S. Baswana, S. Biswas, B. Doerr, T. Friedrich, P. P. Kurur, and F. Neumann. Computing single source shortest paths using single-objective fitness. In Proc. of the 10th ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA), pages 59–66, 2009.Google Scholar
M. Clerc. Discrete particle swarm optimization, illustrated by the Traveling Salesman Problem. In New Optimization Techniques in Engineering, chapter 8, pages 219–239, 2004.Google Scholar
T. H. Cormen, C. E. Leiserson, and R. L. Rivest. Introduction to Algorithms. MIT Press, McGraw-Hill, 1990.Google Scholar
E. F. G. Goldbarg, G. R. de Souza, and M. C. Goldbarg. Particle swarm for the traveling salesman problem. In European Conference on Evolutionary Computation in Combinatorial Optimization, pages 99–110, Springer, 2006.Google Scholar
W. J. Gutjahr. Ant Colony Optimization: Recent Developments in Theoretical Analysis, pages 225–254, 2011.
C. H. Papadimitriou and K. Steiglitz. Combinatorial Optimization: Algorithms and Complexity. Englewood Cliffs, N. J.: Prentice Hall, 1982.Google Scholar
S. Helwig and R. Wanka. Theoretical analysis of initial particle swarm behavior. In Proc. 10th Int. Conf. on Parallel Problem Solving from Nature (PPSN), pages 889–898, 2008.Google Scholar
M. Hoffmann, M. Mühlenthaler, S. Helwig, and R. Wanka. Discrete particle swarm optimization for TSP: Theoretical results and experimental evaluations. In Proc. 2nd Int. Conf. on Adaptive and Intelligent Systems (ICAIS), pages 416–427, 2011.Google Scholar
J. Kennedy and R. C. Eberhart. Particle swarm optimization. In Proc. IEEE International Conference on Neural Networks, volume 4, pages 1942–1948, 1995.Google Scholar
J. Kennedy and R. C. Eberhart. A discrete binary version of the particle swarm algorithm. In Proc. IEEE Int. Conf. on Systems, Man, and Cybernetics, volume 5, pages 4104–4108, 1997.Google Scholar
M. Mühlenthaler, A. Raß, M. Schmitt, A. Siegling, and R. Wanka. Runtime analysis of a discrete particle swarm optimization algorithm on Sorting and OneMax. In Proc. 14th ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA), pages 13–24, 2017.Google Scholar
M. Mühlenthaler, A. Raß, M. Schmitt, and R. Wanka. Exact Markov chain-based runtime analysis of a discrete particle swarm optimization algorithm on Sorting and OneMax. arXiv:1902.01810, 2019. Extended version of .Google Scholar
A. Raß, J. Schreiner, and R. Wanka. Runtime analysis of discrete particle swarm optimization applied to shortest paths computation. In Proc. 19th Evolutionary Computation in Combinatorial Optimization (EvoCOP), Springer International Publishing, 2019.Google Scholar
J. Scharnow, K. Tinnefeld, and I. Wegener. The analysis of evolutionary algorithms on sorting and shortest paths problems. Journal of Mathematical Modelling and Algorithms, 3(4):349–366, 2004.CrossrefGoogle Scholar
X. H. Shi, Y. C. Liang, H. P. Leeb, C. Lu, and Q. X. Wang. Particle swarm optimization-based algorithms for TSP and generalized TSP. Information Processing Letters, (103):169–176, 2007.Web of ScienceCrossrefGoogle Scholar
K.-P. Wang, L. Huang, C.-G. Zhou, and W. Pang. Particle swarm optimization for traveling salesman problem. In Machine Learning and Cybernetics, 2003 International Conference on, volume 3, pages 1583–1585, IEEE, 2003.Google Scholar
I. Wegener. Methods for the analysis of evolutionary algorithms on pseudo-boolean functions. In R. Sarker, M. Mohammadian, and X. Yao, editors, Evolutionary Optimization, chapter 14, pages 349–369, Springer, 2002.Google Scholar
About the article
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 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.
Published Online: 2019-10-24
Published in Print: 2019-08-27