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abstract

Steep gradients as a predictor of PSO failure

Published: 06 July 2013 Publication History

Abstract

There are many features of optimisation problems that can influence the difficulty for search algorithms. This paper investigates the steepness of gradients in a fitness landscape as an additional feature that can be linked to difficulty for particle swarm optimisation (PSO) algorithms. The performances of different variations of PSO algorithms on a range of benchmark problems are considered against average estimations of gradients based on random walks. Results show that all variations of PSO failed to solve problems with estimated steep gradients in higher dimensions.

References

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R. Eberhart and J. Kennedy. A New Optimizer using Particle Swarm Theory. In Proceedings of the Sixth International Symposium on Micromachine and Human Science, pages 39--43, 1995.
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J. Kennedy. The Particle Swarm: Social Adaptation of Knowledge. In Proceedings of the IEEE International Conference on Evolutionary Computation, pages 303--308, April 1997.
[3]
J. Kennedy. Bare bones particle swarms. In Swarm Intelligence Symposium, 2003. SIS '03. Proceedings of the 2003 IEEE, pages 80--87, 2003.
[4]
K. M. Malan and A. P. Engelbrecht. Fitness Landscape Analysis for Metaheuristic Performance Prediction. In H. Richter and A. P. Engelbrecht, editors, Recent advances in the theory and application of fitness landscapes, Emergence, Complexity and Computation, page To appear. Springer, 2013.
[5]
B. Xin, J. Chen, and F. Pan. Problem difficulty analysis for particle swarm optimization: deception and modality. In GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation, pages 623--630, New York, NY, USA, 2009. ACM.

Cited By

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  • (2024)Fundamental Tradeoffs Between Exploration and Exploitation Search MechanismsInto a Deeper Understanding of Evolutionary Computing: Exploration, Exploitation, and Parameter Control10.1007/978-3-031-74013-8_2(101-199)Online publication date: 12-Nov-2024
  • (2023)A Framework for Meta-heuristic Parameter Performance Prediction Using Fitness Landscape Analysis and Machine Learning2023 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC53210.2023.10254195(1-8)Online publication date: 1-Jul-2023
  • (2023)Keenness for characterizing continuous optimization problems and predicting differential evolution algorithm performanceComplex & Intelligent Systems10.1007/s40747-023-01005-79:5(5251-5266)Online publication date: 16-Mar-2023
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Published In

cover image ACM Conferences
GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
July 2013
1798 pages
ISBN:9781450319645
DOI:10.1145/2464576
  • Editor:
  • Christian Blum,
  • General Chair:
  • Enrique Alba
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 2013

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Author Tags

  1. fitness landscape analysis
  2. gradient estimation

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GECCO '13
Sponsor:
GECCO '13: Genetic and Evolutionary Computation Conference
July 6 - 10, 2013
Amsterdam, The Netherlands

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

View all
  • (2024)Fundamental Tradeoffs Between Exploration and Exploitation Search MechanismsInto a Deeper Understanding of Evolutionary Computing: Exploration, Exploitation, and Parameter Control10.1007/978-3-031-74013-8_2(101-199)Online publication date: 12-Nov-2024
  • (2023)A Framework for Meta-heuristic Parameter Performance Prediction Using Fitness Landscape Analysis and Machine Learning2023 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC53210.2023.10254195(1-8)Online publication date: 1-Jul-2023
  • (2023)Keenness for characterizing continuous optimization problems and predicting differential evolution algorithm performanceComplex & Intelligent Systems10.1007/s40747-023-01005-79:5(5251-5266)Online publication date: 16-Mar-2023
  • (2021)Predicting Particle Swarm Optimization Control Parameters From Fitness Landscape Characteristics2021 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC45853.2021.9505006(2289-2298)Online publication date: 28-Jun-2021
  • (2015)Evaluating landscape characteristics of dynamic benchmark functions2015 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2015.7257044(1343-1350)Online publication date: May-2015
  • (2014)A progressive random walk algorithm for sampling continuous fitness landscapes2014 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2014.6900576(2507-2514)Online publication date: Jul-2014
  • (2014)Characterising the searchability of continuous optimisation problems for PSOSwarm Intelligence10.1007/s11721-014-0099-x8:4(275-302)Online publication date: 28-Oct-2014
  • (2014)Fitness Landscape Analysis for Metaheuristic Performance PredictionRecent Advances in the Theory and Application of Fitness Landscapes10.1007/978-3-642-41888-4_4(103-132)Online publication date: 2014

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