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Social interaction in particle swarm optimization, the ranked FIPS, and adaptive multi-swarms

Published: 12 July 2008 Publication History

Abstract

The interaction among particles is a vital aspect of Particle Swarm Optimization. As such, it has a strong influence on the swarm's success. In this study various approaches regarding the particles' communication behavior and their relationship are examined, as well as possibilities to combine the approaches. A new variant of the popular FIPS algorithm, the so-called Ranked FIPS, is introduced, which resolves specific shortcomings of the traditional FIPS. As all tested PSO variants feature distinct strengths and weaknesses, a new adaptive strategy is proposed which operates on dissimiliarly configured subswarms. The exchange between these subswarms is solely based on particle migration. The combination of the Ranked FIPS and other strategies within the so called Particle Swarm Optimizer with Migration achieves a very good, yet remarkably reliable performance over a wide range of recognized benchmark problems.

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Published In

cover image ACM Conferences
GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
July 2008
1814 pages
ISBN:9781605581309
DOI:10.1145/1389095
  • Conference Chair:
  • Conor Ryan,
  • Editor:
  • Maarten Keijzer
Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 12 July 2008

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Author Tags

  1. adaptive optimization
  2. particle swarm optimization
  3. performance analysis
  4. social interaction
  5. subswarms

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Cited By

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  • (2022) Exposing the grey wolf, moth‐flame, whale, firefly, bat, and antlion algorithms: six misleading optimization techniques inspired by bestial metaphors International Transactions in Operational Research10.1111/itor.1317630:6(2945-2971)Online publication date: 26-Jul-2022
  • (2022)PSO-X: A Component-Based Framework for the Automatic Design of Particle Swarm Optimization AlgorithmsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2021.310286326:3(402-416)Online publication date: Jun-2022
  • (2020)A Competitive Mechanism Multi-Objective Particle Swarm Optimization Algorithm and Its Application to Signalized Traffic ProblemCybernetics and Systems10.1080/01969722.2020.1827795(1-32)Online publication date: 12-Oct-2020
  • (2019)Hierarchical Model of Parallel Metaheuristic Optimization AlgorithmsProcedia Computer Science10.1016/j.procs.2019.02.075150(441-449)Online publication date: 2019
  • (2018)A Novel Multi-Epoch Particle Swarm Optimization TechniqueCybernetics and Information Technologies10.2478/cait-2018-003918:3(62-74)Online publication date: 19-Sep-2018
  • (2017)Global-best brain storm optimization algorithmSwarm and Evolutionary Computation10.1016/j.swevo.2017.05.00137(27-44)Online publication date: Dec-2017
  • (2016)Particle Swarm Optimization With Interswarm Interactive Learning StrategyIEEE Transactions on Cybernetics10.1109/TCYB.2015.247415346:10(2238-2251)Online publication date: Oct-2016
  • (2016)Particle SwarmsMetaheuristics10.1007/978-3-319-45403-0_8(203-228)Online publication date: 25-Dec-2016
  • (2015)Operator fission for load balancing in distributed heterogeneous data stream processing systemsProceedings of the 9th ACM International Conference on Distributed Event-Based Systems10.1145/2675743.2776769(332-335)Online publication date: 24-Jun-2015
  • (2014)Intelligent control technique for autonomous collective robotics systems2014 IEEE International Conference on Computational Intelligence and Computing Research10.1109/ICCIC.2014.7238502(1-8)Online publication date: Dec-2014
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