Article

Particle swarm with speciation and adaptation in a dynamic environment

Authors:

Jürgen Branke,

Tim BlackwellAuthors Info & Claims

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

Pages 51 - 58

https://doi.org/10.1145/1143997.1144005

Published: 08 July 2006 Publication History

Abstract

This paper describes an extension to a speciation-based particle swarm optimizer (SPSO) to improve performance in dynamic environments. The improved SPSO has adopted several proven useful techniques. In particular, SPSO is shown to be able to adapt to a series of dynamic test cases with varying number of peaks (assuming maximization). Inspired by the concept of quantum swarms, this paper also proposes a particle diversification method that promotes particle diversity within each converged species. Our results over the moving peaks benchmark test functions suggest that SPSO incorporating this particle diversification method can greatly improve its adaptability hence optima tracking performance.

References

[1]

J. Angeline. Tracking extrema in dynamic environments. In Proc. of the Evolutionary Programming VI, pages 335--345, Indianapolis, 1997. Berlin: Springer-Verlag.]]

Digital Library

[2]

T. Back. On the behaviour of evolutionary algorithms in dynamic environments. In Proc. of International Conference on Evolutionary Computation, pages 446--551. NJ: IEEE Press, 1998.]]

Digital Library

[3]

T. Blackwell and P. Bentley. Dynamic search with charged swarms. In Proc. the Workshop on Evolutionary Algorithms Dynamic Optimization Problems (EvoDOP-2003), pages 19--26, 2002.]]

[4]

T. Blackwell and J. Branke. Multi-swarm optimization in dynamic environments. In LNCS, No. 3005, Proc. of Applications of Evolutionary Computing: EvoWorkshops 2004: EvoBIO, EvoCOMNET, EvoHOT, EvoISAP, EvoMUSART, and EvoSTOC, pages 489--500, 2004.]]

[5]

T. Blackwell and J. Branke. Multi-swarms, exclusion, and anti-convergence in dynamic environments. IEEE Transactions on Evolutionary Computation, to appear.]]

[6]

J. Branke. Memory enhanced evolutionary algorithms for changing optimization problems. In Proc. Congress on Evolutionary Computation CEC'99, pages 1875--1882, 1999.]]

[7]

J. Branke. Evolutionary Optimization in Dynamic Environments. Kluwer Academic Publishers, Norwell, MA, 2002.]]

Digital Library

[8]

J. Branke, E. Salihoglu, and S. Uyar. Towards an analysis of dynamic environments. In Genetic and Evolutionary Computation Conference, pages 1433--1439. ACM, 2005.]]

Digital Library

[9]

J. Branke and H. Schmeck. Designing evolutionary algorithms for dynamic optimization problems. In S. Tsutsui and A. Ghosh, editors, Theory and Application of Evolutionary Computation: Recent Trends, pages 239--262. Springer, 2002.]]

Digital Library

[10]

A. Carlisle and G. Dozier. Adapting particle swarm optimization to dynamic environments. In Proc. the International Conference on Artificial Intelligence (ICAI2000), pages 429--434, 2000.]]

[11]

A. Carlisle and G. Dozier. Tracking changing extrema with adaptive particle swarm optimizer. In Proc. World Automation Cong., pages 265--270, Orlando FL USA, 2002.]]

[12]

M. Clerc and J. Kennedy. The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput., 6:58--73, Feb. 2002.]]

Digital Library

[13]

R. C. Eberhart and Y. Shi. Tracking and optimizing dynamic systems with particle swarms. In Proc. the 2001 Congress on Evolutionary Computation CEC2001, pages 94--100. IEEE Press, 27-30 May 2001.]]

[14]

X. Hu and R. Eberhart. Adaptive particle swarm optimisation: detection and response to dynamic systems. In Proc. Congress on Evolutionary Computation, pages 1666--1670, 2002.]]

Digital Library

[15]

S. Janson and M. Middendorf. A hierarchical particle swarm optimizer for dynamic optimization problems. In G. Raidl, editor, Applications of Evolutionary Computing, LNCS (3005), pages 513--524. Springer, 2004.]]

[16]

Y. Jin and J. Branke. Evolutionary optimization in uncertain environments - a survey. IEEE Trans. Evol. Comput., 9:303--317, 2005.]]

Digital Library

[17]

J. Kennedy and R. Eberhart. Swarm Intelligence. Morgan Kaufmann, 2001.]]

Digital Library

[18]

J.-P. Li, M. E. Balazs, G. T. Parks, and P. J. Clarkson. A species conserving genetic algorithm for multimodal function optimization. Evol. Comput., 10(3):207--234, 2002.]]

Digital Library

[19]

X. Li. Adaptively choosing neighbourhood bests using species in a particle swarm optimizer for multimodal function optimization. In K. Deb, editor, Proc. of Genetic and Evolutionary Computation Conference 2004(LNCS 3102), pages 105--116, 2004.]]

[20]

D. Parrott and X. Li. A particle swarm model for tacking multiple peaks in a dynamic environment using speciation. In Proc. of the 2004 Congress on Evolutionary Computation, pages 98--103, 2004.]]

[21]

A. Petrowski. A clearing procedure as a niching method for genetic algorithms. In Proc. of the 3rd IEEE International Conference on Evolutionary Computation, pages 798--803, 1996.]]

[22]

R. Ursem. Multinational GAs: Multimodal optimization techniques in dynamic environments. In D. Whitley, editor, Proc. Congress on Evolutionary Computation, pages 1666--1670, 2002.]]

Cited By

Yazdani DYazdani DBlanco-Davis ENguyen T(2024)A survey of multi-population optimization algorithms for tracking the moving optimum in dynamic environmentsJournal of Membrane Computing10.1007/s41965-024-00163-yOnline publication date: 2-Jul-2024
https://doi.org/10.1007/s41965-024-00163-y
Mohammadpour MMostafavi SMirjalili S(2024)Solving dynamic optimization problems using parent–child multi-swarm clustered memory (PCSCM) algorithmNeural Computing and Applications10.1007/s00521-024-10205-236:31(19549-19583)Online publication date: 7-Aug-2024
https://doi.org/10.1007/s00521-024-10205-2
Rezvanian AVahidipour SSaghiri A(2023)CaAIS: Cellular Automata-Based Artificial Immune System for Dynamic EnvironmentsAlgorithms10.3390/a1701001817:1(18)Online publication date: 30-Dec-2023
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Index Terms

Particle swarm with speciation and adaptation in a dynamic environment
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
    2. Numerical analysis
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization

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cover image ACM Conferences

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

Copyright © 2006 ACM.

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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Published: 08 July 2006

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GECCO06: Genetic and Evolutionary Computation Conference

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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Cited By

Yazdani DYazdani DBlanco-Davis ENguyen T(2024)A survey of multi-population optimization algorithms for tracking the moving optimum in dynamic environmentsJournal of Membrane Computing10.1007/s41965-024-00163-yOnline publication date: 2-Jul-2024
https://doi.org/10.1007/s41965-024-00163-y
Mohammadpour MMostafavi SMirjalili S(2024)Solving dynamic optimization problems using parent–child multi-swarm clustered memory (PCSCM) algorithmNeural Computing and Applications10.1007/s00521-024-10205-236:31(19549-19583)Online publication date: 7-Aug-2024
https://doi.org/10.1007/s00521-024-10205-2
Rezvanian AVahidipour SSaghiri A(2023)CaAIS: Cellular Automata-Based Artificial Immune System for Dynamic EnvironmentsAlgorithms10.3390/a1701001817:1(18)Online publication date: 30-Dec-2023
https://doi.org/10.3390/a17010018
Aslan MSen S(2023)A dynamic trust management model for vehicular ad hoc networksVehicular Communications10.1016/j.vehcom.2023.10060841:COnline publication date: 5-Jun-2023
https://dl.acm.org/doi/10.1016/j.vehcom.2023.100608
Zhang JLu YChe LZhou M(2022)Moving-Distance-Minimized PSO for Mobile Robot SwarmIEEE Transactions on Cybernetics10.1109/TCYB.2021.307934652:9(9871-9881)Online publication date: Sep-2022
https://doi.org/10.1109/TCYB.2021.3079346
Parque V(2022)Learning Obstacle-Avoiding Lattice Paths using Swarm Heuristics: Exploring the Bijection to Ordered Trees2022 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC55065.2022.9870344(1-8)Online publication date: 18-Jul-2022
https://dl.acm.org/doi/10.1109/CEC55065.2022.9870344
Parque V(2022)Towards Hexapod Gait Adaptation using Enumerative Encoding of Gaits: Gradient-Free Heuristics2022 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC55065.2022.9870257(1-7)Online publication date: 18-Jul-2022
https://dl.acm.org/doi/10.1109/CEC55065.2022.9870257
Bai MKung FTao C(2022)Tracking of Moving Sources in a Reverberant Environment Using Evolutionary AlgorithmsIEEE Access10.1109/ACCESS.2022.321283210(107563-107574)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3212832
Boulesnane AMeshoul S(2022)Do We Need Change Detection for Dynamic Optimization Problems?: A SurveyArtificial Intelligence and Its Applications10.1007/978-3-030-96311-8_13(132-142)Online publication date: 12-Mar-2022
https://doi.org/10.1007/978-3-030-96311-8_13
Yazdani DCheng RYazdani DBranke JJin YYao X(2021)A Survey of Evolutionary Continuous Dynamic Optimization Over Two Decades—Part AIEEE Transactions on Evolutionary Computation10.1109/TEVC.2021.306001425:4(609-629)Online publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1109/TEVC.2021.3060014
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