Multi-objective optimization approach based on Minimum Population Search algorithm
作者: Darian Reyes-Fernández-de-BulnesAntonio Bolufé-RöhlerDania Tamayo-Vera
作者单位: 1Instituto Tecnológico de Tijuana
2University of Prince Edward Island
3Thinking Big Inc.
刊名: International Journal of Knowledge and Technology Management, 2019, Vol.7 (2), pp.1-19
来源数据库: Revista Internacional de Gestión del Conocimiento y la Tecnología
关键词: Evolutionary AlgorithmMinimum Population SearchThresheld ConvergenceMulti-objective Optimization
英文摘要: 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...
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