A Grid-Based Evolutionary Algorithm for Many-Objective Optimization

By Shengxiang Yang, Miqing Li, Xiaohui Liu, Jinhua Zheng. IEEE Transactions on Evolutionary Computation, 2013

808 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/b2c52a57f80a986610d8a9d47dd32af2ed838e08

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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. Hisao Ishibuchi
8. Xin Yao

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. Markus Olhofer

Citations in the corpus

• 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].”
• A Vector Angle-Based Evolutionary Algorithm for Unconstrained Many-Objective Optimization (2017, background): “I. INTRODUCTION MULTIOBJECTIVE problems (MOPs) with at least fourconflicting objectives are informally known as manyobjective optimization problems (MaOPs) [1], [2].”
• An Evolutionary Many-Objective Optimization Algorithm Based on Dominance and Decomposition (2015, methodology): “We consider four state-of-the-art EMO algorithms, including NSGA-III, MOEA/D, GrEA [70], and HypE [28] for comparisons.”
• PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum] (2017, neutral): “grEA [43] 2013 gRid-bASEd EvoluTionARY AlgoRiThm”
• Localized Weighted Sum Method for Many-Objective Optimization (2018, neutral): “, -dominancebased algorithm (DEA) [6] shift-density-based evolutionary algorithm (SDEA) [7], grid-based evolutionary algorithm (GrEA) [8] and 2) Performance indicator based, e.”
• A benchmark test suite for evolutionary many-objective optimization (2017, neutral): “In recent years, a number of newalgorithmshave been proposed for dealingwithMaOPs [1], including the convergence enhancement based algorithms such as the grid-dominancebased evolutionary algorithm (GrEA) [2], the knee pointdriven evolutionary algorithm (KnEA) [3], the two-archive algorithm (Two_Arch”
• A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization (2018, methodology): “assignment based NSGA-II [18], grid-based evolutionary algorithm [19], preference-inspired coevolutionary algorithm [20], many-objective evolutionary algorithm based on directional diversity and favorable convergence [21], and knee point driven evolutionary algorithm (KnEA) [22].”
• Shift-Based Density Estimation for Pareto-Based Algorithms in Many-Objective Optimization (2014, background): “, dominance area control [67], k-optimality [23], preference order ranking [21], subspace dominance comparison [2], [44], and grid dominance [77].”
• Many-Objective Evolutionary Algorithms (2015, methodology): “Yang et al. [2013] proposed a grid-based evolutionary algorithm (GrEA) for optimizing MaOPs.”
• A Strengthened Dominance Relation Considering Convergence and Diversity for Evolutionary Many-Objective Optimization (2018, methodology): “The grid dominance relation is a relaxation of Pareto dominance, whereas it is often criticized due to its high sensitivity to the parameter div [9].”
• A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization (2016, methodology): “By contrast, neither GrEA nor KnEA is able to converge to the true PF of 10-objective DTLZ1.”
• Modified Distance Calculation in Generational Distance and Inverted Generational Distance (2015, methodology): “Among other indicators, the inverted generational distance (IGD [4], [19]) is most frequently used for performance evaluation of EMO algorithms in evolutionary many-objective optimization studies [7], [9], [23].”
• IGD Indicator-Based Evolutionary Algorithm for Many-Objective Optimization Problems (2018, background): “[26] proposed the grid-based approach to select the solutions that”
• A New Two-Stage Evolutionary Algorithm for Many-Objective Optimization (2019, methodology): “For instance, the -dominance method [17] employed a relaxed factor to compare the dominance relation between solutions; the preference order approach [18] used a ranking procedure to lower the adverse impact of the Pareto-dominance-based comparison; the fuzzy dominance method [19] was proposed with ”
• Automated Test Case Generation as a Many-Objective Optimisation Problem with Dynamic Selection of the Targets (2018, neutral): “[54] introduced a Grid-based Evolutionary Algorithm (GrEA) that divides the search space into hyperboxes of a given size and uses the concepts of grid dominance and grid distance to improve diversity among individuals by determining their mutual relationship in a grid environment.”
• Balancing Convergence and Diversity in Decomposition-Based Many-Objective Optimizers (2016, methodology): “the diversity in many-objective optimization, usually failing to achieve a good coverage of PF [20], [26], [43].”
• An Indicator-Based Multiobjective Evolutionary Algorithm With Reference Point Adaptation for Better Versatility (2018, background): “such as the grid dominance-based evolutionary algorithm [7] and knee point driven evolutionary algorithm [8], among many others [2].”