number 1, pp. 136-142.

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Year 2005 : 3 citations

Salima Nabti, Souham Meshoul, and Mohamed Batouche, Predator Prey Optimizer for Unsupervised Clustering in Image Segmentation, International Arab Conference on Information Technology, ACIT'2005, December 6th- 8th, 2005, Al-Isra Private University, Jordan.

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