NEJun 2, 2019
Push and Pull Search Embedded in an M2M Framework for Solving Constrained Multi-objective Optimization Problems
Zhun Fan, Zhaojun Wang, Wenji Li, Yutong Yuan, Yugen You, Zhi Yang, Fuzan Sun, Jie Ruan, Zhaocheng Li
arXiv:1906.00402v173 citations
Originality Synthesis-oriented
AI Analysis
This work aims to improve optimization algorithms for researchers and practitioners in evolutionary computation, but appears incremental as it builds on existing MOEA frameworks.
The paper tackled the challenge of balancing convergence and diversity in multi-objective evolutionary algorithms for constrained optimization problems, proposing a push-pull search method embedded in an M2M framework to address this issue.
In dealing with constrained multi-objective optimization problems (CMOPs), a key issue of multi-objective evolutionary algorithms (MOEAs) is to balance the convergence and diversity of working populations.