A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition

By Anupam Trivedi, Dipti Srinivasan, Krishnendu Sanyal, Abhiroop Ghosh. IEEE Transactions on Evolutionary Computation, 2017

550 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Civil and Structural Engineering, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/0e543d99ae883df85a63c82b6f7e00bfeed09a52

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In Artificial Intelligence

1. Yaochu Jin
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4. Hisao Ishibuchi
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In Civil and Structural Engineering

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In Computational Theory and Mathematics

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2. Kay Chen Tan
3. Ryoji Tanabe
4. Hisao Ishibuchi
5. Sam Kwong
6. Qingfu Zhang
7. Yong Wang
8. Ponnuthurai Nagaratnam Suganthan

Citations in the corpus

• Evolutionary Constrained Multiobjective Optimization: Test Suite Construction and Performance Comparisons (2019, methodology): “Artificial test problems can attract more researchers to design EAs for solving CMOPs.”
• Improved Regularity Model-Based EDA for Many-Objective Optimization (2018, unknown): “[9], [15]–[17].”
• Handling Constrained Multiobjective Optimization Problems With Constraints in Both the Decision and Objective Spaces (2019, methodology): “In the decomposition-based CMOEAs, the original CMOP is decomposed into a set of constrained single-objective optimization problems, and then these problems are optimized in a collaborative way [34].”
• A Survey on Evolutionary Constrained Multiobjective Optimization (2023, methodology): “Up to now, a large number of MOEAs have been proposed, and they can be roughly divided into the methods based on dominance [7]–[9], the methods based on decomposition [10]–[12], and the methods based on indicator [13]–[15].”
• Pareto Multi-Task Learning (2019, background): “4 Pareto Multi-Task Learning Decomposition-based multi-objective evolutionary algorithm [27, 28], which decomposes a multi-objective optimization problem (MOP) into several subproblems and solves them simultaneously, is one of the most popular gradient-free multi-objective optimization methods.”
• An easy-to-use real-world multi-objective optimization problem suite (2020, methodology): “We used the Tchebycheff function g tch [6] and the PBI function g pbi [6] in MOEA/D since they were used in many previous studies [62].”