Multi-objective optimization approach based on Minimum Population Search algorithm

• Darian Reyes-Fernández-de-Bulnes ^[1] ; Antonio Bolufé-Röhler ^[2] ; Dania Tamayo-Vera ^[3]
  1. [1] Instituto Tecnológico de Tijuana

     Instituto Tecnológico de Tijuana

     México

     
  2. [2] University of Prince Edward Island

     University of Prince Edward Island

     Canadá

     
  3. [3] Thinking Big Inc., Canada

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• Localización: GECONTEC: revista Internacional de Gestión del Conocimiento y la Tecnología, ISSN-e 2255-5684, Vol. 7, Nº. 2, 2019, págs. 1-19
• Idioma: inglés
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• Resumen

  • Minimum Population Search is a recently developed metaheuristic for optimization of mono-objective continuous problems, which has proven to be a very effective optimizing large scale and multi-modal problems. One of its key characteristic is the ability to perform an efficient exploration of large dimensional spaces. We assume that this feature may prove useful when optimizing multi-objective problems, thus this paper presents a study of how it can be adapted to a multi-objective approach. We performed experiments and comparisons with five multi-objective selection processes and we test the effectiveness of Thresheld Convergence on this class of problems. Following this analysis we suggest a Multi-objective variant of the algorithm. The proposed algorithm is compared with multi-objective evolutionary algorithms IBEA, NSGA2 and SPEA2 on several well-known test problems. Subsequently, we present two hybrid approaches with the IBEA and NSGA-II, these hybrids allow to further improve the achieved results.