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Cognitive Bare Bones Particle Swarm Optimisation with Jumps

Cognitive Bare Bones Particle Swarm Optimisation with Jumps

Mohammad Majid al-Rifaie, Tim Blackwell
Copyright: © 2016 |Volume: 7 |Issue: 1 |Pages: 31
ISSN: 1947-9263|EISSN: 1947-9271|EISBN13: 9781466691568|DOI: 10.4018/IJSIR.2016010101
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MLA

al-Rifaie, Mohammad Majid, and Tim Blackwell. "Cognitive Bare Bones Particle Swarm Optimisation with Jumps." IJSIR vol.7, no.1 2016: pp.1-31. http://doi.org/10.4018/IJSIR.2016010101

APA

al-Rifaie, M. M. & Blackwell, T. (2016). Cognitive Bare Bones Particle Swarm Optimisation with Jumps. International Journal of Swarm Intelligence Research (IJSIR), 7(1), 1-31. http://doi.org/10.4018/IJSIR.2016010101

Chicago

al-Rifaie, Mohammad Majid, and Tim Blackwell. "Cognitive Bare Bones Particle Swarm Optimisation with Jumps," International Journal of Swarm Intelligence Research (IJSIR) 7, no.1: 1-31. http://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.

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