.png){kind=icon}
.png){kind=icon}
Return
A dual-sampling based evolutionary algorithm for large-scale multi-objective optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2024.112344.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The vast search space in large-scale multi-objective optimization represents a significant challenge for evolutionary algorithms to converge towards the Pareto Front. As an effective search strategy, direction-guided sampling technique could improve the search efficiency by exploring along the approximated directions to approach the Pareto set. However, the approximated directions may fail to interact with the true Pareto set and result in inefficient search. To address this issue, a dual-sampling method is proposed in this paper. In addition to the samples along the directions approximated by direction-guided sampling, fuzzy Gaussian sampling is applied to adjust the search direction and generate more accurate and evenly distributed solutions. Moreover, a convergence-and-diversity-based mating selection is introduced to balance the exploration and exploitation. The experiments on 72 test benchmarks with bi- and tri-objectives and 500-5000 decision variables show the superiority of the proposed algorithm compare with the state-of-the-art algorithms.
Keywords:
Multi-objective evolutionary algorithm
Large-scale multi-objective optimization
Direction-guided sampling
Reproduction
Journal
IF:
6.6
Papers:
1.4W
Citations:
4.8W