@Article{iasc.2024.042841,
AUTHOR = {Fangzhen Ge, Yating Wu, Debao Chen, Longfeng Shen},
TITLE = {A Reference Vector-Assisted Many-Objective Optimization Algorithm with Adaptive Niche Dominance Relation},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {39},
YEAR = {2024},
NUMBER = {2},
PAGES = {189--211},
URL = {http://www.techscience.com/iasc/v39n2/56491},
ISSN = {2326-005X},
ABSTRACT = {It is still a huge challenge for traditional Pareto-dominated many-objective optimization algorithms to solve many-objective optimization problems because these algorithms hardly maintain the balance between convergence and diversity and can only find a group of solutions focused on a small area on the Pareto front, resulting in poor performance of those algorithms. For this reason, we propose a reference vector-assisted algorithm with an adaptive niche dominance relation, for short MaOEA-AR. The new dominance relation forms a niche based on the angle between candidate solutions. By comparing these solutions, the solution with the best convergence is found to be the non-dominated solution to improve the selection pressure. In reproduction, a mutation strategy of -bit crossover and hybrid mutation is used to generate high-quality offspring. On 23 test problems with up to 15-objective, we compared the proposed algorithm with five state-of-the-art algorithms. The experimental results verified that the proposed algorithm is competitive.},
DOI = {10.32604/iasc.2024.042841}
}