Multiobjective optimization Test Instances for the CEC 2009 Special Session and Competition

By Qingfu Zhang, Aimin Zhou, Shi-Zheng Zhao, Ponnuthurai Nagaratnam Suganthan, Wudong Liu, Santosh Tiwari. 2009

1,115 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d0d7a51c9c959248ede0dc3a4684038c10f34fd1

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Citations in the corpus

Data-Driven Surrogate-Assisted Multiobjective Evolutionary Optimization of a Trauma System (2016, methodology): “For this purpose, we set the stopping criterion to be 1 h for each of the three compared and then calculate the IGD value of the solution set using the same reference set as the above experiment.”
A Framework for Large-Scale Multiobjective Optimization Based on Problem Transformation (2018, methodology): “Additionally, we show that it can signiﬁcantly outperform existing classical and large-scale algorithms on the tested multiobjective WFG [4], ZDT [5], DTLZ [6], and CEC2009 competition [7] problems with large numbers of variables.”
An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints (2014, unknown): “As a performance metric, we have chosen the inverse generational distance (IGD) metric [47], [51], which as a single metric can provide a combined information about the convergence and diversity of the obtained solutions.”
Borg: An Auto-Adaptive Many-Objective Evolutionary Computing Framework (2013, background): “, 2006) and CEC 2009 (Zhang et al., 2009b) test suites, we observed that filling the remaining slots with solutions selected randomly from the archive and mutated using uniform mutation applied with probability 1/L achieved significantly better results.”
Bat algorithm for multi-objective optimisation (2011, background): “There are many different test functions for multobjective optimization (Schaffer, 1985; Zhang et al., 2003; Zhang et al, 2009; Zitzler and Thiele, 1999; Zitzler et al., 2000), but a subset of a few widely used functions provides a wide range of diverse properties in terms Pareto front and Pareto…”
Multiobjective cuckoo search for design optimization (2013, background): “There are many different test functions for multiobjective optimization [13,48,50,51], but a subset of a few widely used functions provides a wide range of diverse properties in terms Pareto front and Pareto optimal set.”
Handling Constrained Multiobjective Optimization Problems With Constraints in Both the Decision and Objective Spaces (2019, background): “It is well-known that in unconstrained multiobjective optimization, many excellent benchmark functions have been put forward during the past two decades [17], [22], [39], [40].”
An Evolutionary Algorithm for Large-Scale Sparse Multiobjective Optimization Problems (2020, background): “As for other test suites with complicated Pareto sets [61], [62], few MOPs in them have sparse Pareto optimal solutions.”
Bio-inspired computation: Where we stand and what's next (2019, background): “Multi-objective optimization CEC’2009 [328] and editions ever since, ZDT [329], DTLZ [330], WFG [331], LZ [332], UF [328] and others [333] From classical functions to dynamic benchmarks”
PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum] (2017, methodology): “UF1–UF12 [81] 2008 Unconstrained multi-objective test problems for the CEC 2009 special session and competition F1–F9 for MOEA/D-DE [18] 2009 The test problems extended from [82] designed for MOEA/D-DE C1 DTLZ1, C2 DTLL2, C3 DTLZ4 2014 Constrained DTLZ and IDTLZ1, IDTLZ2 [41] inverted DTLZ F1–F7 for”
A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition (2017, methodology): “state-of-the-art MOEAs on UF [72] test instances.”
Two_Arch2: An Improved Two-Archive Algorithm for Many-Objective Optimization (2015, methodology): “Therefore, we use IGD [57] as the performance metric in our experi-”
A Multiobjective Evolutionary Algorithm Based on Decision Variable Analyses for Multiobjective Optimization Problems With Large-Scale Variables (2016, methodology): “The interactions between two decision variables are sparse and focus on the mixed variable(s) for unconstrained multiobjective objective functions (UF) problems [13] of IEEE Congress on Evolutionary Computation (CEC) 2009 competition.”
Two-Archive Evolutionary Algorithm for Constrained Multiobjective Optimization (2017, background): “, problems with complicated PSs [43] and imbalanced convergence and diversity [44]), dynamic optimization (e.”
An Evolutionary Many-Objective Optimization Algorithm Based on Dominance and Decomposition (2015, background): “In the future, it is interesting to investigate the performance of MOEA/DD for a wider range of problems, such as problems with complicated PS shapes (see [40], [72], [73]), combinatorial optimization problems (see [18], [46], [74]), and problems in real-world with a large number of objectives.”
Evolutionary Constrained Multiobjective Optimization: Test Suite Construction and Performance Comparisons (2019, background): “Following the framework of CTPs, Zhang et al. [19] constructed ten test problems (called CFs).”