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An investigation into the merger of stochastic diffusion search and particle swarm optimisation

Authors:

Mohammad Majid al-Rifaie,

Mark John Bishop,

Tim BlackwellAuthors Info & Claims

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

Pages 37 - 44

https://doi.org/10.1145/2001576.2001583

Published: 12 July 2011 Publication History

Abstract

This study reports early research aimed at applying the powerful resource allocation mechanism deployed in Stochastic Diffusion Search (SDS) [4] to the Particle Swarm Optimiser (PSO) metaheuristic [22], effectively merging the two swarm intelligence algorithms. The results reported herein suggest that the hybrid algorithm, exploiting information sharing between particles, has the potential to improve the optimisation capability of conventional PSOs.

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Cited By

Muthukkumar MDiwakaran S(2023)Stochastic diffusion hunt optimization for potential load balancing in wireless sensor networksMaterials Today: Proceedings10.1016/j.matpr.2023.02.019Online publication date: Feb-2023
https://doi.org/10.1016/j.matpr.2023.02.019
M PRMS P(2022)Optimizing minimum spanning tree using stochastic–Variable neighborhood search for efficient clustering of cancer gene dataConcurrency and Computation: Practice and Experience10.1002/cpe.757335:5Online publication date: 16-Dec-2022
https://doi.org/10.1002/cpe.7573
Ramanan MRaj J(2021)Maintaining the Data Integrity and Data Replication in Cloud using Modified Genetic Algorithm (Mga) and Greedy Search Algorithm (Gsa)Oriental journal of computer science and technology10.13005/ojcst13.0203.0413:0203(82-90)Online publication date: 30-Jan-2021
https://doi.org/10.13005/ojcst13.0203.04
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An investigation into the merger of stochastic diffusion search and particle swarm optimisation

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cover image ACM Conferences

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

July 2011

2140 pages

ISBN:9781450305570

DOI:10.1145/2001576

Editor:
Natalio Krasnogor
University of Nottingham, UK
,
General Chair:
Pier Luca Lanzi
Politecnico di Milano, Italy

Copyright © 2011 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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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

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Published: 12 July 2011

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GECCO '11

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SIGEVO

GECCO '11: Genetic and Evolutionary Computation Conference

July 12 - 16, 2011

Dublin, Ireland

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

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Cited By

Muthukkumar MDiwakaran S(2023)Stochastic diffusion hunt optimization for potential load balancing in wireless sensor networksMaterials Today: Proceedings10.1016/j.matpr.2023.02.019Online publication date: Feb-2023
https://doi.org/10.1016/j.matpr.2023.02.019
M PRMS P(2022)Optimizing minimum spanning tree using stochastic–Variable neighborhood search for efficient clustering of cancer gene dataConcurrency and Computation: Practice and Experience10.1002/cpe.757335:5Online publication date: 16-Dec-2022
https://doi.org/10.1002/cpe.7573
Ramanan MRaj J(2021)Maintaining the Data Integrity and Data Replication in Cloud using Modified Genetic Algorithm (Mga) and Greedy Search Algorithm (Gsa)Oriental journal of computer science and technology10.13005/ojcst13.0203.0413:0203(82-90)Online publication date: 30-Jan-2021
https://doi.org/10.13005/ojcst13.0203.04
Sowmiya BPoovammal E(2021)A Heuristic K-Anonymity Based Privacy Preserving for Student Management Hyperledger Fabric blockchainWireless Personal Communications10.1007/s11277-021-08582-1127:2(1359-1376)Online publication date: 18-May-2021
https://doi.org/10.1007/s11277-021-08582-1
Mejia JHernández ACossio Franco EMontes MLara-Alvarez CAvila-George H(2019)Solving Instances of an Order Picking Model for the Second-Hand Toy Industry Combining Amalgam Case-Based Reasoning and PSO AlgorithmsHandbook of Research on Metaheuristics for Order Picking Optimization in Warehouses to Smart Cities10.4018/978-1-5225-8131-4.ch016(289-302)Online publication date: 2019
https://doi.org/10.4018/978-1-5225-8131-4.ch016
López-Santos IRivera GGonzález S(2019)Application of the Order-Picking and Self-Organizing Maps Models to Optimize the Supply ChainHandbook of Research on Metaheuristics for Order Picking Optimization in Warehouses to Smart Cities10.4018/978-1-5225-8131-4.ch012(227-248)Online publication date: 2019
https://doi.org/10.4018/978-1-5225-8131-4.ch012
Suganya ERajan C(2019)RETRACTED ARTICLE: An AdaBoost-modified classifier using stochastic diffusion search model for data optimization in Internet of ThingsSoft Computing10.1007/s00500-019-04554-724:14(10455-10465)Online publication date: 27-Nov-2019
https://doi.org/10.1007/s00500-019-04554-7
Ramanan MVivekanandan P(2018)Efficient data integrity and data replication in cloud using stochastic diffusion methodCluster Computing10.1007/s10586-018-2480-9Online publication date: 15-Mar-2018
https://doi.org/10.1007/s10586-018-2480-9
Hussain KMohd Salleh MCheng SShi Y(2018)Metaheuristic research: a comprehensive surveyArtificial Intelligence Review10.1007/s10462-017-9605-zOnline publication date: 13-Jan-2018
https://doi.org/10.1007/s10462-017-9605-z
Ravi AGiriprasad MNaganjaneyulu P(2017)SAR images denoising using a novel stochastic diffusion wavelet schemeCluster Computing10.1007/s10586-017-1001-621:1(229-237)Online publication date: 1-Jul-2017
https://doi.org/10.1007/s10586-017-1001-6
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