Evolutionary Algorithms (EAs) are being routinely applied for a variety of optimization tasks, and real-parameter optimization in the presence of constraints is one such important area. During constrained optimization EAs often create solutions that fall outside the feasible region; hence a viable constraint- handling strategy is needed. This paper focuses on the class of constraint-handling strategies that repair infeasible solutions by bringing them back into the search space and explicitly preserve feasibility of the solutions. Several existing constraint-handling strategies are studied, and two new single parameter constraint-handling methodologies based on parent-centric and inverse parabolic probability (IP) distribution are proposed. The existing and newly proposed constraint-handling methods are first studied with PSO, DE, GAs, and simulation results on four scalable test-problems under different location settings of the optimum are presented. The newly proposed constraint-handling methods exhibit robustness in terms of performance and also succeed on search spaces comprising up-to 500 variables while locating the optimum within an error of 10$^{-10}$. The working principle of the IP based methods is also demonstrated on (i) some generic constrained optimization problems, and (ii) a classic `Weld' problem from structural design and mechanics. The successful performance of the proposed methods clearly exhibits their efficacy as a generic constrained-handling strategy for a wide range of applications.
Evolutionary Giant Tour for CVRP using NSE and ML Heuristic]{Evolutionary Giant Tour approach for CVRP using Node Shift Encoding and Machine Learning repair heuristic
University M’hamed Bougara of Boumerdes（布米尔达斯穆哈迈德布加拉大学）
A Reference Vector based Many-Objective Evolutionary Algorithm with Feasibility-aware Adaptation
Dalian University of Technology（大连理工大学）；Montréal Institute of Learning Algorithms (Mila)（蒙特利尔学习算法研究所（Mila））；McGill University（麦吉尔大学）；Washington University in St. Louis（圣路易斯华盛顿大学）；Nanyang Technological University（南洋理工大学）
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）
Handling Imbalanced Classification Problems With Support Vector Machines via Evolutionary Bilevel Optimization
Centro de Investigación en Matemáticas（数学研究中心）；Andalusian Research Institute in Data Science and Computational Intelligence（安达卢西亚数据科学与计算智能研究所）；University of Granada（格拉纳达大学）
RDEx-CSOP: Feasibility-Aware Reconstructed Differential Evolution with Adaptive epsilon-Constraint Ranking
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）
Joint UAV Activation and Placement for Post-Disaster Wireless Restoration via a Hybrid Quantum-Inspired Evolutionary Framework
Mohammed V University in Rabat（拉巴特穆罕默德五世大学）；Hassania School of Public Works (EHTP)（哈桑尼亚公共工程学院）；School of Management, Telecommunications and Computer Science (SUPMTI)（管理、电信与计算机科学学院）；Fordham University（福特汉姆大学）；Misr Higher Institute for Commerce and Computers（埃及商贸计算机高等学院）
Estimate Hitting Time by Hitting Probability for Elitist Evolutionary Algorithms
Nottingham Trent University（诺丁汉特伦特大学）；Southern University of Science and Technology（南方科技大学）；University of Nottingham Ningbo China（诺丁汉大学宁波校区）；Lingnan University（岭南大学）
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）
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）
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 Topological Improvement of the Overall Performance of Sparse Evolutionary Training: Motif-Based Structural Optimization of Sparse MLPs Project
Informatics Institute, University of Amsterdam（阿姆斯特丹大学信息学院）；Department of Computer Science and Technology, SUNY Empire State University（SUNY帝国州大学计算机科学与技术系）
