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Comparison of Firefly algorithm optimisation, particle swarm optimisation and differential evolution

Authors:

Karl O. Jones,

Grégoire BoizantéAuthors Info & Claims

CompSysTech '11: Proceedings of the 12th International Conference on Computer Systems and Technologies

Pages 191 - 197

https://doi.org/10.1145/2023607.2023640

Published: 16 June 2011 Publication History

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Abstract

Evolutionary Computation (EC) is a recent and lively area of study. Some of the recent approaches within EC are particle Swarm Optimisation (PSO) and Differential Evolution (DE), while one of the latest to be developed is Firefly Algorithm (FA): all of which can be used in optimisation problems. This paper makes a comparison of the effectiveness of these three methods on a specific optimisation problem, specifically tuning the parameters of a PID controller.

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Cited By

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Kambham JRamarakula M(2022)An efficient approach for anti-jamming in IRNSS receivers using improved PSO based parametric wavelet packet thresholdingSatellite Navigation10.1186/s43020-022-00082-23:1Online publication date: 10-Oct-2022
https://doi.org/10.1186/s43020-022-00082-2
Ma ZZhong JLiu WYu W(2022)An evolutionary framework for automatic security guards deployment in large public spacesApplied Intelligence10.1007/s10489-022-03975-653:10(11586-11598)Online publication date: 8-Sep-2022
https://doi.org/10.1007/s10489-022-03975-6
Kühn EŠešum-Čavić V(2022)A Framework-Based Approach for Flexible Evaluation of Swarm-Intelligent AlgorithmsWomen in Computational Intelligence10.1007/978-3-030-79092-9_18(393-412)Online publication date: 14-Apr-2022
https://doi.org/10.1007/978-3-030-79092-9_18
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Index Terms

Comparison of Firefly algorithm optimisation, particle swarm optimisation and differential evolution
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
    2. Numerical analysis
      1. Numerical differentiation
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization

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Published In

CompSysTech '11: Proceedings of the 12th International Conference on Computer Systems and Technologies

June 2011

688 pages

ISBN:9781450309172

DOI:10.1145/2023607

Editors:
Boris Rachev
Technical University of Varna, Varna, Bulgaria
,
Angel Smrikarov
University of Ruse, Ruse, Bulgaria

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: 16 June 2011

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CompSysTech '11: 12th International Conference on Computer Systems and Technologies

June 16 - 17, 2011

Vienna, Austria

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Kambham JRamarakula M(2022)An efficient approach for anti-jamming in IRNSS receivers using improved PSO based parametric wavelet packet thresholdingSatellite Navigation10.1186/s43020-022-00082-23:1Online publication date: 10-Oct-2022
https://doi.org/10.1186/s43020-022-00082-2
Ma ZZhong JLiu WYu W(2022)An evolutionary framework for automatic security guards deployment in large public spacesApplied Intelligence10.1007/s10489-022-03975-653:10(11586-11598)Online publication date: 8-Sep-2022
https://doi.org/10.1007/s10489-022-03975-6
Kühn EŠešum-Čavić V(2022)A Framework-Based Approach for Flexible Evaluation of Swarm-Intelligent AlgorithmsWomen in Computational Intelligence10.1007/978-3-030-79092-9_18(393-412)Online publication date: 14-Apr-2022
https://doi.org/10.1007/978-3-030-79092-9_18
Luo FYuan YDing WLu HEmrouznejad AQian Z(2018)An Improved Particle Swarm Optimization Algorithm Based on Adaptive Weight for Task Scheduling in Cloud ComputingProceedings of the 2nd International Conference on Computer Science and Application Engineering10.1145/3207677.3278089(1-5)Online publication date: 22-Oct-2018
https://dl.acm.org/doi/10.1145/3207677.3278089
Xing BMarwala TXing BMarwala T(2017)Cyberware Capacity—Applications Layer PerspectiveSmart Maintenance for Human–Robot Interaction10.1007/978-3-319-67480-3_8(173-191)Online publication date: 10-Sep-2017
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Ebadifard FBabamir S(2017) A PSO ‐based task scheduling algorithm improved using a load‐balancing technique for the cloud computing environment Concurrency and Computation: Practice and Experience10.1002/cpe.436830:12Online publication date: 6-Dec-2017
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Xing B(2016)Novel Nature-Derived Intelligent Algorithms and Their Applications in Antenna OptimizationWideband, Multiband, and Smart Reconfigurable Antennas for Modern Wireless Communications10.4018/978-1-4666-8645-8.ch010(296-339)Online publication date: 2016
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Chou JChong WBui D(2016)Nature-Inspired Metaheuristic Regression System: Programming and Implementation for Civil Engineering ApplicationsJournal of Computing in Civil Engineering10.1061/(ASCE)CP.1943-5487.000056130:5Online publication date: Sep-2016
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Kalantzis GShang CLei YLeventouri T(2016)Investigations of a GPU-based levy-firefly algorithm for constrained optimization of radiation therapy treatment planningSwarm and Evolutionary Computation10.1016/j.swevo.2015.09.00626(191-201)Online publication date: Feb-2016
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A hybrid genetically-bacterial foraging algorithm converged by particle swarm optimisation for global optimisation

Bacterial foraging optimisation algorithm, particle swarm optimisation and genetic algorithm: a comparative study

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