Invited paper: A Review of Thresheld Convergence

Stephen Chen (), James Montgomery (), Antonio Bolufe-Rohler () and Yasser Gonzalez-Fernandez ()
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Stephen Chen: York University
James Montgomery: University of Tasmania
Antonio Bolufe-Rohler: Universidad de La Habana
Yasser Gonzalez-Fernandez: York University

Revista Internacional de Gestión del Conocimiento y la Tecnología (GECONTEC), 2015, vol. 3, issue 1, 1-13

Abstract: 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.

Keywords: Exploration; Exploitation; Heuristic Algorithms; Optimization; Multi-modality (search for similar items in EconPapers)
JEL-codes: D8 D81 M1 M15 O3 O31 O32 (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:rge:journl:v:3:y:2015:i:1:p:1-13

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