K
Kyle Robert Harrison
Researcher at University of New South Wales
Publications - 38
Citations - 479
Kyle Robert Harrison is an academic researcher from University of New South Wales. The author has contributed to research in topics: Particle swarm optimization & Metaheuristic. The author has an hindex of 11, co-authored 36 publications receiving 347 citations. Previous affiliations of Kyle Robert Harrison include Brock University & University of Ontario Institute of Technology.
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Self-adaptive particle swarm optimization: a review and analysis of convergence
TL;DR: Investigating the convergence behaviours of 18 SAPSO algorithms both analytically and empirically examines whether the adapted parameters reach a stable point and whether the final parameter values adhere to a well-known convergence criterion.
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Inertia weight control strategies for particle swarm optimization: Too much momentum, not enough analysis
TL;DR: An overview of 18 inertia weight control strategies is provided, conditions required for the strategies to exhibit convergent behaviour are derived, and results of the empirical investigation show that none of the examined strategies even perform on par with a constant inertia weight.
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Optimal parameter regions and the time-dependence of control parameter values for the particle swarm optimization algorithm
TL;DR: This paper aims to demonstrate the efforts towards in-situ applicability of EMMARM, as to provide real-time information about concrete mechanical properties such as E-modulus and compressive strength.
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The bi-objective critical node detection problem
TL;DR: It is proved that the proposed bi-objective formulation of the Critical Node Detection Problem is distinct from the CNDP, despite their common motivation, and it is found that of the examined algorithms, NSGAII generally produces the most desirable approximation fronts.
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A parameter-free particle swarm optimization algorithm using performance classifiers
TL;DR: A parameter-free PSO algorithm is proposed, which performs on par with other top-performing PSO variants, namely the three best performing static PSO configurations, particle swarm optimization with time-varying acceleration coefficients (PSO-TVAC), and particle Swarm optimization with improved random constants (PSo-iRC).