Article

Adaptive diversity in PSO

Authors:

Christopher K. Monson,

Kevin D. SeppiAuthors Info & Claims

GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation

Pages 59 - 66

https://doi.org/10.1145/1143997.1144006

Published: 08 July 2006 Publication History

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Abstract

Spatial Extension PSO (SEPSO) and Attractive-Repulsive PSO (ARPSO) are methods for artificial injection of diversity into particle swarm optimizers that are intended to encourage converged swarms to engage in exploration. While simple to implement, effective when tuned correctly, and benefiting from intuitive appeal, SEPSO behavior can be improved by adapting its radius and bounce parameters in response to collisions. In fact, adaptation can allow SEPSO to compete with and outperform ARPSO. The adaptation strategies presented here are simple to implement, easy to tune, and retain SEPSO's intuitive appeal.

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Pluhacek MViktorin AKadavy TKovac JSenkerik RLi XHandl J(2024)Measuring Population Diversity in Variable Dimension Search SpacesProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664170(1511-1519)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3664170
Patnaik SPanda A(2023)Power quality enhancement using a novel SAPF control scheme employing high selectivity filterInternational Journal of Emerging Electric Power Systems10.1515/ijeeps-2022-038025:3(367-381)Online publication date: 26-May-2023
https://doi.org/10.1515/ijeeps-2022-0380
Zhang YZhao KRen GZhu M(2022) A node positioning method for IoT based on LSSVR and optimized particle swarm algorithm Internet Technology Letters10.1002/itl2.3515:3Online publication date: 17-Feb-2022
https://doi.org/10.1002/itl2.351
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Index Terms

Adaptive diversity in PSO
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization

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GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation

July 2006

2004 pages

ISBN:1595931864

DOI:10.1145/1143997

General Chair:
Mike Cattolico
Tiger Mountain Scientific, Inc., USA

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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Association for Computing Machinery

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Published: 08 July 2006

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July 8 - 12, 2006

Washington, Seattle, USA

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GECCO '06 Paper Acceptance Rate 205 of 446 submissions, 46%;

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

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Pluhacek MViktorin AKadavy TKovac JSenkerik RLi XHandl J(2024)Measuring Population Diversity in Variable Dimension Search SpacesProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664170(1511-1519)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3664170
Patnaik SPanda A(2023)Power quality enhancement using a novel SAPF control scheme employing high selectivity filterInternational Journal of Emerging Electric Power Systems10.1515/ijeeps-2022-038025:3(367-381)Online publication date: 26-May-2023
https://doi.org/10.1515/ijeeps-2022-0380
Zhang YZhao KRen GZhu M(2022) A node positioning method for IoT based on LSSVR and optimized particle swarm algorithm Internet Technology Letters10.1002/itl2.3515:3Online publication date: 17-Feb-2022
https://doi.org/10.1002/itl2.351
Yuan ZLi JYang HZhang B(2021)A Hybrid Whale Optimization and Particle Swarm Optimization Algorithm2021 IEEE International Conference on Progress in Informatics and Computing (PIC)10.1109/PIC53636.2021.9687017(260-264)Online publication date: 17-Dec-2021
https://doi.org/10.1109/PIC53636.2021.9687017
Basílio SLemonge ACarvalho É(2021)A Population Size Dynamic Reduction Criterion in PSO Algorithms2021 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC45853.2021.9504791(1349-1356)Online publication date: 28-Jun-2021
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Liu ZHan FLing Q(2019)A novel particle swarm optimisation with mutation breedingConnection Science10.1080/09540091.2019.170091132:4(333-361)Online publication date: 12-Dec-2019
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Olivas FAmador-Angulo LPerez JCaraveo CValdez FCastillo O(2017)Comparative Study of Type-2 Fuzzy Particle Swarm, Bee Colony and Bat Algorithms in Optimization of Fuzzy ControllersAlgorithms10.3390/a1003010110:3(101)Online publication date: 28-Aug-2017
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Liu HHan F(2017)A Modified Standard PSO-2011 with Robust Search AbilityBio-inspired Computing: Theories and Applications10.1007/978-981-10-7179-9_16(207-222)Online publication date: 9-Nov-2017
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Aziz MTaib MAdnan R(2016)Moment of inertia of a DC motor as significant factor on the performance of PSO algorithm utilizing WTRI based fitness function2016 IEEE Conference on Systems, Process and Control (ICSPC)10.1109/SPC.2016.7920736(236-241)Online publication date: Dec-2016
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Aziz MTaib MAdnan R(2016)The functionality validation of PSO algorithm in optimizing PID controller's parameters using multi-dimensional test functions2016 7th IEEE Control and System Graduate Research Colloquium (ICSGRC)10.1109/ICSGRC.2016.7813328(203-208)Online publication date: Aug-2016
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Cited By

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Recommendations

PSO+: A new particle swarm optimization algorithm for constrained problems

CAPSO: Centripetal accelerated particle swarm optimization

Opposition based initialization in particle swarm optimization (O-PSO)

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