Invited paper: A Review of Thresheld Convergence

• Stephen Chen ^[1] ; James Montgomery ^[2] ; Antonio Bolufé-Röhler ^[3] ; Yasser Gonzalez-Fernandez ^[1]
  1. [1] York University (Canadá)

     York University (Canadá)

     Canadá

     
  2. [2] University of Tasmania

     University of Tasmania

     Australia

     
  3. [3] Universidad de La Habana

     Universidad de La Habana

     Cuba

     

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• Localización: GECONTEC: revista Internacional de Gestión del Conocimiento y la Tecnología, ISSN-e 2255-5684, Vol. 3, Nº. 1, 2015, págs. 1-13
• Idioma: inglés
• Enlaces
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• Resumen

  • 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.

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