.png){kind=icon}
返回
A Multitasking-Based Constrained Multi-Objective Evolutionary Algorithm With Forward and Backward Stages
.png){kind=icon}
PRE
AI
DOI：10.1109/TETCI.2024.3393368.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Solving constrained multi-objective optimization problems (CMOPs) via evolutionary multitasking optimization (EMTO) algorithm is a meaningful attempt due to the fact that knowledge learning and transfer across the related optimization exercises can improve the efficiency of others. However, the utilization of EMTO in CMOPs is still in its infancy. To fill this research gap, an EMTO-based constrained multi-objective optimization framework including forward and backward stages (CEMTFB) is developed to address an original CMOP task together with two helper CMOP tasks. Firstly, the original task and a helper task evolve forward simultaneously with and without constraints, responsible for exploring well-converged and well-distributed feasible and infeasible solutions, respectively. Then, in the backward stage, a novel reverse haulage strategy is designed for another helper task to conduct a search within the promising areas that are not dominated by any examined feasible solution, thereby collaborating with the original task to approach the constrained Pareto front from two complementary directions. Moreover, a dynamic knowledge transfer strategy is proposed to coordinate the interaction between the original and helper tasks. Finally, comprehensive experiments are conducted on 45 benchmark functions and 19 real-world CMOPs, and the comparison against seven state-of-the-art peer methods demonstrates the superior or at least competitive performance of CEMTFB.
Keyword:
Task analysis
Optimization
Statistics
Sociology
Knowledge transfer
Multitasking
Convergence
Constrained multi-objective optimization problems (CMOPs)
evolutionary multitasking
multi-stage
knowledge transfer
期刊
I
IF:
6.5
论文数:
1.4K
被引数:
4.5K
机构
引用论文
.png){kind=icon}
Evolutionary Multitasking with Global and Local Auxiliary Tasks for Constrained Multi-Objective Optimization具有全局和局部辅助任务的进化多任务处理，用于约束多目标优化
PREAI
分享收藏PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective OptimizationPlatEMO: 用于进化多目标优化的MATLAB平台
OAAI
OAAI分享收藏A Meta-Knowledge Transfer-Based Differential Evolution for Multitask Optimization基于元知识转移的差分进化多任务优化算法
OAAI
OAAI分享收藏
查看更多内容