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A Chaotic Local Search-Based Particle Swarm Optimizer for Large-Scale Complex Wind Farm Layout Optimization

IEEE/CAA Journal of Automatica Sinica 2023年第5期10卷 1168-1180页

作者： Zhenyu Lei Shangce Gao Zhiming Zhang Haichuan Yang Haotian Li The authors are with the Faculty of Engineering University of ToyamaToyama 930-8555Japan

Wind energy has been widely applied in power generation to alleviate climate *** wind turbine layout of a wind farm is a primary factor of impacting power conversion efficiency due to the wake effect that reduces the power outputs of wind turbines located in *** farm layout optimization(WFLO)aims to reduce the wake effect for maximizing the power outputs of the wind ***,the wake effect among wind turbines increases significantly as the number of wind turbines increases in the wind farm,which severely affect power conversion *** heuristic algorithms suffer from issues of low solution quality and local optimum for large-scale WFLO under complex wind ***,a chaotic local search-based genetic learning particle swarm optimizer(CGPSO)is proposed to optimize large-scale WFLO *** is tested on four larger-scale wind farms under four complex wind scenarios and compares with eight state-of-the-art *** experiment results indicate that CGPSO significantly outperforms its competitors in terms of performance,stability,and *** be specific,a success and failure memories-based selection is proposed to choose a chaotic map for chaotic search *** improves the solution *** parameter and search pattern of chaotic local search are also analyzed for WFLO problems.

关键词： Chaotic local search(CLS) evolutionary computation genetic learning particle swarm optimization(PSO) wake effect wind farm layout optimization(WFLO)

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An intelligent hyperheuristic algorithm for the berth allocation and scheduling problem at marine container terminals

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2025年 198卷

作者： Li, Bokang Afkhami, Payam Khayamim, Razieh Borowska-Stefanska, Marta Wisniewski, Szymon Fathollahi-Fard, Amir M. Ozkul, Seckin Dulebenets, Maxim A. Florida A&M Univ Florida State Univ FAMU FSU Coll Engn Dept Civil & Environm Engn 2035 E Paul Dirac DrSliger BldgSuite 275 Tallahassee FL 32310 USA Univ Lodz Fac Geog Sci PL-90142 Lodz Poland Univ Lodz Inst Built Environm & Spatial Policy Fac Geog Sci Kopcinskiego St 31 PL-90142 Lodz Poland Univ Lodz Res Ctr European Spatial Policy & Local Dev Kopcinskiego St 31 PL-90142 Lodz Poland Macau Univ Sci & Technol Fac Innovat Engn Dept Engn Sci Taipa Macau Peoples R China Univ S Florida Muma Coll Business Sch Mkt & Innovat 4202 E Fowler AveBSN 3403 Tampa FL 33620 USA

Berth allocation and scheduling at marine container terminals holds critical significance for optimizing port operations and ensuring efficient maritime logistics, yet it is exceptionally challenging due to its operational complexity and practical constraints. This study presents a mixed-integer linear programming model for the dynamic discrete berth allocation and scheduling problem with the objective to minimize the total turnaround cost and develop an efficient service schedule for each incoming vessel with a feasible service order and berthing position. A novel Hyperheuristic Hybridized with Exact Optimization (HHEO) is developed to explicitly solve the challenging decision problem studied herein. The HHEO algorithm is designed to dynamically select and apply different genetic operators based on their actual performance. Furthermore, intelligent exact optimization procedures specific to the domain of berth allocation and scheduling are periodically employed within the HHEO framework to improve solution quality and facilitate search for high-quality solutions. Detailed computational experiments are carried out to prove the superiority of the HHEO algorithm against solution methods inspired by exact optimization along with some well-known metaheuristic algorithms, demonstrating its efficiency and applicability for real-life berth planning at marine container terminals. Moreover, the experiments clearly demonstrate that incorporating a hyperheuristic framework along with problemspecific hybridization methods is critical for improving the exploratory and exploitative capabilities of the developed HHEO algorithm. Last but not least, valuable practical insights are uncovered through the proposed approach, offering reliable solutions that can support effective port management under different scenarios of berthing availability, vessel arrival intensity, and delayed vessel departure penalties.

关键词： Maritime transportation Berth allocation and scheduling Optimization evolutionary computation Hyperheuristic algorithms Hybridization

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Machine Learning-Enabled evolutionary Two-Stage Stochastic Programming

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IEEE TRANSACTIONS ON EMERGING TOPICS IN computationAL INTELLIGENCE 2025年

作者： Pan, Jeng-Shyang Song, Pei-Cheng Chu, Shu-Chuan Snasel, Vaclav Watada, Junzo Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing Peoples R China Chaoyang Univ Technol Dept Informat Management Taichung Taiwan Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Peoples R China VSB Tech Univ Ostrava Dept Comp Sci Ostrava Czech Republic Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Japan

Two-stage Stochastic Programming (2SP) is an effective framework for decision-making and modeling under uncertainty. Some 2SP problems are challenging due to their high dimensionality and nonlinearity. Machine learning can assist in solving 2SP problems by providing data-driven insights and approximations. evolutionary algorithms are more general and effective methods for handling various 2SP problems by exploiting their structures and features. However, there is still a research gap in combining machine learning and evolutionary algorithms for solving 2SP problems. Therefore, this paper proposes for the first time a Machine Learning-enabled evolutionary 2SP framework (MLE2SP), which uses machine learning to construct surrogate-assisted evolutionary optimization frameworks for 2SP. It constructs a novel multi-output 2SP surrogate model that considers scenarios and decision variables of both stages for the first time and proposes a data conversion method to handle the high-dimensional decision variables and scenarios. It also proposes a Machine Learning-enabled Differential Evolution Sampling (MLDES) method to update candidate solutions, which extracts knowledge from dominant candidate solutions to guide the evolutionary direction. Moreover, this work provides open sources of linear and nonlinear two-stage stochastic mixed-integer programming problem instances as benchmark test functions. The effectiveness and generality of the proposed algorithm and framework are verified by the test results on the benchmark test functions and a disaster relief logistics problem, which provide a new research direction for designing general and effective two-stage stochastic programming solving frameworks.

关键词： Stochastic processes Programming Machine learning evolutionary computation Machine learning algorithms Linear programming Random variables Metaheuristics Heuristic algorithms Decision making Stochastic programming machine learning evolutionary computation surrogate model uncertainty

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A Low-Complexity Large-MIMO Approximate Message Passing Detector

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第5期73卷 7384-7388页

作者： Ge, Yingmeng Wang, Haibo Zhang, Zaichen Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Southeast Univ Frontiers Sci Ctr Mobile Informat Commun & Secur Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China

Message passing algorithms (MPAs) are iterative algorithms widely used in multiple-input multiple-output (MIMO) detection, benefiting from their good trade-off between performance and complexity. In this paper, a low-complexity large-MIMO approximate message passing (LC-LAMA) detector with partial message updates is proposed. Also, we propose an evolutionary algorithm (EA) based method to optimize the number of updated messages in LC-LAMA for multiple scenarios. Numerical results exhibit that the proposed LC-LAMA detector presents comparable performance to that of state-of-the-art (SOA) detectors. The superior performance of LC-LAMA in mismatch scenarios illustrates its robustness. An analysis of complexity reveals the proposed LC-LAMA provides at most 72.7% complexity reduction as compared to other SOA detectors.

关键词： Detectors MIMO communication Complexity theory Optimization Message passing Transmitting antennas evolutionary computation Multiple-input multiple-output (MIMO) detector approximate message passing partial message updates evolutionary algorithm (EA)

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Clustered Orienteering Problem with Function-Based Rewards

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IEEE LATIN AMERICA TRANSACTIONS 2025年第6期23卷 472-478页

作者： Faria, Vinicius L. C. Macharet, Douglas G. Univ Fed Minas Gerais Comp Vis & Robot Lab VeRLab Dept Comp Sci Belo Horizonte Brazil

The Orienteering Problem is a well-studied problem that aims to determine an optimal route that maximizes the cumulative rewards while attaining a predefined travel budget. This classical formulation has been generalized in many forms, including grouping customers (locations) into clusters. However, such cluster-based formulation typically demands visiting all customers to collect the reward, thereby restricting its applicability. In this paper, we introduce a novel variant in which rewards associated with a cluster can be partially collected following a function that correlates with the number of customers attended within the cluster. This new formulation is a suitable alternative to model scenarios where the reward of the clusters can exhibit distinct and unique scaling patterns. We propose using an evolutionary algorithm to solve this problem and evaluate its performance considering different scenarios and aspects. The results demonstrate that our method effectively selects an appropriate number of clients to attend to based on the assigned function for each cluster.

关键词： Routing Urban areas Genetic algorithms Indexes evolutionary computation Gaussian distribution Vectors Probability Codes Buildings Autonomous Agents Orienteering Problem Vehicle Routing evolutionary Algorithms

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Utilizing a Bounding Procedure Based on Simulated Annealing to Effectively Locate the Bounds for the Parameters of Radial Basis Function Networks

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ALGORITHMS 2025年第4期18卷 234-234页

作者： Tsoulos, Ioannis G. Charilogis, Vasileios Tsalikakis, Dimitrios Univ Ioannina Dept Informat & Telecommun Ioannina 45110 Greece Univ Western Macedonia Dept Engn Informat & Telecommun Kozani 50100 Greece

Radial basis function (RBF) networks are an established parametric machine learning tool which has been extensively utilized in data classification and data fitting problems. These specific machine learning tools have been applied in various scientific areas, such as problems in physics, chemistry, and medicine, with excellent results. A two-step technique is usually used to adjust the parameters of these models, which is in most cases extremely effective. However, it does not effectively explore the value space of the network parameters and often results in parameter stability problems. In this paper, the use of a bounding technique that explores the value space of the parameters of these networks using intervals generated by a procedure based on the Simulated Annealing method is recommended. After finding a promising range of values for the network parameters, a genetic algorithm is applied within this range of values to more effectively adjust its parameters. The new method was applied on a wide range of classification and regression datasets from the relevant literature and the results are reported in the current manuscript.

关键词： radial basis function networks simulated annealing stochastic techniques evolutionary computation

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Multi-objective optimization of flight schedules to maximize constraint tolerance by local search and archive mechanisms

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ARTIFICIAL LIFE AND ROBOTICS 2025年第2期30卷 289-302页

作者： Ishizuka, Tomoki Murata, Akinori Sato, Hiroyuki Takadama, Keiki Univ Electrocommun Dept Informat Tokyo Japan Elect Nav Res Inst Tokyo Japan Univ Tokyo Informat Technol Ctr Tokyo Japan

To introduce the concept of the "constraint tolerance" (i.e., a feasibility of solutions) in the flight scheduling problem, this paper proposes the optimization method that can find the feasible flight schedules by optimizing the original objective function while maximizing the constraint tolerance as much as possible. The proposed method further is improved by integrating it with the local search and archive mechanisms to obtain a wide range of Pareto-optimal solutions with a high constraint tolerance. A comparison between the proposed method and the conventional methods with or without adding a new objective function to maximize the constraint tolerance shows the statistical superiority of the proposed method.

关键词： Multi-objective optimization evolutionary computation Constraint handling Constraint tolerance Flight schedule

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Variable interaction network analysis to enhance boundary update method for constrained optimization

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RESULTS IN ENGINEERING 2025年 25卷

作者： Rahimi, Iman Yazdanjue, Navid Khorshidi, Mohammad Sadegh Nikoo, Mohammad Reza Chen, Fang Gandomi, Amir H. Univ Technol Sydney Data Sci Inst Fac Engn & Informat Technol Ultimo Australia Sultan Qaboos Univ Dept Civil & Architectural Engn Muscat Oman Obuda Univ Univ Res & Innovat Ctr EKIK H-1034 Budapest Hungary

The boundary update approach was proposed by Gandomi and Deb [Computer Methods in Applied Mechanics and Engineering, 363, 112,917, 2020] for constrained optimization problems. The boundary update (BU) method defines a dynamic formulation of an optimization problem in order to eliminate the infeasible search space. This study investigates the concept of variable interaction using a differential grouping algorithm, DG2, to evaluate and visualize variable interaction impacted by the boundary update method. Using multiple social network analysis (SNA) metrics, including Average Betweenness Centrality, Average Closeness Centrality, Network Density, and Clustering Coefficient, this research reveals significant structural simplifications in optimization problems under the BU method. Results show significant improvements in some aspects by applying the BU method. For example, it reduces network density by up to 34 %, closeness centrality by over 67 %, and enhances independence among variables by 45 %, simplifying the optimization landscape. Furthermore, a systematic evaluation using the TOPSIS multi-criteria decision-making (MCDM) approach confirms that BU improves convergence efficiency and solution quality by 20%-30 % compared to without BU methods across various benchmark problems. Network visualizations corroborate these findings, demonstrating reduced complexity and improved clarity in variable relationships. This comprehensive analysis establishes the BU method as a transformative framework, significantly advancing constrained optimization through its ability to streamline variable interactions and enhance algorithmic performance.

关键词： Boundary update Variable interaction Network analysis Optimization evolutionary computation

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Constrained Multi-Objective Optimization With Constraint Priority

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IEEE TRANSACTIONS ON EMERGING TOPICS IN computationAL INTELLIGENCE 2025年

作者： Zhou, Xinyu Zhu, Yanjun Wang, Yizhi Sun, Ruiqing Zou, Juan Jiangxi Normal Univ Sch Comp & Informat Engn Nanchang 330022 Peoples R China Ganzhou Open Univ Ganzhou 341000 Peoples R China Natl Univ Def Technol Coll Comp Changsha 410073 Peoples R China Xiangtan Univ Key Lab Intelligent Comp & Informat Proc Minist Educ China Xiangtan 411105 Peoples R China

Constrained multi-objective problems (CMOPs) are tricky, because it is difficult to handle multiple objectives and constraints simultaneously. Most existing algorithms perform well on CMOPs with a single constraint or multiple simple constraints, but their performance on CMOPs with multiple complex constraints often deteriorates. Generally, for CMOPs, it is much easier to deal with a single constraint than with multiple constraints. Therefore, this paper proposes a strategy for identifying the constraint priority, and then handles the constraints one by one according to the priority. Specifically, for each constraint, an associated population is first established to exclusively consider this constraint, and then the corresponding infeasibility rates of all the constraints are calculated to capture the constraint priority. After that, based on the constraint priority, the roulette selection method is used to determine which constraint should be handled first, which ensures the constraint with high priority has more chances to be handled. Experimental results on five benchmark suites and three real-world problems show that our approach has significant advantages over five state-of-the-art algorithms.

关键词： Optimization Convergence Linear programming Classification algorithms Benchmark testing Sun Fans evolutionary computation Vectors Training evolutionary algorithm constraint priority constrained multi-objective optimization

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A Fast Clustering Based evolutionary Algorithm for Super-Large-Scale Sparse Multi-Objective Optimization

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 1048-1063页

作者： Ye Tian Yuandong Feng Xingyi Zhang Changyin Sun Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information TechnologyAnhui UniversityHefei 230601China School of Computer Science and Technology Anhui UniversityHefei 230601China School of Artificial Intelligence Anhui UniversityHefei 230601China School of Automation Southeast UniversityNanjing 210096China

During the last three decades,evolutionary algorithms(EAs)have shown superiority in solving complex optimization problems,especially those with multiple objectives and non-differentiable ***,due to the stochastic search strategies,the performance of most EAs deteriorates drastically when handling a large number of decision *** tackle the curse of dimensionality,this work proposes an efficient EA for solving super-large-scale multi-objective optimization problems with sparse optimal *** proposed algorithm estimates the sparse distribution of optimal solutions by optimizing a binary vector for each solution,and provides a fast clustering method to highly reduce the dimensionality of the search *** importantly,all the operations related to the decision variables only contain several matrix calculations,which can be directly accelerated by *** existing EAs are capable of handling fewer than 10000 real variables,the proposed algorithm is verified to be effective in handling 1000000 real ***,since the proposed algorithm handles the large number of variables via accelerated matrix calculations,its runtime can be reduced to less than 10%of the runtime of existing EAs.

关键词： evolutionary computation fast clustering sparse multi-objective optimization super-large-scale optimization

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