Simulated annealing with thresheld convergence
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Chen, Stephen; Xudiera, Carlos; Montgomery, James
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Stochastic search techniques for multi-modal search spaces require the ability to balance exploration with exploitation. Exploration is required to find the best region, and exploitation is required to find the best solution (i.e. the local optimum) within this region. Compared to hill climbing which is purely exploitative, simulated annealing probabilistically allows "backward" steps which facilitate exploration. However, the balance between exploration and exploitation in simulated annealing...[Show more]
dc.contributor.author | Chen, Stephen | |
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dc.contributor.author | Xudiera, Carlos | |
dc.contributor.author | Montgomery, James | |
dc.coverage.spatial | Brisbane Australia | |
dc.date.accessioned | 2012-07-04T02:25:39Z | |
dc.date.available | 2012-07-04T02:25:39Z | |
dc.date.created | June 10-15 2012 | |
dc.identifier.citation | Chen, S., Xudiera, C. & Montgomery, J. (June 2012). Simulated annealing with thresheld convergence. Paper presented at the 2012 IEEE Congress on Evolutionary Computation (CEC), Brisbane, Australia, June 10-15, 2012 (pp. 1946-1952)[and] 2012 IEEE World Congress on Computational Intelligence. Piscataway, NJ: IEEE CEC | |
dc.identifier.isbn | 978-1-4673-1508-1 | |
dc.identifier.isbn | 978-1-4673-1510-4 | |
dc.identifier.uri | http://hdl.handle.net/1885/9121 | |
dc.description.abstract | Stochastic search techniques for multi-modal search spaces require the ability to balance exploration with exploitation. Exploration is required to find the best region, and exploitation is required to find the best solution (i.e. the local optimum) within this region. Compared to hill climbing which is purely exploitative, simulated annealing probabilistically allows "backward" steps which facilitate exploration. However, the balance between exploration and exploitation in simulated annealing is biased towards exploitation - improving moves are always accepted, so local (greedy) search steps can occur at even the earliest stages of the search process. The purpose of "thresheld convergence" is to have these early-stage local search steps "held" back by a threshold function. It is hypothesized that early local search steps can interfere with the effectiveness of a search technique's (concurrent) mechanisms for global search. Experiments show that the addition of thresheld convergence to simulated annealing can lead to significant performance improvements in multi-modal search spaces. | |
dc.description.sponsorship | IEEE Computational Intelligence Society | |
dc.format | 7 pages | |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE Inc) | |
dc.relation.ispartofseries | IEEE Congress on Evolutionary Computation (CEC 2012) | |
dc.rights | http://www.ieee.org/publications_standards/publications/rights/ieeecopyrightform.pdf "… Authors and/or their employers shall have the right to post the accepted version of IEEE-copyrighted articles on their own personal servers or the servers of their institutions or employers without permission from IEEE, provided that the posted version includes a prominently displayed IEEE copyright notice and, when published, a full citation to the original IEEE publication, including a link to the article abstract in IEEEXplore. Authors shall not post the final, published versions of their papers." From January 2011, "the following copyright notice must be displayed on the initial screen displaying IEEE copyrighted material": "© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works." - from publisher web site (as at 24/03/11) | |
dc.source | 2012 IEEE Congress on Evolutionary Computation Proceedings | |
dc.subject | simulated annealing | |
dc.subject | optimisation | |
dc.subject | multimodal optimisation | |
dc.subject | diversity control | |
dc.subject | heuristic search | |
dc.subject | annealing | |
dc.subject | benchmark testing | |
dc.subject | convergence | |
dc.subject | educational institutions | |
dc.subject | particle swarm optimization | |
dc.subject | trajectory | |
dc.title | Simulated annealing with thresheld convergence | |
dc.type | Conference paper | |
local.description.notes | James Montgomery also identified as Erin Montgomery | |
local.description.refereed | Yes | |
dcterms.dateAccepted | 2012 | |
dc.date.issued | 2012-06 | |
local.identifier.absfor | 080108 - Neural, Evolutionary and Fuzzy Computation | |
local.identifier.ariespublication | f5625xPUB1923 | |
local.publisher.url | http://www.ieee.org/index.html | |
local.type.status | Accepted Version | |
local.contributor.affiliation | Chen, Stephen, York University, Toronto, Canada, School of Information Technology | |
local.contributor.affiliation | Xudiera, Carlos, York University, Toronto, Canada, Department of Computer Science and Engineering | |
local.contributor.affiliation | Montgomery, James, ANU, Research School of Computer Science | |
local.bibliographicCitation.startpage | 1 | |
local.bibliographicCitation.lastpage | 7 | |
local.identifier.doi | 10.1109/CEC.2012.6256591 | |
dc.date.updated | 2015-12-10T11:24:22Z | |
local.identifier.scopusID | 2-s2.0-84866867182 | |
Collections | ANU Research Publications |
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