Objective Reduction in Many-Objective Optimization: Linear and Nonlinear Algorithms

By Dhish Kumar Saxena, João A. Duro, Ashutosh Tiwari, Kalyanmoy Deb, Qingfu Zhang. IEEE Transactions on Evolutionary Computation, 2013

296 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/67f9a3be09e5f5450685c60dfa9f9a6d09b6c7e8

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In Artificial Intelligence

1. Yaochu Jin
2. Ran Cheng
3. Xing-yi Zhang
4. Ye Tian
5. Gary G. Yen
6. Yanan Sun
7. Xin Yao
8. Handing Wang

In Computational Theory and Mathematics

1. Yaochu Jin
2. Xing-yi Zhang
3. Ran Cheng
4. Ye Tian
5. Gary G. Yen
6. Yanan Sun
7. Xin Yao
8. Handing Wang

In Management Science and Operations Research

1. Yaochu Jin
2. Xing-yi Zhang
3. Ye Tian
4. Ran Cheng
5. Gary G. Yen
6. Yanan Sun
7. Zhang Yi
8. Xin Yao

Citations in the corpus

• A Strength Pareto Evolutionary Algorithm Based on Reference Direction for Multiobjective and Many-Objective Optimization (2017, background): “objective reduction) focuses on the reduction of the number of objectives [6], [8], [37], [38], which attempts to circumvent the problems of MaOPs by means of identification and”
• Many-Objective Evolutionary Algorithms (2015, methodology): “Paper Test Problems ( m ) L-PCA [Saxena et al. 2013] DTLZ(25), WFG(25) NL-MVU-PCA DTLZ5(I,M)(50) Exact-δ -MOSS/ k -EMOSS [Brockhoff and Zitzler 2009] - Greedy-δ -MOSS [Brockhoff and Zitzler 2006a, 2006b, 2009] DTLZ(25), MKP(25) Greedy-k -EMOSS [Brockhoff and Zitzler 2006a, 2006b, 2009] DTLZ(25),…”
• A Grid-Based Evolutionary Algorithm for Many-Objective Optimization (2013, unknown): “A detailed description of the DTLZ and DTLZ5(I,M ) problem suites can be found in [16], [60].”
• MOEA/D with Adaptive Weight Adjustment (2014, background): “NSGA-II deteriorates the situation by preferring the remote and boundary non-dominated solutions (Saxena et al., 2010); therefore, it obtains good coverage and diversity but converges slowly.”
• A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization (2018, background): “One branch is to use the objective reduction approaches to eliminate redundant or irrelevant objectives, such as dominance relation preservation based algorithms [40], [41], unsupervised feature selection based algorithms [42], Pareto corner search based algorithms [43], machine learning based objec”
• An Evolutionary Many-Objective Optimization Algorithm Based on Dominance and Decomposition (2015, background): “analysis [56], to identify the embedded EF in the ambient objective space.”
• A comprehensive survey on NSGA-II for multi-objective optimization and applications (2023, background): “The potential benefits of objective reduction have been clearly highlighted in several studies (Saxena et al. 2013; Sinha et al. 2013; Yuan et al. 2018).”
• A benchmark test suite for evolutionary many-objective optimization (2017, methodology): “In addition, the DTLZ and WFG functions which are used as MaOPs with a degenerate Pareto front (i.e., DTLZ5, DTLZ6 and WFG3) have a nondegenerate part of the Pareto front when the number of objectives is larger than four [10,21,22].”
• Performance of Decomposition-Based Many-Objective Algorithms Strongly Depends on Pareto Front Shapes (2017, background): “they have four or more objectives [10], [36], [37].”
• Improved Regularity Model-Based EDA for Many-Objective Optimization (2018, background): “[45] proposed the linear PCA and nonlinear MVC over a set of Pareto-optimal solutions to check the correlation.”
• Two_Arch2: An Improved Two-Archive Algorithm for Many-Objective Optimization (2015, methodology): “Additionally, Convergence g(x) is a part of the DTLZ problems, hence, we adopt it as a metric to analyze the convergence ability of Two_Arch2 as in [29].”