.png){kind=icon}
返回
Incremental learning-inspired mating restriction strategy for Evolutionary Multiobjective Optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2022.109430.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The prior knowledge from the problem property can boost the evolutionary multiobjective opti-mization (EMO). The existing machine learning model for knowledge mining in the EMO has led to enhanced performance on multiobjective optimization problems with complicated Pareto fronts (MOPs-cPF), but the high computational cost resulting from model training should not be taken as a natural expense. To overcome this drawback, this paper proposes an incremental learning-inspired mating restriction strategy (ILMR) for solving MOP-cPF efficiently. In ILMR, a mating restriction is implemented based on an incremental learning model that establishes the mating pool for each solution in the population and incrementally updates as the population evolves. Specifically, it consists of two interdependent parts, i.e., a learning module and a forgetting module. In one evolutionary cycle, the learning module is used to learn new knowledge from the high-quality offspring solutions, while the forgetting module is utilized to remove the information provided by relatively poor solutions in the population. Moreover, a multiobjective evolutionary algorithm with ILMR, named MEILM, is proposed and compared with six state-of-the-art algorithms on a variety of MOP-cPF. The experimental results show that there are significant improvements benefitting from the proposed mating restriction strategy.(c) 2022 Elsevier B.V. All rights reserved.
Keyword:
Evolutionary Multiobjective Optimization
Incremental learning
Mating restriction
Complicated Pareto front
Evolutionary algorithm
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
机构
引用论文
PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective OptimizationPlatEMO: 用于进化多目标优化的MATLAB平台
OAAI
OAAI分享收藏分享收藏A general framework for evolutionary multiobjective optimization via manifold learning.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏Self-organizing multiobjective optimization based on decomposition with neighborhood ensembleNEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏Global and Local Surrogate-Assisted Differential Evolution for Expensive Constrained Optimization Problems With Inequality Constraints具有不等式约束的昂贵约束优化问题的全局和局部代理辅助差分进化OAAI
OAAI分享收藏Lack of floristic identity in campos rupestres —A hyperdiverse mosaic of rocky montane savannas in South AmericaFloraOAAI
FloraIF0
OAAI分享收藏
查看更多内容