Invited paper: A Review of Thresheld Convergence
A multi-modal search space can be defined as having multiple attraction basins – each basin has a single local optimum which is reached from all points in that basin when greedy local search is used. Optimization in multi-modal search spaces can then be viewed as a two-phase process. The first phase...
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Universidad Pablo de Olavide
2015-07-01
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Online Access: | http://www.upo.es/revistas/index.php/gecontec/article/view/1410/pdf_15 |
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doaj-4677a20b83af4ab59cf5414714c883a92020-11-25T02:54:26ZengUniversidad Pablo de OlavideGECONTEC: Revista Internacional de Gestión del Conocimiento y la Tecnología2255-56842015-07-0131113Invited paper: A Review of Thresheld ConvergenceStephen Chen0James Montgomery1Antonio Bolufé-Röhler2Yasser Gonzalez-Fernandez3York UniversityUniversity of TasmaniaUniversidad de La HabanaYork UniversityA multi-modal search space can be defined as having multiple attraction basins – each basin has a single local optimum which is reached from all points in that basin when greedy local search is used. Optimization in multi-modal search spaces can then be viewed as a two-phase process. The first phase is exploration in which the most promising attraction basin is identified. The second phase is exploitation in which the best solution (i.e. the local optimum) within the previously identified attraction basin is attained. The goal of thresheld convergence is to improve the performance of search techniques during the first phase of exploration. The effectiveness of thresheld convergence has been demonstrated through applications to existing metaheuristics such as particle swarm optimization and differential evolution, and through the development of novel metaheuristics such as minimum population search and leaders and followers.http://www.upo.es/revistas/index.php/gecontec/article/view/1410/pdf_15ExplorationExploitationHeuristic AlgorithmsOptimizationMulti-modality |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Stephen Chen James Montgomery Antonio Bolufé-Röhler Yasser Gonzalez-Fernandez |
spellingShingle |
Stephen Chen James Montgomery Antonio Bolufé-Röhler Yasser Gonzalez-Fernandez Invited paper: A Review of Thresheld Convergence GECONTEC: Revista Internacional de Gestión del Conocimiento y la Tecnología Exploration Exploitation Heuristic Algorithms Optimization Multi-modality |
author_facet |
Stephen Chen James Montgomery Antonio Bolufé-Röhler Yasser Gonzalez-Fernandez |
author_sort |
Stephen Chen |
title |
Invited paper: A Review of Thresheld Convergence |
title_short |
Invited paper: A Review of Thresheld Convergence |
title_full |
Invited paper: A Review of Thresheld Convergence |
title_fullStr |
Invited paper: A Review of Thresheld Convergence |
title_full_unstemmed |
Invited paper: A Review of Thresheld Convergence |
title_sort |
invited paper: a review of thresheld convergence |
publisher |
Universidad Pablo de Olavide |
series |
GECONTEC: Revista Internacional de Gestión del Conocimiento y la Tecnología |
issn |
2255-5684 |
publishDate |
2015-07-01 |
description |
A multi-modal search space can be defined as having multiple attraction basins – each basin has a single local optimum which is reached from all points in that basin when greedy local search is used. Optimization in multi-modal search spaces can then be viewed as a two-phase process. The first phase is exploration in which the most promising attraction basin is identified. The second phase is exploitation in which the best solution (i.e. the local optimum) within the previously identified attraction basin is attained. The goal of thresheld convergence is to improve the performance of search techniques during the first phase of exploration. The effectiveness of thresheld convergence has been demonstrated through applications to existing metaheuristics such as particle swarm optimization and differential evolution, and through the development of novel metaheuristics such as minimum population search and leaders and followers. |
topic |
Exploration Exploitation Heuristic Algorithms Optimization Multi-modality |
url |
http://www.upo.es/revistas/index.php/gecontec/article/view/1410/pdf_15 |
work_keys_str_mv |
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