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.
History
Publication title
GECONTEC: Revista Internacional de Gestión del Conocimiento y la Tecnología
Pagination
1-13
ISSN
2255-5684
Department/School
School of Information and Communication Technology
Publisher
Universidad Pablo de Olavide
Place of publication
Spain
Repository Status
Restricted
Socio-economic Objectives
Expanding knowledge in the information and computing sciences