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Hyper-heuristic Evolutionary Algorithm Utilizing Q-learning for Addressing the Distributed Flexible Job-Shop Scheduling Problem in the Context of Worker Absenteeism 认领 引用
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作者 Zhiqing Li Yongquan Zhou Qifang Luo 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第3期1932-1969,共38页
The Distributed Flexible Job Shop Scheduling Problem(DFJSP)is critical in modern manufacturing;however,existing research has not sufficiently addressed dynamic disturbances,particularly unexpected worker absences.This... The Distributed Flexible Job Shop Scheduling Problem(DFJSP)is critical in modern manufacturing;however,existing research has not sufficiently addressed dynamic disturbances,particularly unexpected worker absences.This study extends the DFJSPW model to introduce an enhanced framework,DFJSPWA,which optimizes maximum makespan,worker workload,and total energy consumption by integrating worker load factors with random absenteeism.To solve this complex problem,we propose a Q-learning-based Hyper-heuristic Evolutionary Algorithm(QLHHEA).This algorithm features a segmented encoding scheme that implicitly captures absenteeism information,utilizing a decoding process tailored for both standard and rescheduling contexts.Additionally,we construct a pool of twelve efficient Low-Level Heuristics(LLHs)combined with Q-learning to enable the adaptive selection of operators.Furthermore,a Hybrid Rescheduling Method(HRM)is developed,employing three response strategies based on worker status and the urgency of the absenteeism.Comprehensive experiments on 58 benchmark instances demonstrate that QLHHEA significantly outperforms six established algorithms,including MOEA/D and NSGA-II.Statistical validation confirms the superiority of the proposed method.This research provides a robust theoretical and methodological framework for improving scheduling efficiency and resource utilization in distributed production systems facing worker absenteeism. 展开更多
关键词 Worker absenteeism Distributed flexible job shop scheduling problem Hyper-heuristic evolutionary algorithm Dynamic rescheduling Q-learning
An Efficient Evolutionary Algorithm for Few-for-Many Optimization 认领 引用
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作者 Ke Shang Hisao Ishibuchi +1 位作者 Zexuan Zhu Qingfu Zhang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1362-1377,共16页
Few-for-many(F4M)optimization,recently introduced as a novel paradigm in multi-objective optimization,aims to find a small set of solutions that effectively handle a large number of conflicting objectives.Unlike tradi... Few-for-many(F4M)optimization,recently introduced as a novel paradigm in multi-objective optimization,aims to find a small set of solutions that effectively handle a large number of conflicting objectives.Unlike traditional many-objective optimization methods,which typically attempt comprehensive coverage of the Pareto front,F4M optimization emphasizes finding a small representative solution set to efficiently address highdimensional objective spaces.Motivated by the computational complexity and practical relevance of F4M optimization,this paper proposes a new evolutionary algorithm explicitly tailored for efficiently solving F4M optimization problems.Inspired by Smetric selection evolutionary multi-objective optimization algorithm(SMS-EMOA),our proposed approach employs a(μ+1)-evolution strategy guided by the objective of F4M optimization.Furthermore,to facilitate rigorous performance assessment,we propose a novel benchmark test suite specifically designed for F4M optimization by leveraging the similarity betw een the R2indicator and F4M formulations.Our test suite is highly flexible,allowing any existing multi-objective optimization problem to be transformed into a corresponding F4M instance via scalarization using the weighted Tchebycheff function.Comprehensive experimental evaluations on benchmarks demonstrate the superior performance of our algorithm compared to existing state-of-the-art algorithms,especially on instances involving a large number of objectives.The source code of the proposed algorithm will be released publicly.Source code is available at http://gffzz188fe103f8f1460asqqconcboxcqv6n95.ffgz.tsg.suse.edu.cn/MOL-SZU/SoM-EMOA. 展开更多
关键词 Evolutionary algorithm few-for-many optimization many-objective optimization (MOO) multi-objective optimization
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Many-objective evolutionary algorithms based on reference-point-selection strategy for application in reactor radiation-shielding design 认领 引用
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作者 Cheng-Wei Liu Ai-Kou Sun +4 位作者 Ji-Chong Lei Hong-Yu Qu Chao Yang Tao Yu Zhen-Ping Chen 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2025年第6期201-215,共15页
In recent years,the development of new types of nuclear reactors,such as transportable,marine,and space reactors,has presented new challenges for the optimization of reactor radiation-shielding design.Shielding struct... In recent years,the development of new types of nuclear reactors,such as transportable,marine,and space reactors,has presented new challenges for the optimization of reactor radiation-shielding design.Shielding structures typically need to be lightweight,miniaturized,and radiation-protected,which is a multi-parameter and multi-objective optimization problem.The conventional multi-objective(two or three objectives)optimization method for radiation-shielding design exhibits limitations for a number of optimization objectives and variable parameters,as well as a deficiency in achieving a global optimal solution,thereby failing to meet the requirements of shielding optimization for newly developed reactors.In this study,genetic and artificial bee-colony algorithms are combined with a reference-point-selection strategy and applied to the many-objective(having four or more objectives)optimal design of reactor radiation shielding.To validate the reliability of the methods,an optimization simulation is conducted on three-dimensional shielding structures and another complicated shielding-optimization problem.The numerical results demonstrate that the proposed algorithms outperform conventional shielding-design methods in terms of optimization performance,and they exhibit their reliability in practical engineering problems.The many-objective optimization algorithms developed in this study are proven to efficiently and consistently search for Pareto-front shielding schemes.Therefore,the algorithms proposed in this study offer novel insights into improving the shielding-design performance and shielding quality of new reactor types. 展开更多
关键词 Many-objective optimization problem Evolutionary algorithm Radiation-shielding design Reference-point-selection strategy
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Adaptive Meta-Loss Networks:Learning Task-Agnostic Loss Functions via Evolutionary Optimization 认领 引用
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作者 Mirna Yunita Xiabi Liu +1 位作者 Zhaoyang Hai Rachmat Muwardi 《Computers, Materials & Continua》 SCIE EI 2026年第5期1931-1949,共19页
Designing appropriate loss functions is critical to the success of supervised learning models.However,most conventional losses are fixed and manually designed,making them suboptimal for diverse and dynamic learning sc... Designing appropriate loss functions is critical to the success of supervised learning models.However,most conventional losses are fixed and manually designed,making them suboptimal for diverse and dynamic learning scenarios.In this work,we propose an Adaptive Meta-Loss Network(Adaptive-MLN)that learns to generate taskagnostic loss functions tailored to evolving classification problems.Unlike traditional methods that rely on static objectives,Adaptive-MLN treats the loss function itself as a trainable component,parameterized by a shallow neural network.To enable flexible,gradient-free optimization,we introduce a hybrid evolutionary approach that combines GeneticAlgorithms(GA)for global exploration and Evolution Strategies(ES)for local refinement.This co-evolutionary process dynamically adjusts the loss landscape,improvingmodel generalization without relying on analytic gradients or handcrafted heuristics.Experimental evaluations on synthetic tasks and the CIFAR-10 andMNIST datasets demonstrate that our approach consistently outperforms standard losses such as Cross-Entropy and Mean Squared Error in terms of accuracy,convergence,and adaptability. 展开更多
关键词 Meta-learning adaptive loss function task-agnostic optimization evolutionary strategy genetic algorithm classification
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Feature Selection Optimisation for Cancer Classification Based on Evolutionary Algorithms:An Extensive Review 认领 引用
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作者 Siti Ramadhani Lestari Handayani +4 位作者 Theam Foo Ng Sumayyah Dzulkifly Roziana Ariffin Haldi Budiman Shir Li Wang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第6期2711-2765,共55页
In recent years,feature selection(FS)optimization of high-dimensional gene expression data has become one of the most promising approaches for cancer prediction and classification.This work reviews FS and classificati... In recent years,feature selection(FS)optimization of high-dimensional gene expression data has become one of the most promising approaches for cancer prediction and classification.This work reviews FS and classification methods that utilize evolutionary algorithms(EAs)for gene expression profiles in cancer or medical applications based on research motivations,challenges,and recommendations.Relevant studies were retrieved from four major academic databases-IEEE,Scopus,Springer,and ScienceDirect-using the keywords‘cancer classification’,‘optimization’,‘FS’,and‘gene expression profile’.A total of 67 papers were finally selected with key advancements identified as follows:(1)The majority of papers(44.8%)focused on developing algorithms and models for FS and classification.(2)The second category encompassed studies on biomarker identification by EAs,including 20 papers(30%).(3)The third category comprised works that applied FS to cancer data for decision support system purposes,addressing high-dimensional data and the formulation of chromosome length.These studies accounted for 12%of the total number of studies.(4)The remaining three papers(4.5%)were reviews and surveys focusing on models and developments in prediction and classification optimization for cancer classification under current technical conditions.This review highlights the importance of optimizing FS in EAs to manage high-dimensional data effectively.Despite recent advancements,significant limitations remain:the dynamic formulation of chromosome length remains an underexplored area.Thus,further research is needed on dynamic-length chromosome techniques for more sophisticated biomarker gene selection techniques.The findings suggest that further advancements in dynamic chromosome length formulations and adaptive algorithms could enhance cancer classification accuracy and efficiency. 展开更多
关键词 Feature selection(FS) gene expression profile(GEP) cancer classification evolutionary algorithms(EAs) dynamic-length chromosome
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MH-Raft:an efficient and low-latency consensus algorithm for distributed systems viaMOEA/D and hybrid hierarchical clustering 认领 引用
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作者 Fei ZHAO Guilong PENG Tianyi ZANG 《ENGINEERING Information Technology & Electronic Engineering》 SCIE EI CSCD 2026年第5期34-48,共15页
Raft is a foundational consensus protocol for distributed systems,architected to ensure state machine replication and data consistency across machine clusters.However,traditional Raft faces significant performance bot... Raft is a foundational consensus protocol for distributed systems,architected to ensure state machine replication and data consistency across machine clusters.However,traditional Raft faces significant performance bottlenecks,particularly regarding suboptimal election efficiency and substantial consensus latency in large-scale deployments.To address these challenges,this study presents MH-Raft,an enhanced consensus variant designed for high efficiency and minimal latency.We propose a hierarchical node management and election framework to optimize network coordination.Specifically,a leader election methodology leveraging the multi-objective evolutionary algorithm based on decomposition(MOEA/D)is formulated to minimize election latency by evaluating multi-dimensional node attributes.To further refine the proposed hierarchical architecture,a rigorous tightness definition is devised for optimal mediator node selection,which is integrated into a hybrid clustering algorithm that adaptively partitions the network and optimizes the mapping between mediator nodes and follower nodes.Quantitative evaluations via comprehensive experiments demonstrate that MH-Raft significantly reduces overall election latency and lowers consensus latency by 14.87%–34.45%,while enhancing average throughput by 30.43%compared to the conventional Raft implementation. 展开更多
关键词 Consensus algorithm Blockchain Multi-objective evolutionary algorithm Distributed systems
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A Review of the Evolution of Multi-Objective Evolutionary Algorithms 认领 引用 被引量：1
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作者 Thomas Hanne Mohammad Jahani Moghaddam 《Computers, Materials & Continua》 SCIE EI 2025年第12期4203-4236,共34页
Multi-Objective Evolutionary Algorithms(MOEAs)have significantly advanced the domain of MultiObjective Optimization(MOO),facilitating solutions for complex problems with multiple conflicting objectives.This review exp... Multi-Objective Evolutionary Algorithms(MOEAs)have significantly advanced the domain of MultiObjective Optimization(MOO),facilitating solutions for complex problems with multiple conflicting objectives.This review explores the historical development of MOEAs,beginning with foundational concepts in multi-objective optimization,basic types of MOEAs,and the evolution of Pareto-based selection and niching methods.Further advancements,including decom-position-based approaches and hybrid algorithms,are discussed.Applications are analyzed in established domains such as engineering and economics,as well as in emerging fields like advanced analytics and machine learning.The significance of MOEAs in addressing real-world problems is emphasized,highlighting their role in facilitating informed decision-making.Finally,the development trajectory of MOEAs is compared with evolutionary processes,offering insights into their progress and future potential. 展开更多
关键词 Multi-objective optimization evolutionary algorithms Pareto-based selection decomposition-based methods advanced analytics
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Particle Swarm Optimization Algorithm for Feature Selection Inspired by Peak Ecosystem Dynamics 认领 引用
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作者 Shaobo Deng Meiru Xie +3 位作者 Bo Wang Shuaikun Zhang Sujie Guan Min Li 《Computers, Materials & Continua》 SCIE EI 2025年第2期2723-2751,共29页
In recent years, particle swarm optimization (PSO) has received widespread attention in feature selection due to its simplicity and potential for global search. However, in traditional PSO, particles primarily update ... In recent years, particle swarm optimization (PSO) has received widespread attention in feature selection due to its simplicity and potential for global search. However, in traditional PSO, particles primarily update based on two extreme values: personal best and global best, which limits the diversity of information. Ideally, particles should learn from multiple advantageous particles to enhance interactivity and optimization efficiency. Accordingly, this paper proposes a PSO that simulates the evolutionary dynamics of species survival in mountain peak ecology (PEPSO) for feature selection. Based on the pyramid topology, the algorithm simulates the features of mountain peak ecology in nature and the competitive-cooperative strategies among species. According to the principles of the algorithm, the population is first adaptively divided into many subgroups based on the fitness level of particles. Then, particles within each subgroup are divided into three different types based on their evolutionary levels, employing different adaptive inertia weight rules and dynamic learning mechanisms to define distinct learning modes. Consequently, all particles play their respective roles in promoting the global optimization performance of the algorithm, similar to different species in the ecological pattern of mountain peaks. Experimental validation of the PEPSO performance was conducted on 18 public datasets. The experimental results demonstrate that the PEPSO outperforms other PSO variant-based feature selection methods and mainstream feature selection methods based on intelligent optimization algorithms in terms of overall performance in global search capability, classification accuracy, and reduction of feature space dimensions. Wilcoxon signed-rank test also confirms the excellent performance of the PEPSO. 展开更多
关键词 Machine learning feature selection evolutionary algorithm particle swarm optimization
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Evolutionary Algorithm Based on Surrogate and Inverse Surrogate Models for Expensive Multiobjective Optimization 认领 引用
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作者 Qi Deng Qi Kang +4 位作者 MengChu Zhou Xiaoling Wang Shibing Zhao Siqi Wu Mohammadhossein Ghahramani 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第5期961-973,共13页
When dealing with expensive multiobjective optimization problems,majority of existing surrogate-assisted evolutionary algorithms(SAEAs)generate solutions in decision space and screen candidate solutions mostly by usin... When dealing with expensive multiobjective optimization problems,majority of existing surrogate-assisted evolutionary algorithms(SAEAs)generate solutions in decision space and screen candidate solutions mostly by using designed surrogate models.The generated solutions exhibit excessive randomness,which tends to reduce the likelihood of generating good-quality solutions and cause a long evolution to the optima.To improve SAEAs greatly,this work proposes an evolutionary algorithm based on surrogate and inverse surrogate models by 1)Employing a surrogate model in lieu of expensive(true)function evaluations;and 2)Proposing and using an inverse surrogate model to generate new solutions.By using the same training data but with its inputs and outputs being reversed,the latter is simple to train.It is then used to generate new vectors in objective space,which are mapped into decision space to obtain their corresponding solutions.Using a particular example,this work shows its advantages over existing SAEAs.The results of comparing it with state-of-the-art algorithms on expensive optimization problems show that it is highly competitive in both solution performance and efficiency. 展开更多
关键词 Expensives multi-objective optimization reverse model surrogate-assisted evolutionary algorithms(SAEAs)
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Multi-Firmware Comparison Based on Evolutionary Algorithm and Trusted Base Point 认领 引用
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作者 Wenbing Wang Yongwen Liu 《Computers, Materials & Continua》 SCIE EI 2025年第7期763-790,共28页
Multi-firmware comparison techniques can improve efficiency when auditing firmwares in bulk.How-ever,the problem of matching functions between multiple firmwares has not been studied before.This paper proposes a multi... Multi-firmware comparison techniques can improve efficiency when auditing firmwares in bulk.How-ever,the problem of matching functions between multiple firmwares has not been studied before.This paper proposes a multi-firmware comparison method based on evolutionary algorithms and trusted base points.We first model the multi-firmware comparison as a multi-sequence matching problem.Then,we propose an adaptation function and a population generation method based on trusted base points.Finally,we apply an evolutionary algorithm to find the optimal result.At the same time,we design the similarity of matching results as an evaluation metric to measure the effect of multi-firmware comparison.The experiments show that the proposed method outperforms Bindiff and the string-based method.Precisely,the similarity between the matching results of the proposed method and Bindiff matching results is 61%,and the similarity between the matching results of the proposed method and the string-based method is 62.8%.By sampling and manual verification,the accuracy of the matching results of the proposed method can be about 66.4%. 展开更多
关键词 Multi-firmware comparison evolutionary algorithm multi-sequence matching binary code comparison
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A Bi-Level Optimization Model and Hybrid Evolutionary Algorithm for Wind Farm Layout with Different Turbine Types 认领 引用
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作者 Erping Song Zipin Yao 《Energy Engineering》 EI 2025年第12期5129-5147,共19页
Wind farm layout optimization is a critical challenge in renewable energy development,especially in regions with complex terrain.Micro-siting of wind turbines has a significant impact on the overall efficiency and eco... Wind farm layout optimization is a critical challenge in renewable energy development,especially in regions with complex terrain.Micro-siting of wind turbines has a significant impact on the overall efficiency and economic viability of wind farm,where the wake effect,wind speed,types of wind turbines,etc.,have an impact on the output power of the wind farm.To solve the optimization problem of wind farm layout under complex terrain conditions,this paper proposes wind turbine layout optimization using different types of wind turbines,the aim is to reduce the influence of the wake effect and maximize economic benefits.The linear wake model is used for wake flow calculation over complex terrain.Minimizing the unit energy cost is taken as the objective function,considering that the objective function is affected by cost and output power,which influence each other.The cost function includes construction cost,installation cost,maintenance cost,etc.Therefore,a bi-level constrained optimization model is established,in which the upper-level objective function is to minimize the unit energy cost,and the lower-level objective function is to maximize the output power.Then,a hybrid evolutionary algorithm is designed according to the characteristics of the decision variables.The improved genetic algorithm and differential evolution are used to optimize the upper-level and lower-level objective functions,respectively,these evolutionary operations search for the optimal solution as much as possible.Finally,taking the roughness of different terrain,wind farms of different scales and different types of wind turbines as research scenarios,the optimal deployment is solved by using the algorithm in this paper,and four algorithms are compared to verify the effectiveness of the proposed algorithm. 展开更多
关键词 Bi-level optimization genetic algorithm differential evolution hybrid evolutionary algorithm wind farm layout
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Multiobjective evolutionary algorithm for dynamic nonlinear constrained optimization problems 认领 引用 被引量：2
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作者 Liu Chun'an Wang Yuping 《Journal of Systems Engineering and Electronics》 SCIE EI 2009年第1期204-210,共7页
A new method to solve dynamic nonlinear constrained optimization problems （DNCOP） is proposed. First, the time （environment） variable period of DNCOP is divided into several equal subperiods. In each subperiod, th... A new method to solve dynamic nonlinear constrained optimization problems （DNCOP） is proposed. First, the time （environment） variable period of DNCOP is divided into several equal subperiods. In each subperiod, the DNCOP is approximated by a static nonlinear constrained optimization problem （SNCOP）. Second, for each SNCOP, inspired by the idea of multiobjective optimization, it is transformed into a static bi-objective optimization problem. As a result, the original DNCOP is approximately transformed into several static bi-objective optimization problems. Third, a new multiobjective evolutionary algorithm is proposed based on a new selection operator and an improved nonuniformity mutation operator. The simulation results indicate that the proposed algorithm is effective for DNCOP. 展开更多
关键词 dynamic optimization nonlinear constrained optimization evolutionary algorithm optimal solutions
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A self-adaptive linear evolutionary algorithm for solving constrained optimization problems 认领 引用 被引量：1
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作者 Kezong TANG Jingyu YANG +1 位作者 Shang GAO Tingkai SUN 《控制理论与应用（英文版）》 2010年第4期533-539,共7页
In many real-world applications of evolutionary algorithms,the fitness of an individual requires a quantitative measure.This paper proposes a self-adaptive linear evolutionary algorithm （ALEA） in which we introduce ... In many real-world applications of evolutionary algorithms,the fitness of an individual requires a quantitative measure.This paper proposes a self-adaptive linear evolutionary algorithm （ALEA） in which we introduce a novel strategy for evaluating individual＇s relative strengths and weaknesses.Based on this strategy,searching space of constrained optimization problems with high dimensions for design variables is compressed into two-dimensional performance space in which it is possible to quickly identify ＇good＇ individuals of the performance for a multiobjective optimization application,regardless of original space complexity.This is considered as our main contribution.In addition,the proposed new evolutionary algorithm combines two basic operators with modification in reproduction phase,namely,crossover and mutation.Simulation results over a comprehensive set of benchmark functions show that the proposed strategy is feasible and effective,and provides good performance in terms of uniformity and diversity of solutions. 展开更多
关键词 Multiobjective optimization Evolutionary algorithms Pareto optimal solution Linear fitness function
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基于AEA-ITD3-MMC算法的核电蒸汽系统性能优化 认领 引用 被引量：1
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作者 董元发 张俊 +4 位作者 肖云龙 安友军 刘浩 张弦 郭鹏 《计算机集成制造系统》 EI CSCD 北大核心 2026年第1期115-130,共16页
针对核电蒸汽系统的高维函数优化问题,传统进化算法存在精度差、收敛速度慢和极易陷入局部最优等问题。为此,通过结合传统进化算法和深度强化学习,提出了一种包含多机制协同和改进TD3(ITD3)的自适应进化算法(AEA-ITD3-MMC)。首先,引入... 针对核电蒸汽系统的高维函数优化问题,传统进化算法存在精度差、收敛速度慢和极易陷入局部最优等问题。为此,通过结合传统进化算法和深度强化学习,提出了一种包含多机制协同和改进TD3(ITD3)的自适应进化算法(AEA-ITD3-MMC)。首先,引入基于多机制协同的种群重构策略,以增强初始种群的质量;其次,采用平衡优选策略,增强算法的全局探索能力和局部开发能力;然后,通过对标准TD3算法进行改进,设计了针对单目标函数优化问题的状态空间、动作空间、决策变量更新策略和自适应终止条件等,极大地提升了标准TD3算法的局部搜索能力;最后,设计了子代种群的生成策略,以保持子代种群的收敛性和多样性。在数值实验中,首先利用CEC2014和CEC2017测试函数集对AEA-ITD3-MMC算法的改进算子进行有效性分析,论证了所有改进算子的有效性;然后通过与传统进化算法进行对比,证明了AEA-ITD3-MMC算法在整体性能上显著优于10种经典进化算法;最后将AEA-ITD3-MMC算法应用于某核电蒸汽系统的高维决策变量优化问题上,进一步论证了该算法在工程应用中的优越性。 展开更多
关键词 自适应进化算法 TD3算法 高维函数优化 核电蒸汽系统性能优化
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改进的MOEA/D算法求解考虑机器恶化效应的柔性流水车间节能调度问题 认领 引用 被引量：2
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作者 卫晨昊 李敬敏 +1 位作者 李韵辰 胡晓兵 《计算机集成制造系统》 EI CSCD 北大核心 2026年第2期585-598,共14页
针对考虑机器恶化效应的柔性流水车间节能调度问题,提出一种改进的基于分解的多目标进化算法(MMODEA/D)。首先,以最小化最大完工时间和最小化总能耗为目标,考虑机器在不同负载下具有不同恶化速度,建立了柔性流水车间的节能调度模型;其次... 针对考虑机器恶化效应的柔性流水车间节能调度问题,提出一种改进的基于分解的多目标进化算法(MMODEA/D)。首先,以最小化最大完工时间和最小化总能耗为目标,考虑机器在不同负载下具有不同恶化速度,建立了柔性流水车间的节能调度模型;其次,基于贪婪规则设计了一种在考虑机器恶化效应的条件下能够兼顾生产效率与节能需求的具有递进式优化策略的节能解码方法;再次,设计了具有自适应亲代选择机制的遗传策略以加强算法对整体解空间的搜索能力;最后,使用了禁忌搜索策略加强算法对局部解空间的利用能力。通过多组对照实验,证明了所提改进策略的有效性以及MMOEA/D算法的优越性。 展开更多
关键词 基于分解的多目标进化算法 节能调度 柔性流水车间 机器恶化效应
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QIMIG:A Quantum-Inspired Evolutionary Framework for Software Library Migration 认领 引用
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作者 Yun Liu Jinghua Zhao +3 位作者 Liang Ma Zijie Huang Lizhi Cai Jianxin Ge 《Computers, Materials & Continua》 SCIE EI 2026年第9期795-812,共18页
Automated library migration reduces refactoring costs but challenges traditional evolutionary algorithms,which often suffer from premature convergence and poor recall in sparse,complex API mapping spaces.To address th... Automated library migration reduces refactoring costs but challenges traditional evolutionary algorithms,which often suffer from premature convergence and poor recall in sparse,complex API mapping spaces.To address this,we propose QIMIG,a multi-objective optimization framework integrating quantum-inspired encoding with quality-aware and greedy heuristic filtering.QIMIG utilizes a probabilistic Q-bit representation to maintain population diversity and avoid local optima.Simultaneously,its heuristic components leverage historical usage context to filter semantic noise and guide the search toward valid mappings.Evaluated on 9 real-world migration rules derived from 57,447 open-source projects,QIMIG statistically significantly outperforms state-of-the-art baselines such as UNSGA-III.The framework achieves a global mean F1-score of 0.92,exceeding the best-performing baseline by an absolute margin of 0.05,and demonstrates strong stability in resolving complex mapping structures. 展开更多
关键词 Library migration API mapping search-based software engineering quantum-inspired evolutionary algorithm multi-objective optimization
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A Comprehensive Review of Barnacles Mating Optimizer:Theoretical Foundation,Variants,Applications,and Future Research Directions 认领 引用
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作者 Mohammed A.El-Shorbagy Anas Bouaouda Fatma A.Hashim 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期41-118,共78页
As real-world optimization problems become more complex,the development of sophisticated and robust algorithms has become essential.Consequently,researchers are focusing on advanced optimizationmethods that efficientl... As real-world optimization problems become more complex,the development of sophisticated and robust algorithms has become essential.Consequently,researchers are focusing on advanced optimizationmethods that efficiently explore the feasible solution space.This involves designing new high-performance algorithms or enhancing existing meta-heuristic methods by integrating advanced evolutionary strategies.Barnacles Mating Optimizer(BMO)is an evolutionary-basedmeta-heuristic algorithminspired by themating behavior of barnacles,incorporating Hardy–Weinberg principles and the sperm-cast mechanism.Introduced in 2020,BMO has attracted significant attention and has been successfully applied across diverse fields due to its simple design,ease of implementation,high flexibility,and efficient convergence.Therefore,this review provides an overview and synthesis of studies employing BMO.It begins with an introduction to BMO,describing its natural inspiration and optimization framework,followed by a discussion of its core operational procedures and theoretical foundations.The paper then presents a comprehensive analysis of recent BMO variants,systematically categorizing them into modified,multi-objective,and hybrid versions.It also examines BMO’s diverse real-world applications,including power and control engineering,classification,image processing,wireless networks,forecasting,and signal processing.In addition,an updated performance evaluation of BMO is provided,comparing its effectiveness against recently published algorithms using the CEC2005 benchmark suite.Key strengths of BMO are highlighted,including its ability to balance exploration and exploitation,adaptability across problem domains,and its potential for hybridization with other optimization algorithms.Finally,potential enhancements and future research directions are outlined,including multi-objective variants,integration with deep learning,and parallel or distributed implementations. 展开更多
关键词 Evolutionary algorithms barnacles mating optimizer meta-heuristics engineering optimization computational intelligence
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A Competitive Parallel Animated Oat Optimization Algorithm for Reversible Digital Watermarking 认领 引用
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作者 Shu-Chuan Chu Libin Fu Jeng-Shyang Pan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第7期1209-1238,共30页
The Animated Oat Optimization Algorithm(AOO)is a novel evolutionary algorithm inspired by the behavior of animated oats.This paper proposes a Competitive Parallel Animated Oat Optimization Algorithm(CPAOO)comprising t... The Animated Oat Optimization Algorithm(AOO)is a novel evolutionary algorithm inspired by the behavior of animated oats.This paper proposes a Competitive Parallel Animated Oat Optimization Algorithm(CPAOO)comprising two components.First,a parallel strategy is employed in which inter-subpopulation communication is triggered at predefined iteration thresholds to balance exploration and exploitation.Second,a grouped competition strategy with incentive mechanisms is introduced,enabling the prioritized evolution of superior individuals to enhance the algorithm’s efficiency.Furthermore,building on the Prediction Error Expansion(PEE)algorithm,this paper proposes a Dual-Layer PEE(DLPEE)algorithm for reversible digital watermarking.Based on differences in pixel values around embedding points,image blocks are classified as either smooth or textured regions.The CPAOO algorithm is used to optimize the weights of the pixel predictor and to prioritize embedding secret information in smooth blocks.This approach enhances both the embedding capacity and the invisibility of the watermarked data.Experimental results demonstrate that the proposed methods achieve satisfactory performance. 展开更多
关键词 Evolutionary algorithm parallel strategy communication strategy grouped competition strategy reversible digital watermarking
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MaOEA/I:Many-objective Evolutionary Algorithm Based on Indicator Iε+ 认领 引用
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作者 Sifeng Zhu Chengrui Yang Jiaming Hu 《Journal of Harbin Institute of Technology(New Series)》 CAS 2023年第5期52-64,共13页
Balancing the diversity and convergence of the population is challenging in multi-objective optimization. The work proposed a many-objective evolutionary algorithm based on indicator Iε+(MaOEA/I) to solve the abov... Balancing the diversity and convergence of the population is challenging in multi-objective optimization. The work proposed a many-objective evolutionary algorithm based on indicator Iε+(MaOEA/I) to solve the above problems. Indicator Iε+(x,y) is used for environmental selection to ensure diversity and convergence of the population. Iε+(x,y) can evaluate the quality of individual x compared with individual y instead of the whole population. If Iε+(x,y) is less than 0, individual x dominates y. If Iε+(x,y) is 0, individuals x and y are the same. If Iε+(x,y) is greater than 0, no dominant relationship exists between individuals x and y. The smaller Iε+(x,y), the closer the two individuals. The dominated individuals should be deleted in environmental selection because they do not contribute to convergence. If there is no dominant individual, the same individuals and similar individuals should be deleted because they do not contribute to diversity. Therefore, the environmental selection of MaOEA/I should consider the two individuals with the smallest Iε+(x,y). If Iε+(x,y) is not greater than 0, delete individual y;if Iε+(x,y) is greater than 0, check the distance between individuals x, y, and the target point and delete the individual with a longer distance. MaOEA/I is compared with 6 algorithms until the population does not exceed the population size. Experimental results demonstrate that MaOEA/I can gain highly competitive performance when solving many-objective optimization problems. 展开更多
关键词 many-objective evolutionary algorithm indicator diversity convergence
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A Niche-Based Evolutionary Tabu Search Algorithm for Optimal Coordination of Protection Relays in Power Networks 认领 引用
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作者 YUAN Rongxiang ZOU Xiufen XU Chunlin 《Wuhan University Journal of Natural Sciences》 CAS 2009年第3期215-222,共8页
In this paper, refusal of operation and the degree of constraint violation are considered as one part of the objective function for optimizing the protection relay setting coordination of over current relays in power ... In this paper, refusal of operation and the degree of constraint violation are considered as one part of the objective function for optimizing the protection relay setting coordination of over current relays in power systems; a new expression of objective function in this optimization problem is proposed. To combine the advantages of both Tabu search （TS） and evolutionary algorithms （EAs）, a new niche-based evolutionary Tabu search algorithm （named NETS） is presented to solve this optimization problem. Moreover, in two cases of power networks, comparisons between NETS and two published algorithms are given. Experimental results show the expression of the objective function for protection relay setting is feasible and reasonable, and the proposed algorithm NETS exhibits a good performance. 展开更多
关键词 evolutionary algorithm Tabu search power system protection relay coordination
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