An adaptive reference point technique to improve the quality of decomposition based multi-objective evolutionary algorithm
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https://doi.org/10.54939/1859-1043.j.mst.CSCE7.2023.3-14Keywords:
Evolutionary multi-objective optimization; Balance of exploration and exploitation; Population distribution; Empty region; Adaptive reference point; MOEA/D.Abstract
Applying multi-objective evolutionary optimization algorithms in solving multi-objective optimization problems is a research field that has received attention recently. In the literature of this research field, many studies have been carried out to propose multi-objective evolutionary algorithms or improve published algorithms. However, balancing the exploitation and exploration capabilities of the algorithm during the evolution process is still challenging. This article proposes an approach to solve that equilibrium problem based on analyzing population distribution during the evolutionary process to identify empty regions in which no solutions are selected. After that, information about empty regions with the most significant area will be combined with the current reference point to create a new reference point to prioritize choosing solutions in those regions. Experiments on 10 test problems of 2 typical benchmark sets showed that this mechanism increases the diversity of the population, thereby contributing to a balance between the algorithm's abilities in the evolutionary process and enhancing the algorithm.
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