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Volume 61, Issue 4

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Runtime analysis of discrete particle swarm optimization algorithms: A survey

Moritz Mühlenthaler / Alexander RaßORCID iD: https://orcid.org/0000-0003-1274-6398
Published Online: 2019-10-24 | DOI: https://doi.org/10.1515/itit-2019-0009

Abstract

A discrete particle swarm optimization (PSO) algorithm is a randomized search heuristic for discrete optimization problems. A fundamental question about randomized search heuristics is how long it takes, in expectation, until an optimal solution is found. We give an overview of recent developments related to this question for discrete PSO algorithms. In particular, we give a comparison of known upper and lower bounds of expected runtimes and briefly discuss the techniques used to obtain these bounds.

Keywords: randomized search heuristics; particle swarm optimization; runtime analysis

ACM CCS: Theory of computationTheory of randomized search heuristicsTheory of computationBio-inspired optimizationTheory of computationOptimization with randomized search heuristicsMathematics of computingMarkov processesMathematics of computingProbabilistic algorithms

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About the article

Moritz Mühlenthaler

Moritz Mühlenthaler is currently a postdoctoral researcher at the Discrete Optimization group at TU Dortmund University. He received his diploma and doctoral degrees from the University of Erlangen-Nuremberg, Germany, under the supervision of Prof. Rolf Wanka. His research interests include analysis of algorithms, in particular approximation algorithms and randomized search heuristics, as well as combinatorial reconfiguration.

Alexander Raß

Alexander Raß is a doctoral student at the University of Erlangen-Nuremberg, where he already received his Master of Science in Mathematics. His research interests are runtime analysis of algorithms working on discrete domains and convergence analysis of algorithms working on continuous domains. In both cases the focus is on Particle Swarm Optimization (PSO). Additionally he established an open-source project for PSO with very high and adaptive precision.


Received: 2019-02-22

Revised: 2019-09-17

Accepted: 2019-10-10

Published Online: 2019-10-24

Published in Print: 2019-08-27


Citation Information: it - Information Technology, Volume 61, Issue 4, Pages 177–185, ISSN (Online) 2196-7032, ISSN (Print) 1611-2776, DOI: https://doi.org/10.1515/itit-2019-0009.

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