Expensive Multiobjective Optimization by MOEA/D With Gaussian Process Model

By Qingfu Zhang, Wudong Liu, Edward P. K. Tsang, Botond Virginas. IEEE Transactions on Evolutionary Computation, 2010

701 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/c6aacb107b0d5b0e13493ccdad22e81301c715f8

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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. Handing Wang
5. Ran Cheng
6. Kay Chen Tan
7. Yew-Soon Ong
8. Tinkle Chugh

In Computational Theory and Mathematics

1. Yaochu Jin
2. Handing Wang
3. Xing-yi Zhang
4. Ye Tian
5. Ran Cheng
6. Kay Chen Tan
7. Tinkle Chugh
8. Chaoli Sun

In Management Science and Operations Research

1. Yaochu Jin
2. Ye Tian
3. Xing-yi Zhang
4. Ran Cheng
5. Handing Wang
6. Tinkle Chugh
7. Kaisa Miettinen
8. Cheng He

Citations in the corpus

• Data-Driven Evolutionary Optimization: An Overview and Case Studies (2019, methodology): “In optimization, ExI [52] is adopted to identify the most promising candidate solutions, and k-means clustering is applied in the decision space to choose sampling points, while fuzzy c-means clustering [68] is introduced to limit the number of data for training the Kriging model.”
• Review of surrogate modeling in water resources (2012, methodology): “4, has stimulated a great deal of research to extend the expected improvement concept to multiobjective optimization [Bautista, 2009; Ginsbourger et al., 2010; Jeong et al., 2005; Keane, 2006; Knowles, 2006; Ponweiser et al., 2008; Svenson, 2011; Wagner et al., 2010; Zhang et al., 2010].”
• MOEA/D with Adaptive Weight Adjustment (2014, methodology): “…(Pal et al., 2010a,b), wireless sensor networks (Konstantinidis et al., 2010), robot path planning (Waldocka and Corne, 2011), missile control (Zhang et al., 2010a), portfolio management (Zhang et al., 2010c) and rule mining in machine learning (Chan et al., 2010) have also been investigated.”
• A Classification-Based Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective Optimization (2019, methodology): “FEs with the computationally cheap surrogate, thereby reducing the computation time for solving expensive optimization problems [21].”
• A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition (2017, methodology): “[75] proposed MOEA/D with Gaussian stochas-”
• A Multi-Facet Survey on Memetic Computation (2011, methodology): “Other specialized refinement procedures proposed for mitigating the impact of uncertain search environments or noisy problems include the trust-region derivative-free memetic algorithms [79]–[82].”
• Data-Driven Surrogate-Assisted Multiobjective Evolutionary Optimization of a Trauma System (2016, background): “Many machine learning models, such as linear, nonlinear or polynomial repression models [30], Kriging or Gaussian processes [31], [32], [33], [34], [35], support vector mach ines (SVMs) [36], radial basis function (RBF) networks [37], [38], [39], and many other neural networks [40], [41], [42], [43]”
• A Gaussian Process Surrogate Model Assisted Evolutionary Algorithm for Medium Scale Expensive Optimization Problems (2014, methodology): “It has been widely used for initialization in some other SAEAs [7], [15].”
• Model-Based Multi-objective Optimization: Taxonomy, Multi-Point Proposal, Toolbox and Benchmark (2015, methodology): “Right now, only one multi-objective approach exists [23] (see [3] for a comparison of methods and a new approach in the single-objective case).”
• Surrogate-Assisted Cooperative Swarm Optimization of High-Dimensional Expensive Problems (2017, methodology): “The most commonly used surrogate models include polynomial regression (PR) [19], also known as response surface methodology [19], support vector machines (SVMs) [20], [21], [22], artificial neural networks (ANNs) [12], [23], [24], radial basis function (RBF) networks [25], [26], [27], [28], [29], an”
• A Random Forest-Assisted Evolutionary Algorithm for Data-Driven Constrained Multiobjective Combinatorial Optimization of Trauma Systems (2020, background): “SAEAs for single-objective optimization [6], [25], [29], [30]; multiobjective optimization [12], [22], [31], [32]; and manyobjective optimization [13], [33] have been proposed.”