research-article

An evaporation mechanism for dynamic and noisy multimodal optimization

Authors:

Jose Luis Fernandez-Marquez,

Josep Lluis ArcosAuthors Info & Claims

GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation

Pages 17 - 24

https://doi.org/10.1145/1569901.1569905

Published: 08 July 2009 Publication History

Abstract

Dealing with imprecise information is a common characteristic in real-world problems. Specifically, when the source of the information are physical sensors, a level of noise in the evaluation has to be assumed. Particle Swarm Optimization is a technique that presented a good behavior when dealing with noisy fitness functions. Nevertheless, the problem is still open. In this paper we propose the use of the evaporation mechanism for managing with dynamic multi-modal optimization problems that are subject to noisy fitness functions. We will show how the evaporation mechanism does not require the detection of environment changes and how can be used for improving the performance of PSO algorithms working in noisy environments.

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Cited By

Rossides GHunter AMetcalfe B(2022)Source Localisation Using Wavefield Correlation-Enhanced Particle Swarm OptimisationRobotics10.3390/robotics1102005211:2(52)Online publication date: 18-Apr-2022
https://doi.org/10.3390/robotics11020052
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
Kazemi Kordestani JRazapoor Mirsaleh MRezvanian AMeybodi M(2021)An Overview of Multi-population Methods for Dynamic EnvironmentsAdvances in Learning Automata and Intelligent Optimization10.1007/978-3-030-76291-9_7(253-286)Online publication date: 24-Jun-2021
https://doi.org/10.1007/978-3-030-76291-9_7
Show More Cited By

Index Terms

An evaporation mechanism for dynamic and noisy multimodal optimization
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Multi-agent systems
    2. Search methodologies
      1. Heuristic function construction

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cover image ACM Conferences

GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation

July 2009

2036 pages

ISBN:9781605583259

DOI:10.1145/1569901

General Chair:
Franz Rothlauf
University of Mainz, Germany

Copyright © 2009 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: 08 July 2009

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GECCO09

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GECCO09: Genetic and Evolutionary Computation Conference

July 8 - 12, 2009

Québec, Montreal, Canada

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

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Cited By

Rossides GHunter AMetcalfe B(2022)Source Localisation Using Wavefield Correlation-Enhanced Particle Swarm OptimisationRobotics10.3390/robotics1102005211:2(52)Online publication date: 18-Apr-2022
https://doi.org/10.3390/robotics11020052
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
Kazemi Kordestani JRazapoor Mirsaleh MRezvanian AMeybodi M(2021)An Overview of Multi-population Methods for Dynamic EnvironmentsAdvances in Learning Automata and Intelligent Optimization10.1007/978-3-030-76291-9_7(253-286)Online publication date: 24-Jun-2021
https://doi.org/10.1007/978-3-030-76291-9_7
Choi SBae J(2020)An Effective Adjustment to the Integration of Optimal Computing Budget Allocation for Particle Swarm Optimization in Stochastic EnvironmentsIEEE Access10.1109/ACCESS.2020.30255598(173654-173665)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3025559
Fernandez-Marquez JArcos JSerugendo G(2018)A DECENTRALIZED APPROACH FOR DETECTING DYNAMICALLY CHANGING DIFFUSE EVENT SOURCES IN NOISY WSN ENVIRONMENTSApplied Artificial Intelligence10.1080/08839514.2012.65365926:4(376-397)Online publication date: 25-Dec-2018
https://dl.acm.org/doi/10.1080/08839514.2012.653659
Fernandez-Marquez JMarzo Serugendo GMontagna SViroli MArcos J(2018)Description and composition of bio-inspired design patternsNatural Computing: an international journal10.1007/s11047-012-9324-y12:1(43-67)Online publication date: 19-Dec-2018
https://dl.acm.org/doi/10.1007/s11047-012-9324-y
Taghiyeh SXu J(2016)A new particle swarm optimization algorithm for noisy optimization problemsSwarm Intelligence10.1007/s11721-016-0125-210:3(161-192)Online publication date: 9-Jul-2016
https://doi.org/10.1007/s11721-016-0125-2
Rada-Vilela JJohnston MZhang M(2015)Population statistics for particle swarm optimization: Hybrid methods in noisy optimization problemsSwarm and Evolutionary Computation10.1016/j.swevo.2015.01.00322(15-29)Online publication date: Jun-2015
https://doi.org/10.1016/j.swevo.2015.01.003
Rada-Vilela JJohnston MZhang M(2014)Population statistics for particle swarm optimization: Resampling methods in noisy optimization problemsSwarm and Evolutionary Computation10.1016/j.swevo.2014.02.00417(37-59)Online publication date: Aug-2014
https://doi.org/10.1016/j.swevo.2014.02.004
Rada-Vilela JJohnston MZhang M(2014)Population statistics for particle swarm optimization: Single-evaluation methods in noisy optimization problemsSoft Computing10.1007/s00500-014-1438-y19:9(2691-2716)Online publication date: 12-Sep-2014
https://doi.org/10.1007/s00500-014-1438-y
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