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Constructing Tumor Cellular Cluster Models and Their Evolutionary Dynamics to Decode Tumor Evolution 认领 引用
1
作者 Haili Qian Fei Ma 《Cancer Innovation》 CSCD 2026年第3期20-22,共3页
Tumor research has undergone a transition from a cancer cell-centered paradigm to a systematic evolutionary perspective.Can-cer is a highly complex disease,and tumor initiation and pro-gression have traditionally been... Tumor research has undergone a transition from a cancer cell-centered paradigm to a systematic evolutionary perspective.Can-cer is a highly complex disease,and tumor initiation and pro-gression have traditionally been interpreted primarily through genetic and molecular alterations within tumor cells.However,with the emergence of systems biology,this"cancer cell-centered"view has gradually evolved into a"systems evolutionary perspec-tive."The most recent theoretical framework in oncology em-phasizes that tumors are dynamic systems comprising multiple cell types and their interactions,highlighting that their evolution reflects system-level remodeling driven by multidimensional sig-naling networks[1].Within this framework,tumor cells,immune cells,stromal cells,and vascular-associated cells form highly in-terconnected regulatory networks through molecular interactions and cell-cell communication.These networks collectively shape tumor initiation,progression,and therapeutic response.Conse-quently,approaches focusing solely on single molecules or indi-vidual cells are unable to sufficiently account for the complexity and dynamic behavior of tumors. 展开更多
关键词 evolutionary dynamics systems biologythiscancer tumor evolution asystems evolutionary theoretical framework tumor cellular cluster models systematic evolutionary perspectivecan cer systems biology
Complex Evolutionary Games:Problem,Method,and Future 认领 引用
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作者 Zhou-Zhi Lu Yi Jiang +4 位作者 Zhi-Fan Tang Liu-Yue Luo Chun-Hua Chen Jun Zhang Zhi-Hui Zhan 《Artificial Intelligence Science and Engineering》 2026年第2期141-164,共24页
Evolutionary game theory(EGT)is a complex and emerging research area.In recent years,the rapid development of evolutionary games has driven extensive research across multiple interdisciplinary domains.However,existing... Evolutionary game theory(EGT)is a complex and emerging research area.In recent years,the rapid development of evolutionary games has driven extensive research across multiple interdisciplinary domains.However,existing surveys remain scenario-specific and lack a comprehensive overview of complex evolutionary games.To address this research gap,this survey aims to provide a comprehensive review of existing studies in the field of complex evolutionary games,thereby offering a clear reference framework for future researchers.First,a novel 5C taxonomy framework is proposed to systematically characterize the core complexities in complex evolutionary games from five complementary dimensions,namely complex structures,contingent strategies,changing environments,coupled players,and compound payoffs.This framework provides a unified taxonomy for organizing fragmented studies and revealing the intrinsic relationships among different categories of complex evolutionary games.Second,based on the proposed 5C taxonomy framework,this survey systematically investigates and analyzes state-of-the-art studies,comprehensively summarizing their advantages and limitations.Finally,potential future research directions are discussed with the aim of promoting further advancements in complex evolutionary games.This survey seeks to provide researchers with a comprehensive reference and valuable research insights,thereby facilitating the continued development and prosperity of the evolutionary games field. 展开更多
关键词 evolutionary game theory complex evolutionary games 5C taxonomy framework complex systems evolutionary dynamics evolutionary computation
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Unveiling the biodiversity and evolutionary mysteries of the Himalayas in the genomic era 认领 引用
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作者 Yundong Gao Jia-Tang Li Shunping He 《Journal of Systematics and Evolution》 SCIE CSCD 2026年第1期1-3,共3页
With advances in transportation,information exchange,and technologies such as next-generation sequencing,a new era has dawned for investigating biodiversity and conservation in the Himalayan region.In this issue,we ha... With advances in transportation,information exchange,and technologies such as next-generation sequencing,a new era has dawned for investigating biodiversity and conservation in the Himalayan region.In this issue,we have gathered together leading researchers to deepen our understanding of the mechanisms and evolutionary processes shaping Himalayan biodiversity,as well as the implications of these for conservation. 展开更多
关键词 mechanisms evolutionary processes biodiversity Himalayas information exchange next generation sequencing biodiversity conservation evolutionary processes transportation
Large Language Model-Driven Evolutionary Optimization With a Hallucination-Resilient Mechanism 认领 引用
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作者 Weixiong Huang Rui Wang +3 位作者 Tao Zhang Sheng Qi Shichao Fan Ling Wang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1428-1445,共18页
Large language models(LLMs)have demonstrated significant potential as black-box optimizers due to their strong reasoning capabilities.However,challenges such as the hallucination phenomenon introduce instability and u... Large language models(LLMs)have demonstrated significant potential as black-box optimizers due to their strong reasoning capabilities.However,challenges such as the hallucination phenomenon introduce instability and uncertainty,limiting their effectiveness.This paper proposes an LLM-driven evolutionary optimization framework,referred to as LLM-driven hybrid evolutionary optimization framework(LHO),that integrates LLMs with traditional evolutionary operators.LLMs accelerate the optimization process by generating high-quality solutions,while evolutionary operators ensure stability and provide performance guarantees.To further enhance robustness,we introduce a hallucination-resilient mechanism to mitigate the risks associated with LLM hallucinations.Experimental results on various benchmark tests,encompassing single-objective,multiobjective,and complex constrained multiobjective problems,confirm the effectiveness and practicality of the proposed framework,offering valuable insights and future directions for LLM as evolutionary optimizers. 展开更多
关键词 Evolutionary algorithm (EA) evolutionary computation large language models (LLMs)
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Constraint Intensity-Driven Evolutionary Multitasking for Constrained Multi-Objective Optimization 认领 引用
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作者 Leyu Zheng Mingming Xiao +2 位作者 Yi Ren Ke Li Chang Sun 《Computers, Materials & Continua》 SCIE EI 2026年第3期1241-1261,共21页
In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and red... In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and reduces population diversity.To address these challenges,we propose a novel algorithm named Constraint IntensityDriven Evolutionary Multitasking(CIDEMT),which employs a two-stage,tri-task framework to dynamically integrates problem structure and knowledge transfer.In the first stage,three cooperative tasks are designed to explore the Constrained Pareto Front(CPF),the Unconstrained Pareto Front(UPF),and theε-relaxed constraint boundary,respectively.A CPF-UPF relationship classifier is employed to construct a problem-type-aware evolutionary strategy pool.At the end of the first stage,each task selects strategies from this strategy pool based on the specific type of problem,thereby guiding the subsequent evolutionary process.In the second stage,while each task continues to evolve,aτ-driven knowledge transfer mechanism is introduced to selectively incorporate effective solutions across tasks.enhancing the convergence and feasibility of the main task.Extensive experiments conducted on 32 benchmark problems from three test suites(LIRCMOP,DASCMOP,and DOC)demonstrate that CIDEMT achieves the best Inverted Generational Distance(IGD)values on 24 problems and the best Hypervolume values(HV)on 22 problems.Furthermore,CIDEMT significantly outperforms six state-of-the-art constrained multi-objective evolutionary algorithms(CMOEAs).These results confirm CIDEMT’s superiority in promoting convergence,diversity,and robustness in solving complex CMOPs. 展开更多
关键词 Constrained multi-objective optimization evolutionary algorithm evolutionary multitasking knowledge transfer
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A brief review of evolutionary game dynamics in the reinforcement learning paradigm 认领 引用
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作者 Guozhong Zheng Xin Ou +2 位作者 Shengfeng Deng Jiqiang Zhang Li Chen 《Communications in Theoretical Physics》 SCIE CAS CSCD 2026年第6期220-232,共13页
Cooperation,fairness,trust,and resource coordination are cornerstones of modern civilization,yet their emergence remains inadequately explained,largely due to persistent discrepancies between theoretical predictions a... Cooperation,fairness,trust,and resource coordination are cornerstones of modern civilization,yet their emergence remains inadequately explained,largely due to persistent discrepancies between theoretical predictions and behavioral experiments.Part of this gap may arise from the imitation learning paradigm commonly used in prior theoretical models,which assumes individuals merely copy successful neighbors according to predetermined,fixed rules.This review examines recent advances in evolutionary game dynamics that employ reinforcement learning(RL)as an alternative paradigm.In RL,individuals learn through trial and error and intro spec tively refine their strategies based on environmental feedback.We begin by introducing key concepts in evolutionary game theory and the two learning paradigms,then synthesize progress in applying RL to elucidate cooperation,trust,fairness,optimal resource coordination,and ecological dynamics.Collectively,these studies indicate that RL offers a promising unified framework for understanding the diverse social and ecological phenomena observed in human and natural systems. 展开更多
关键词 reinforcement learning evolutionary game theory cooperation fairness trust resource allocation biodiversity
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Comprehensive Analysis of the Relative Dispersion of Droplet-Size Distributions and Their Relationships to Key Physical Fog Processes Under Different Aerosol Conditions and Evolutionary Stages 认领 引用
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作者 Xingcan JIA Chunsong LU +7 位作者 Zhiheng LIAO Ping TIAN Yuan WANG Fei WANG Shuai YANG Youjun DOU Xiange LIU Yangang LIU 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2026年第4期861-873,I0042-I0046,共13页
The relative dispersion of cloud and fog droplets has significant impacts on aerosol indirect effects,radiative transfer,and microphysical processes.However,previous studies have been mostly concerned with clouds,with... The relative dispersion of cloud and fog droplets has significant impacts on aerosol indirect effects,radiative transfer,and microphysical processes.However,previous studies have been mostly concerned with clouds,with limited studies on fog,particularly those that examine the combined influences of all key physical processes and their roles during fog evolution.As such,this study aims to conduct a comprehensive investigation by examining the relationships between relative dispersion and other microphysical variables,as well as the underlying microphysical and dynamic processes,based on field fog campaigns in polluted and clean conditions.In polluted fog,droplet concentrations are higher,leading to smaller droplets and increased dispersion.The correlation between dispersion and droplet volume-mean radius is positive in the polluted fog,but shifts to negative in clean fog.We attribute the difference to various microphysical processes like aerosol activation,condensation,collision-coalescence,and entrainment-mixing.In polluted fog,high aerosol concentrations,low supersaturations,and strong turbulence(entrainment-mixing)provide suitable conditions for the simultaneous occurrence of droplet condensation and aerosol activation,resulting in a positive correlation between dispersion and volume-mean radius,especially during the fog formation stage.In contrast,during the mature stage in clean fog,condensation is dominant with weak aerosol activation leading to a negative correlation between relative dispersion and volume-mean radius.The collision-coalescence process is more active in the mature stage,increasing radii and leading to the negative correlation between dispersion and volume-mean radius.This result sheds new light on understanding the relative dispersion and mechanisms in fog under different aerosol backgrounds. 展开更多
关键词 fog relative dispersion evolutionary stage aerosol activation collision-coalescence
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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://gffzz188fe103f8f1460as666bu6cf5vqw6q9n.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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EXISTENCE AND STABILITY ANALYSIS OF EVOLUTIONARY DIFFERENTIAL HEMIVARIATIONAL INEQUALITIES IN BANACH SPACES 认领 引用
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作者 Wei LI Ke XU +1 位作者 Rong HU Zhiyang MA 《Acta Mathematica Scientia》 SCIE CSCD 2026年第2期549-567,共19页
this paper,we introduce and study a new class of evolutionary differential hemivariational inequalities in Banach spaces.Using a fixed-point theorem,we first establish the existence and uniqueness of mild solutions fo... this paper,we introduce and study a new class of evolutionary differential hemivariational inequalities in Banach spaces.Using a fixed-point theorem,we first establish the existence and uniqueness of mild solutions for the evolutionary differential hemivariational inequality.Building on this result,we then investigate the stability of the evolutionary differential hemivariational inequality problem under perturbations of the mappings,constraint set,and infinitesimal generator of the Co-semigroup etB. 展开更多
关键词 evolutionary differential hemivariational inequality mild solution stability Cosemigroup
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Modeling of lane-changing behaviors of human-machine codriving vehicles based on evolutionary game 认领 引用
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作者 Ronghan Yao Xiaomei Wang +3 位作者 Bing Yang Yang Chen Feng Sun Hu Zhang 《Digital Transportation and Safety》 2026年第1期10-21,共12页
To alleviate the conflicts resulting from lane-changing in human-machine co-driving,this study investigates four game scenarios:human-driven lanechanging vehicles(HLVs)and human-driven target vehicles(HTVs),HLVs and A... To alleviate the conflicts resulting from lane-changing in human-machine co-driving,this study investigates four game scenarios:human-driven lanechanging vehicles(HLVs)and human-driven target vehicles(HTVs),HLVs and Autonomous target vehicles(ATVs),autonomous lane-changing vehicles(ALVs)and HTVs,and ALVs and ATVs.An evolutionary game model is formulated by integrating safety,efficiency,comfort,fatigue,and energy-saving utilities.The strategy selection process of decision-makers is simulated by replicator dynamics equations.Based on the Friedman method,the stability of equilibrium points in different situations is analyzed,and the sensitivity of evolutionary results to utility parameters is clarified.Results show that the strategy profile of(lane-changing,not-giving-way)that causes conflicts only exists temporarily under certain conditions and will not become a long-term stable point.The game characteristics under different scenarios are obviously different;that is,the game evolutionary paths for autonomous vehicles are the most stable,while the adjustment of game strategies for human-driven vehicles is more frequent.Additionally,energy-saving promotes the formation of the strategy profile of(lane-changing,giving-way),while fatigue limits the frequent lane-changing of human-driven vehicles.The evolutionary game model proposed can provide theoretical support for improving the performance of the human-machine co-driving system. 展开更多
关键词 Human-machine co-driving Autonomous driving Lane-changing Evolutionary game
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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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Supervisory strategies for agroecological security in plateau regions based on a four-party evolutionary game model 认领 引用
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作者 Yuanwei Du Yiwei Zhang 《Chinese Journal of Population,Resources and Environment》 CSCD 2026年第2期278-292,共15页
Conflicting interests among multiple stakeholders and regulatory imbalances are challenges faced by agricultural ecosystems in highland regions.Current governance models largely fail to reflect complex interactions am... Conflicting interests among multiple stakeholders and regulatory imbalances are challenges faced by agricultural ecosystems in highland regions.Current governance models largely fail to reflect complex interactions among stakeholders,resulting in suboptimal outcomes.To address this problem,this study constructs a four party asymmetric evolutionary game model that includes the government,regulatory agencies,agricultural enterprises,and local residents to analyze stakeholder strategy choices under different benefit conditions.It innovatively incorporates public supervision incentives and dynamic reward-penalty mechanisms into the regulatory system to demonstrate how the government can influence agricultural enterprises’strategic choices through mechanism design.The evolution of the system is analyzed using a dynamic equation,and numerical simulations are performed using data on the ecological baseline of the plateau region and socioeconomic parameters.The findings indicate that effective long-term governance of plateau agricultural ecological security depends on the establishment of a reasonable cost-benefit distribution mechanism.Moreover,public participation in supervision mechanisms can effectively guide agricultural enterprises toward eco-friendly behavior.Additionally,dynamic reward-punishment mechanisms are advantageous for achieving long-term ecological security goals.The scientific value of this study lies in proposing a collaborative regulatory mechanism for plateau agricultural ecological security from a game theory perspective,and its research paradigm provides a theoretical reference for complex regulatory problems in other fields.Based on these findings,we recommend a collaborative governance mechanism that combines dynamic rewards and punishments with public participation to improve governance efficiency and achieve the sustainable development of the plateau’s economy and ecology. 展开更多
关键词 Plateau-characteristic agriculture Ecological security Supervisory strategy Evolutionary game Sustainable development
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Evolutionary dynamics of pyrethroid resistance in the Megalurothrips usitatus of Hainan and detection of voltage-gated sodium channel mutation sites 认领 引用
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作者 Guoliang Xia Dongfu Yuan +5 位作者 Likui Wang Ruibo Gao Qinglong Wang Yuran Xie Jiang Wayne Shaoying Wu 《Tropical Plants》 CSCD 2026年第1期27-35,共9页
To elucidate the evolutionary dynamics and molecular mechanisms underlying resistance to pyrethroid insecticides in the Megalurothrips usitatus of Hainan Province,resistance levels to deltamethrin,bifenthrin,and cyper... To elucidate the evolutionary dynamics and molecular mechanisms underlying resistance to pyrethroid insecticides in the Megalurothrips usitatus of Hainan Province,resistance levels to deltamethrin,bifenthrin,and cypermethrin were systematically assessed in four field populations from 2024 to 2026.The voltage-gated sodium channel(VGSC)gene was cloned,and resistance-associated mutation sites were identified.The findings indicated that all four field populations demonstrated elevated levels of resistance to the three pyrethroids.Although resistance continued to escalate from 2024 to 2026,the rate of increase decelerated in 2026.The overall resistance of the populations was ranked as CM-R>LS-R>SY-R>LD-R,with the selectivity of resistance to the insecticides ordered as bifenthrin>cypermethrin>deltamethrin.The full-length VGSC gene of M.usitatus,the T929I mutation,was identified in all four field populations,exhibiting a frequency of 100%in 2024.From 2025 to 2026,this frequency exhibited a declining trend,accompanied by population differentiation.Although no M918L/T or L1014F mutations were detected,these mutations are widely present in other thrips closely related to the M.usitatus,indicating potential risks that may arise in the future.Currently,T929I is the sole mutation associated with pyrethroid resistance identified in the M.usitatus in Hainan,and its decreasing frequency may be linked to novel resistance adaptations within the VGSC of this insect.The findings of this study provide a theoretical foundation for the early monitoring and scientific management of resistance in the M.usitatus in Hainan. 展开更多
关键词 molecular mechanisms Megalurothrips usitatus pyrethroid insecticides evolutionary dynamics Hainan deltamethrin pyrethroid resistance resistance levels
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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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Evolutionary hypergraph dismantling via deep reinforcement learning 认领 引用
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作者 Junjie Qian Wenlan Wang +3 位作者 Hanyun Wang Qiqi Wang Yao Zhang Huijia Li 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第6期886-898,共13页
Assessing the vulnerability of complex systems requires effective hypergraph dismantling strategies,yet existing methods struggle with the dynamic nature of cascading failures and the rugged optimization landscapes of... Assessing the vulnerability of complex systems requires effective hypergraph dismantling strategies,yet existing methods struggle with the dynamic nature of cascading failures and the rugged optimization landscapes of high-order networks.In this paper,we propose a novel framework:hypergraph dismantling via evolutionary deep reinforcement learning(HD-EDR).First,we model a realistic dismantling environment incorporating hyperdegree-based and residual-capacitybased load redistribution mechanisms.Second,we introduce a hybrid learning architecture that synergizes the global exploration of evolutionary strategies with the gradient-based exploitation of deep reinforcement learning.A bidirectional parameter synchronization mechanism is designed to prevent the agent from being trapped in local optima.Furthermore,we integrate an inductive encoder to capture the evolving high-order dependencies of the residual network in real time.Extensive experiments across nine real-world datasets demonstrate that our framework significantly outperforms state-of-the-art baselines,providing a highly effective and robust strategy for maximizing structural damage in high-order networks. 展开更多
关键词 network dismantling hypergraph deep reinforcement learning evolutionary strategies combinatorial optimization
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Analysis of Tripartite Evolutionary Game in Marketization of New Energy Electricity Prices Based on Large Language Models 认领 引用
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作者 MAO Hui TAN Benyan +2 位作者 KHANAL Ribesh ULLAH Obaid HUANG Meizhong 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2026年第3期225-240,共16页
The newly-issued 2025 policy on deepening the market-oriented reform of new energy feed-in tariffs has exerted a profound impact on reshaping the development pattern of new energy industries,such as photovoltaic power... The newly-issued 2025 policy on deepening the market-oriented reform of new energy feed-in tariffs has exerted a profound impact on reshaping the development pattern of new energy industries,such as photovoltaic power.In this evolving context,collaborative grid-connection among photovoltaic power generation enterprises,power grid enterprises,and government agencies is crucial for enhancing the competitiveness of the new energy industry and achieving energy transition.This paper constructs a tripartite evolutionary game model to deeply explore the strategy selection and key influencing factors of each subject in the grid-connection process.It integrates large language models(LLMs)to analyze factors affecting strategy selection among different stakeholders and utilizes LLMs to capture the heterogeneous cognitive characteristics of different subjects,thereby overcoming the limitations of"strong assumptions"commonly found in traditional game models.Through multi-round semantic parsing,it identifies key influencing factors such as market-oriented electricity price fluctuations,technological innovation costs,and assessment penalty.Furthermore,based on the actual data of photovoltaic industry development in Jiangxi and Hubei Provinces,numerical simulations are employed to analyze the impact of key factors(e.g.,marketoriented electricity price fluctuations)on the strategic choices of the three stakeholder parties under the new policy framework and verify the model's effectiveness.The study clarifies the critical thresholds affecting collaborative grid connection,providing a data-driven theoretical basis for the government to implement targeted policies and enterprises to optimize decision-making. 展开更多
关键词 electricity marketization in 2025 large language models(LLMs) policy simulation tripartite evolutionary game
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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
Multi-Objective Evolutionary Framework for High-Precision Community Detection in Complex Networks 认领 引用
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作者 Asal Jameel Khudhair Amenah Dahim Abbood 《Computers, Materials & Continua》 SCIE EI 2026年第1期1453-1483,共31页
Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may r... Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification. 展开更多
关键词 Multi-objective optimization evolutionary algorithms community detection heuristic metaheuristic hybrid social network models
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Evolutionary analysis of multi-player snowdrift game with cost sharing under Aspiration–Fermi hybrid rule 认领 引用
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作者 Bolin Yang Guanghui Yang 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第3期185-194,共10页
This paper firstly constructs a multi-player snowdrift game with all players'cost sharing once the snowdrift is removed for a fairness.Secondly,an Aspiration-Fermi hybrid rule is proposed to derive an extended ave... This paper firstly constructs a multi-player snowdrift game with all players'cost sharing once the snowdrift is removed for a fairness.Secondly,an Aspiration-Fermi hybrid rule is proposed to derive an extended average abundance function via Markov chain evolutionary processes.Besides,extensive numerical simulations well verify theoretical results.Our findings show that both cost sharing and the Aspiration-Fermi hybrid rule significantly promote the cooperation by enhancing the average abundance in multi-person snowdrift game.By comparison with the existing woks,our proposed model and hybrid strategy-update rule provide novel insights into the evolution of cooperation in multi-person games. 展开更多
关键词 multi-player snowdrift evolutionary game cost sharing Aspiration-Fermi hybrid rule average abundance function
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A Tripartite Evolutionary Game Model for Air‑Rail Intermodal Transportation Stakeholders Based on Perspective of Airspace Congestion 认领 引用
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作者 SUN Bo XU Zehui GAO Han 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2026年第2期203-224,共22页
A tripartite evolutionary game model of enterprise,air traffic control(ATC)and passengers in an air-rail intermodal transport(ARIT)system was developed and investigated.The optimal interaction among enterprise,ATC and... A tripartite evolutionary game model of enterprise,air traffic control(ATC)and passengers in an air-rail intermodal transport(ARIT)system was developed and investigated.The optimal interaction among enterprise,ATC and passengers was explored based on the congestion charging mechanism,as presented in terms of the payoffs and decision-making behaviors of three participants.Payoff matrices were established for three game players,wherein fare,mileage cost,en-route charge and generalized travel cost were taken into consideration.After that,the replicated dynamic equations were derived and employed to analyze the reliability of the proposed model and the dynamic behaviors of each game player under initial conditions.Eventually,the Beijing-Shanghai,Beijing-Guangzhou and Beijing-Kunming corridors were used as practical cases to clarify the impact of key factors(e.g.,distance,enroute charge and passenger sharing ratio)on the evolutionary trend and final strategy.The results showed that three players tend to choose the strategy which is always profitable.The enterprises would choose to introduce the ARIT strategy in medium-distance route,but not in short-and long-distance route,ATC chose to implement the congestion charging strategy,and passengers preferred the ARIT strategy.In addition,the final strategies were affected by any changes in key factors,and enterprises were more sensitive and likely to introduce the ARIT strategy out of individual interest. 展开更多
关键词 air-rail intermodal transport(ARIT) air traffic control(ATC) passengers tripartite evolutionary game model
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