Real-world constrained multi-objective optimization problems (CMOPs) commonly involve multiple constraints, and understanding and exploiting their coupling relationships is crucial for efficient optimization. Recent constraint-decoupling methods handle individual constraints separately, but they generally search only in the evolutionary direction to approximate single-constraint Pareto fronts (SCPFs). In this study, we show that part or all of the constrained Pareto front (CPF) may be unrelated to any SCPF and instead be shaped by the boundaries of infeasible regions. We refer to such a portion as the independent CPF (ICPF) and introduce the reverse CPF (RCPF) to characterize its associated informative infeasible boundaries. Based on these observations, we propose a bidirectional constraint-decoupling coevolutionary algorithm named DCF2D. DCF2D dynamically identifies the constraints obstructing the main population and activates constraint-specific auxiliary populations. These populations adaptively search in the evolutionary direction for the corresponding SCPFs or in the reverse evolutionary direction for the corresponding RCPFs. Its three-stage framework integrates unconstrained global exploration, event-driven bidirectional coevolution, and final convergence refinement. Experiments on 87 benchmark instances from seven test suites and 28 real-world engineering CMOPs demonstrate that DCF2D achieves the best overall performance among nine algorithms. Code available at: https://github.com/RuiqingS/DCF2D.
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）
Runtime Analysis of Single- and Multi-Objective Evolutionary Algorithms for Chance Constrained Optimization Problems with Normally Distributed Random Variables
Multi-Objective Evolutionary Optimization of Chance-Constrained Multiple-Choice Knapsack Problems with Implicit Probability Distributions
Southern University of Science and Technology（南方科技大学）；Central China Normal University（华中师范大学）；Institute of High Performance Computing, Agency for Science Technology and Research（新加坡科技研究局高性能计算研究所）；Nanyang Technological University（南洋理工大学）
Multi-Constrained Evolutionary Molecular Design Framework: An Interpretable Drug Design Method Combining Rule-Based Evolution and Molecular Crossover
Graduate School of Technology, Industrial and Social Sciences, Tokushima University（德岛大学技术、产业与社会科学研究院）；College of Chemistry and Chemical Engineering, China University of Petroleum (East China)（中国石油大学（华东）化学与化工学院）
MOEA/D with Angle-based Constrained Dominance Principle for Constrained Multi-objective Optimization Problems
提出了一种新的约束处理机制--基于角度的约束优势原理（ACDP），并将其嵌入到基于分解的多目标进化算法（MOEA/D）中，用于求解约束多目标优化问题（CMOPs）. ACDP在进化过程中利用解的角度信息来调整解的优势关系，以保持工作种群的多样性。本文使用14个基准测试实例来评估MOEA/D with ACDP（MOEA/D-ACDP）的性能。此外，还对一个工程优化问题（工字梁优化问题）进行了优化。通过上述基准测试和工程应用，对提出的MOEA/D-ACDP以及其他四种基于分解的C-MOEA/D、MOEA/D-CDP、MOEA/D-Epoxy和MOEA/D-SR进行了测试。实验结果表明，MOEA/D-ACDP在这些测试实例和真实案例上的性能明显优于其他四种CMOEA，表明ACDP在求解CMOPs问题上更有效。
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）
Ranking Constraints via Topological Dual-Directional Search in Evolutionary Multi-Objective Optimization
National Key Laboratory of Parallel and Distributed Computing, College of Computer Science and Technology, National University of Defense Technology（国防科技大学计算机科学与技术学院并行与分布式计算国家重点实验室）；College of System Engineering, National University of Defense Technology（国防科技大学系统工程学院）；College of Intelligence Science and Technology, National University of Defense Technology（国防科技大学智能科学学院）；State Key Laboratory of Complex & Critical Software Environment, College of Information and Communication, National University of Defense Technology（国防科技大学信息与通信学院复杂与关键软件环境国家重点实验室）
Decoupling Numerical and Structural Parameters: An Empirical Study on Adaptive Genetic Algorithms via Deep Reinforcement Learning for the Large-Scale TSP
School of Mathematics（数学学院）；Statistics（统计学）；Xi'an Jiaotong University（西安交通大学）；School of Mechanical Engineering（机械工程学院）；Frontier Institute of Science（前沿科学研究院）；Technology（技术）；Xi'an, China（中国西安）
Robust Dynamic Material Handling via Adaptive Constrained Evolutionary Reinforcement Learning
Eindhoven University of Technology（埃因霍温理工大学）；Southern University of Science and Technology（南方科技大学）；Lingnan University（岭大大学）；Hong Kong Polytechnic University（香港理工大学）
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）
