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Comparative performance and scaling of the pareto improving particle swarm optimization algorithm

Published: 06 July 2018 Publication History

Abstract

The Pareto Improving Particle Swarm Optimization algorithm (PI-PSO) has been shown to perform better than Global Best PSO on a variety of benchmark problems. However, these experiments used benchmark problems with a single dimension, namely 32d. Here we compare global best PSO and PI-PSO on benchmark problems of varying dimensions and with varying numbers of particles. The experiments show that PI-PSO generally achieves better performance than PSO as the number of dimensions increases. PI-PSO also outperforms PSO on problems with the same dimension but with the same or fewer particles.

References

[1]
Stephyn G. W. Butcher, John W. Sheppard, and Shane Strasser. 2018. Pareto Improving Selection of the Global Best in Particle Swarm Optimization. In 2018 IEEE Congress on Evolutionary Computation (CEC). forthcoming.
[2]
AP Engelbrecht. 2014. Fitness Function Evaluations: A Fair Stopping Condition?. In Proceedings of the IEEE Swarm Intelligence Symposium (SIS). 1--8.
[3]
Mitchell A Potter and Kenneth A De Jong. 1994. A cooperative coevolutionary approach to function optimization. In Parallel Problem Solving from Nature---PPSN III. Springer, 249--257.
[4]
Francisco J Solis and Roger J-B Wets. 1981. Minimization by random search techniques. Mathematics of operations research 6, 1 (1981), 19--30.
[5]
Shane Strasser, Nathan Fortier, John Sheppard, and Rollie Goodman. 2017. Factored Evolutionary Algorithms. IEEE Transactions on Evolutionary Computation 21, 3 (2017), 281--293.
[6]
Frans Van Den Bergh and Andries P Engelbrecht. 2001. Training product unit networks using cooperative particle swarm optimisers. In Proceedings of International Joint Conference on Neural Networks, Vol. 1. IEEE, 126--131.
[7]
David H Wolpert and William G Macready. 1997. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1, 1 (1997), 67--82.

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cover image ACM Conferences
GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion
July 2018
1968 pages
ISBN:9781450357647
DOI:10.1145/3205651
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 July 2018

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Author Tags

  1. curse of dimensionality
  2. hitchhiking
  3. pareto improvement
  4. particle swarm optimization

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