research-article

Theoretical and empirical study of particle swarms with additive stochasticity and different recombination operators

Author:

Jorge PeñaAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 95 - 102

https://doi.org/10.1145/1389095.1389109

Published: 12 July 2008 Publication History

Abstract

Standard particle swarms exhibit both multiplicative and additive stochasticity in their update equations. Recently, a simpler particle swarm with just additive stochasticity has been proposed and studied using a new theoretical approach. In this paper we extend the main results of that study to a large number of existing particle swarm optimisers by defining a general update rule from which actual algorithms can be instantiated via the choice of specific recombination operators. In particular, we derive the stability conditions and the dynamic equations for the first two moments of the sampling distribution during stagnation, and show how they depend on the used recombination operator. Finally, the optimisation efficiency of several particle swarms with additive stochasticity is compared in a suite of 16 benchmark functions.

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Cited By

Camacho‐Villalón CDorigo MStützle T(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
https://doi.org/10.1111/itor.13176
Camacho-Villalon CDorigo MStutzle T(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
https://doi.org/10.1109/TEVC.2021.3102863
Camacho Villalón CStützle TDorigo M(2020)Grey Wolf, Firefly and Bat Algorithms: Three Widespread Algorithms that Do Not Contain Any NoveltySwarm Intelligence10.1007/978-3-030-60376-2_10(121-133)Online publication date: 23-Oct-2020
https://doi.org/10.1007/978-3-030-60376-2_10
Show More Cited By

Index Terms

Theoretical and empirical study of particle swarms with additive stochasticity and different recombination operators
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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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
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

Copyright © 2008 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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Publication History

Published: 12 July 2008

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GECCO08

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GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

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

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Cited By

Camacho‐Villalón CDorigo MStützle T(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
https://doi.org/10.1111/itor.13176
Camacho-Villalon CDorigo MStutzle T(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
https://doi.org/10.1109/TEVC.2021.3102863
Camacho Villalón CStützle TDorigo M(2020)Grey Wolf, Firefly and Bat Algorithms: Three Widespread Algorithms that Do Not Contain Any NoveltySwarm Intelligence10.1007/978-3-030-60376-2_10(121-133)Online publication date: 23-Oct-2020
https://doi.org/10.1007/978-3-030-60376-2_10
al-Rifaie M(2014)Penguins Huddling OptimisationInternational Journal of Agent Technologies and Systems10.4018/ijats.20140401016:2(1-29)Online publication date: 1-Apr-2014
https://doi.org/10.4018/ijats.2014040101
Peña J(2008)Simple Dynamic Particle Swarms without VelocityProceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence10.1007/978-3-540-87527-7_13(144-154)Online publication date: 22-Sep-2008
https://dl.acm.org/doi/10.1007/978-3-540-87527-7_13

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