Indicator-Based Selection in Multiobjective Search

By Eckart Zitzler, Simon Künzli. Parallel Problem Solving from Nature, 2004

2,415 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/8ed104b1d836930e88cc048e55bc9b985b05542f

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. Ran Cheng
3. Xing-yi Zhang
4. Ye Tian
5. Hisao Ishibuchi
6. Xin Yao
7. Gary G. Yen
8. Kalyanmoy Deb

In Computational Theory and Mathematics

1. Yaochu Jin
2. Xing-yi Zhang
3. Ran Cheng
4. Ye Tian
5. Hisao Ishibuchi
6. Gary G. Yen
7. Kalyanmoy Deb
8. Xin Yao

Citations in the corpus

• An Improved Dimension-Sweep Algorithm for the Hypervolume Indicator (2006, methodology): “Despite these limitations, the hypervolume indicator is still considered to possess some reasonable properties, having also been proposed as a guidance criterion for accepting solutions in Multiobjective Evolutionary Algorithms [2], [3].”
• Theory of the hypervolume indicator: optimal μ-distributions and the choice of the reference point (2009, background): “In various publications, the reference point is chosen as the nadir point of the investigated solution set, e.g., in [20, 19, 13], while others recommend rescaling of the objective values everytime the hypervolume indicator is computed [25].”
• A Grid-Based Evolutionary Algorithm for Many-Objective Optimization (2013, neutral): “, the aggregation-based criterion [26], [2 8], [71] and indicator-based criterion [3], [4], [74].”
• Covariance Matrix Adaptation for Multi-objective Optimization (2007, methodology): “For example, the (cid:6) -indicator is a promising candidate (Zitzler and K¨unzli, 2004).”
• Two-Archive Evolutionary Algorithm for Constrained Multiobjective Optimization (2017, methodology): “tist nondominated sorting genetic algorithm (NSGA-II) [2], indicator-based EA [3], and multiobjective EA based on decomposition [4].”
• Test Problems for Large-Scale Multiobjective and Many-Objective Optimization (2017, methodology): “In general, multi-objective EAs (MOEAs) can be categorized into three groups, including dominance-based methods [6]–[8], decomposition-based methods [9]–[11], and performance indicator-based methods [12], [13].”
• A Method for a Posteriori Identification of Knee Points Based on Solution Density (2018, background): “MOEAs can be roughly classified into four categories, namely, dominancebased methods [1]–[4], indicators or metrics based approaches [5]–[7], aggregation or decomposition based methods [8]–[11], as well as preference-based methods [12]–[15].”
• A Classification-Based Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective Optimization (2019, methodology): “, the region-based selection algorithm (PESA-II) [5], the improved strength Pareto evolutionary algorithm [6], the elitist nondominated sorting genetic algorithm (NSGA-II) [7], the improved indicator-based evolutionary algorithm [8], and the MOEA based on decomposition (MOEA/D) [9].”
• Benchmark Problems and Performance Indicators for Search of Knee Points in Multiobjective Optimization (2020, methodology): “MOEAs for MaOPs can roughly be divided into four categories [8], [9]; namely, 1) dominance relationship modification-based methods [10]–[13]; 2) indicators-based methods [14]–[16]; 3) aggregation-based (also known as decomposition based) [17]–[19]; or 4) preference-based approaches [20]–[23].”
• An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions (2017, methodology): “Representative methods include IBEA (Zitzler and Künzli (2004)), SMS-EMOA (Beume et al (2007)), HypE (Bader and Zitzler (2011)), FV-MOEA (Jiang et al (2015)) and so on.”
• A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization (2018, background): “Among many others [23]–[26], some representative approaches of this category are indicator-based evolutionary algorithm [27], S-metric selection based evolutionary multiobjective optimization algorithm [28], and hypervolume (HV)-based evolutionary algorithm [29].”
• Fuzzy-Based Pareto Optimality for Many-Objective Evolutionary Algorithms (2014, background): “IBEA [14] is probably the most successful implementation of this class in that it has been shown to be more effective than other MOEAs for higher dimensional MOPs.”
• Balancing Convergence and Diversity in Decomposition-Based Many-Objective Optimizers (2016, methodology): “There have been a few well-established indicator-based MOEAs [30]–[32] available in the literature that employ the HV as the selection criterion.”
• The jMetal framework for multi-objective optimization: Design and architecture (2010, methodology): “This allows, for example, to develop indicator based algorithms, such as IBEA, or to establish a termination condition consisting of getting a front with an specific value of a given indicator.”