Large-Scale Evolutionary Optimization Approach Based on Decision Space Decomposition

نویسندگان

چکیده

The identification of decision variable interactions has a crucial role in the final outcome algorithm large-scale optimization domain. It is prerequisite for decomposition-based algorithms to achieve grouping. In this paper, we design recognition method with higher efficiency and grouping accuracy. based on decomposition strategy min hash solve global (LSGO) problems, called MHD. Our proposed focuses discovering variables through forming subcomponents principle that interdependencies between these are maintained at minimal level. This described as follows: first, performs several permutations vector composed variables. Second, index value first non-zero row after rearrangement found obtain new feature vector. Third, probability identical data each position calculated decide whether there some certain advantages are: simpler computation greater improvement than comparison two or variables; ability find similar very quickly; cluster simple way. Therefore, well reliability MHD guaranteed. On accuracy aspect, various types benchmark test function. Finally, experimental results analysis summarize performance competitiveness our from aspects when it used within co-evolutionary framework.

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ژورنال

عنوان ژورنال: Frontiers in Energy Research

سال: 2022

ISSN: ['2296-598X']

DOI: https://doi.org/10.3389/fenrg.2022.926161