Nonlinear multiobjective optimization

By Kaisa Miettinen. 1998

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

1. Yaochu Jin
2. Ye Tian
3. Ran Cheng
4. Xing-yi Zhang
5. Handing Wang
6. Kalyanmoy Deb
7. Qingfu Zhang
8. Hisao Ishibuchi

Citations in the corpus

• Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution (2018, background): “Multi-objective Optimization Multi-objective optimization (Miettinen, 1999) deals with problems that have multiple, complementary objective functions f 1 , . . . , f n .”
• The Promise and Peril of Human Evaluation for Model Interpretability (2017, background): “• Multi-objective loss functions – Optimization of a multi-objective loss function becomes difficult when nontrivial [10, 13, 5, 1].”
• Multi-Task Learning as Multi-Objective Optimization (2018, methodology): “We recommend Miettinen (1998) and Ehrgott (2005) for surveys of this field.”
• Open-ended Learning in Symmetric Zero-sum Games (2019, neutral): “Our setting resembles multiobjective optimization (Fonseca & Fleming, 1993; Miettinen, 1998).”
• Caveats for information bottleneck in deterministic scenarios (2018, unknown): “For a full treatment of multi-objective optimization, see Miettinen (1999).”
• Two_Arch2: An Improved Two-Archive Algorithm for Many-Objective Optimization (2015, background): “IN the real world, many problems [1], [2] can be regarded as many-objective optimization problems (ManyOPs) [3], [4], a category of multiobjective optimization problems (MOPs) [5] with more than three objectives.”
• Deep Reinforcement Learning for Multiobjective Optimization (2019, methodology): “ization problem usually leads to a Pareto optimal solution. The desired Pareto Front (PF) can be obtained when all the scalar optimization problems are solved. In specific, the well-known Weighted Sum [30] approach is employed. Certainly, other scalarizing methods can also be JOURNAL OF IEEE TRANSCAT”
• A Population Prediction Strategy for Evolutionary Dynamic Multiobjective Optimization (2014, background): “An optimal tradeoff set, which is called Pareto set (PS) in the decision space and Pareto front (PF) in the objective space, defines the optimality of an MOP [21], [22].”
• Dynamic Multi-objective Optimization and Decision-Making Using Modified NSGA-II: A Case Study on Hydro-thermal Power Scheduling (2007, background): “objective GA and solve several -constraint problems [8] by fixing f1 value at different levels.”
• An efficient, adaptive parameter variation scheme for metaheuristics based on the epsilon-constraint method (2006, unknown): “Whenever a multiobjective problem has a finite number of Pareto-optimal solutions, one would expect being able to identify all of them using one of the traditional generating methods proposed in the literature, detailed overviews and discussion of such methods can be found in [7,10,5].”
• MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition (2007, methodology): “To overcome these shortcomings, some effort has been made to incorporate other techniques such as -constraint into this approach, more details can be found in [1].”
• Innovization: innovating design principles through optimization (2006, methodology): “Ideally, such a bi-objective optimization task results in a set of optimal solutions, known as Pareto-optimal solutions, each portraying a trade-off between the two objectives [19, 6].”
• A systematic review and meta-Analysis of SWARA and WASPAS methods: Theory and applications with recent fuzzy developments (2017, unknown): “Gal, Stewart and Hanne [95], Miettinen [96].”
• Stable Matching-Based Selection in Evolutionary Multiobjective Optimization (2014, methodology): “Several approaches can be used to decompose the approximation of the PF into a number of scalar optimization subproblems [10], [21].”