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Cognitive bare bones particle swarm optimisation with jumps

Cognitive bare bones particle swarm optimisation with jumps

Al-Rifaie, Mohammad Majid ORCID: 0000-0002-1798-9615 and Blackwell, Tim (2016) Cognitive bare bones particle swarm optimisation with jumps. International Journal of Swarm Intelligence Research (IJSIR), 7 (1). pp. 1-31. ISSN 1947-9263 (Print), 1947-9271 (Online) (doi:https://doi.org/10.4018/ijsir.2016010101)

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Abstract

The ‘bare bones’ (BB) formulation of particle swarm
optimisation (PSO) was originally advanced as a model of
PSO dynamics. The idea was to model the forces between
particles with sampling from a probability distribution in the
hope of understanding swarm behaviour with a conceptually
simpler particle update rule. ‘Bare bones with jumps’ (BBJ)
proposes three significant extensions to the BB algorithm: (i)
two social neighbourhoods, (ii) a tuneable parameter that can
advantageously bring the swarm to the ‘edge of collapse’ and
(iii) a component-by-component probabilistic jump to anywhere
in the search space. The purpose of this paper is to investigate
the role of jumping within a specific BBJ algorithm, cognitive
BBJ (cBBJ). After confirming the effectiveness of cBBJ, this
paper finds that: jumping in one component only is optimal
over the 30 dimensional benchmarks of this study; that a small
per particle jump probability of 1/30 works well for these
benchmarks; jumps are chiefly beneficial during the early stages
of optimisation and finally this work supplies evidence that
jumping provides escape from regions surrounding sub-optimal
minima.

Item Type: Article
Uncontrolled Keywords: Particle Swarm Optimisation, Bare Bones PSO, Global Optimization, Optimisation, Swarm Intelligence
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Faculty of Liberal Arts & Sciences
Related URLs:
Last Modified: 29 Jun 2021 12:40
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/20994

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