T1 Invited paper: A Review of Thresheld Convergence
A1 Chen, Stephen
A1 Montgomery, James
A1 Bolufé-Röhler, Antonio
A1 Gonzalez-Fernandez, Yasser
K1 Exploration
K1 Exploitation
K1 Heuristic Algorithms
K1 Optimization
K1 Multi-modality
AB 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.
PB Universidad Pablo de Olavide
SN 2255-5684
YR 2015
FD 2015
LK http://hdl.handle.net/10433/2780
UL http://hdl.handle.net/10433/2780
LA en
NO GECONTEC: Revista Internacional de Gestión del Conocimiento y la Tecnología
DS RIO
RD May 30, 2026