Constrained multiobjective optimization has gained much interest in the past few years. However, constrained multiobjective optimization problems (CMOPs) are still unsatisfactorily understood. Consequently, the choice of adequate CMOPs for benchmarking is difficult and lacks a formal background. This paper addresses this issue by exploring CMOPs from a performance space perspective. First, it presents a novel performance assessment approach designed explicitly for constrained multiobjective optimization. This methodology offers a first attempt to simultaneously measure the performance in approximating the Pareto front and constraint satisfaction. Secondly, it proposes an approach to measure the capability of the given optimization problem to differentiate among algorithm performances. Finally, this approach is used to contrast eight frequently used artificial test suites of CMOPs. The experimental results reveal which suites are more efficient in discerning between three well-known multiobjective optimization algorithms. Benchmark designers can use these results to select the most appropriate CMOPs for their needs.
National University of Defense Technology（国防科技大学）；Hunan Key Laboratory of Multi-energy System Intelligent Interconnection Technology（多能系统智能互联技术湖南省重点实验室）；Xiangjiang Laboratory（湘江实验室）；Tsinghua University（清华大学）
RDEx-CMOP: Feasibility-Aware Indicator-Guided Differential Evolution for Fixed-Budget Constrained Multiobjective Optimization
University of Toyama（富山大学）；Tohoku University（东北大学）；Hirosaki University（弘前大学）；Tongji University（同济大学）；University of Electronic Science and Technology of China（电子科技大学）；Chongqing Polytechnic University of Electronic Technology（重庆电子科技职业大学）；Yantai Vocational College（烟台职业学院）
A Detection Region Method-Based Evolutionary Algorithm for Binary Constrained Multiobjective Optimization
National University of Defense Technology（国防科技大学）；Hunan Key Laboratory of Multi-energy System Intelligent Interconnection Technology（湖南省多能系统智能互联技术重点实验室）；Xiangjiang Laboratory（湘江实验室）；Tsinghua University（清华大学）
Proven Advantage of Multiobjective Evolutionary Algorithms for Problems with Different Degrees of Conflict
School of Computer Science and Technology, International Research Institute for Artificial Intelligence, Harbin Institute of Technology, Shenzhen, China（哈尔滨工业大学深圳，计算机科学与技术学院，人工智能国际研究院）
A Novel Immune Algorithm for Multiparty Multiobjective Optimization
Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies（广东省新型安全智能技术重点实验室）；School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳）计算机科学与技术学院）；Peng Cheng Laboratory（鹏城实验室）；School of Artificial Intelligence, Nanjing University of Information Science and Technology（南京信息工程大学人工智能学院）；School of Computer Science and Engineering, Southern University of Science and Technology（南方科技大学计算机科学与工程系）
RDEx-MOP: Indicator-Guided Reconstructed Differential Evolution for Fixed-Budget Multiobjective Optimization
Department of Engineering, University of Toyama（富山大学工学部）；Cyberscience Center, Tohoku University（东北大学网络科学中心）；Faculty of Science and Technology, Hirosaki University（弘前大学理工学部）；Sino-German College of Applied Sciences, Tongji University（同济大学中德工程学院）；School of Mechanical Engineering, Tongji University（同济大学机械工程学院）；Department of Information Engineering, Yantai Vocational College（烟台职业学院信息工程系）
Multi-task multi-constraint differential evolution with elite-guided knowledge transfer for coal mine integrated energy system dispatching
School of Information and Control Engineering, China University of Mining and Technology（中国矿业大学信息与控制工程学院）；School of Artificial Intelligence and Computer Science, Jiangnan University（江南大学人工智能与计算机学院）；School of Information Science and Technology, Qingdao University of Science and Technology（青岛科技大学信息科学与技术学院）
Multiobjective Evolutionary Component Effect on Algorithm behavior
IRIT - CNRS UMR5505, University of Toulouse（图卢兹大学IRIT（法国国家科学研究中心UMR5505））；University of Brasilia（巴西利亚大学）；University of Stirling（斯特林大学）；University of Tsukuba（筑波大学）
