TY  - EJOU
AU  - Ge, Fangzhen 
AU  - Wu, Yating 
AU  - Chen, Debao 
AU  - Shen, Longfeng 

TI  - A Reference Vector-Assisted Many-Objective Optimization Algorithm with Adaptive Niche Dominance Relation
T2  - Intelligent Automation \& Soft Computing

PY  - 2024
VL  - 39
IS  - 2
SN  - 2326-005X

AB  - 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.
KW  - Many-objective optimization; evolutionary algorithm; Pareto dominance; reference vector; adaptive niche

DO  - 10.32604/iasc.2024.042841