A Multiobjective Evolutionary Algorithm Based on Decision Variable Analyses for Multiobjective Optimization Problems With Large-Scale Variables

By Xiaoliang Ma, Fang Liu, Yutao Qi, Xiaodong Wang, Lingling Li, L. Jiao, Minglei Yin, Maoguo Gong. IEEE Transactions on Evolutionary Computation, 2016

441 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/dc46dae64ffb89a0115f50f10f839b537cefd96b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yaochu Jin
2. Xing-yi Zhang
3. Ye Tian
4. Ran Cheng
5. Xiutao Zheng
6. Yusuke Nojima
7. Hisao Ishibuchi
8. Chao Wang

In Computational Theory and Mathematics

1. Yaochu Jin
2. Xing-yi Zhang
3. Ye Tian
4. Ran Cheng
5. Xiutao Zheng
6. Fan Cheng
7. Yusuke Nojima
8. Chao Wang

Citations in the corpus

• Efficient Large-Scale Multiobjective Optimization Based on a Competitive Swarm Optimizer (2020, methodology): “As a result, it has been empirically verified that most existing MOEAs cannot solve large-scale MOPs well [10], [11].”
• A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization (2018, methodology): “Algorithm 2 presents the details of the interaction analysis for convergence-related variables, which adopts the strategy developed in [65].”
• An Indicator-Based Multiobjective Evolutionary Algorithm With Reference Point Adaptation for Better Versatility (2018, background): “However, further investigation of applying IGD-NS in other MOEAs is still desirable, especially on problems with a large number of decision variables, namely, largescale MOPs or MaOPs [49], [63].”
• An Evolutionary Algorithm for Large-Scale Sparse Multiobjective Optimization Problems (2020, background): “On the other hand, although some MOEAs have been tailored for large-scale MOPs [29]–[31], they cannot be applied to large-scale sparse MOPs with binary variables since these MOEAs are based on decision variable division and only effective in solving MOPs with real variables.”
• PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum] (2017, background): “moEA/dvA [59] 2016 mulTi-obJECTivE EvoluTionARY AlgoRiThm bASEd on dECiSion vARiAblE AnAlYSES”
• A Framework for Large-Scale Multiobjective Optimization Based on Problem Transformation (2018, methodology): “7–12 and also Figs.”