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Multi-objective optimization of work package scheme problem to minimize project carbon emissions and cost

COMPUTERS & INDUSTRIAL ENGINEERING 2025年 200卷

作者： Zhang, Yaning Li, Xiao Teng, Yue Shen, Geoffrey Q. P. Bai, Sijun Hong Kong Polytech Univ Dept Bldg & Real Estate Hong Kong Peoples R China Northwestern Polytech Univ Sch Management Xian Peoples R China Univ Hong Kong Dept Civil Engn Hong Kong Peoples R China

The construction industry accounts for around 30% of global energy consumption and 33% of CO2 emissions. For the carbon neutrality initiative, reducing carbon emissions from construction projects become a critical objective for project success. However, a dilemma arises in balancing carbon emissions and project cost, particularly during the work package-based project planning phase. To address this issue, this article presents a novel multiobjective optimization model for the work package scheme problem, aimed at minimizing both project carbon emissions and cost. Multi-objective evolutionary algorithms (EAs) are developed to solve the model. Firstly, a multi-objective Mixed-Integer Programming (MIP) model is developed to establish the functional relation between work package attributes (duration and work content) and optimization objectives (carbon emissions and cost). Secondly, two multi-objective optimization EAs, NSGA-II and SPEA2, are developed to obtain the Pareto frontier. The experimental results indicate that NSGA-II and SPEA2 exhibit superior trade-off capabilities compared to the Gurobi and the state-of-the-art heuristic algorithm. Compared to Gurobi, the proposed EAs achieve an approximately 68% reduction in carbon emissions, accompanied by about an 11% cost increase. Compared to the heuristic algorithm, the EAs achieve around 10% reductions in carbon emissions with an approximately 5% cost increase. Additionally, sensitivity analysis conducted on a project instance dataset demonstrates the robustness of the proposed model and algorithms. This article paves the way for achieving lowcarbon and sustainable construction project management in the context of carbon neutrality.

关键词： Multi-objective optimization Project planning Work packages Carbon emissions evolutionary algorithms

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Totipotent neural controllers for modular soft robots: Achieving specialization in body-brain co-evolution through Hebbian learning

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NEUROCOMPUTING 2025年 614卷

作者： Ferigo, Andrea Iacca, Giovanni Medvet, Eric Nadizar, Giorgia Univ Trento Dept Informat Engn & Comp Sci Trento Italy Univ Trieste Dept Engn & Architecture Trieste Italy Univ Trieste Dept Math & Geosci Trieste Italy

Multi-cellular organisms typically originate from a single cell, the zygote, that then develops into a multitude of structurally and functionally specialized cells. The potential of generating all the specialized cells that make up an organism is referred to as cellular "totipotency", a concept introduced by the German plant physiologist Haberlandt in the early 1900s. In an attempt to reproduce this mechanism in synthetic organisms, we present a model based on a kind of modular robot called Voxel-based Soft Robot (VSR), where both the body, i.e ., the arrangement of voxels, and the brain, i.e ., the Artificial Neural Network (ANN) controlling each module, are subject to an evolutionary process aimed at optimizing the locomotion capabilities of the robot. In an analogy between totipotent cells and totipotent ANN-controlled modules, we then include in our model an additional level of adaptation provided by Hebbian learning, which allows the ANNs to adapt their weights during the execution of the locomotion task. Our in silico experiments reveal two main findings. Firstly, we confirm the common intuition that Hebbian plasticity effectively allows better performance and adaptation. Secondly and more importantly, we verify for the first time that the performance improvements yielded by plasticity are in essence due to a form of specialization at the level of single modules (and their associated ANNs): thanks to plasticity, modules specialize to react indifferent ways to the same set of stimuli, i.e ., they become functionally and behaviorally different even though their ANNs are initialized in the same way. This mechanism, which can be seen as a form of totipotency at the level of ANNs, can have, in our view, profound implications in various areas of Artificial Intelligence (AI) and applications thereof, such as modular robotics and multi-agent systems.

关键词： Hebbian learning Neural plasticity Voxel-based Soft Robots evolutionary algorithms

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Online Optimization of Curriculum Learning Schedules using evolutionary Optimization 6

Online Optimization of Curriculum Learning Schedules using E...

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6th Annual IEEE Conference on Games (CoG)

作者： Jiwatode, Mohit Schlecht, Leon Dockhorn, Alexander Leibniz Univ Hannover Inst Informat Proc Hannover Germany

ISBN: (纸本)9798350350685;9798350350678

We propose RHEA CL, which combines Curriculum Learning (CL) with Rolling Horizon evolutionary algorithms (RHEA) to automatically produce effective curricula during the training of a reinforcement learning agent. RHEA CL optimizes a population of curricula, using an evolutionary algorithm, and selects the best-performing curriculum as the starting point for the next training epoch. Performance evaluations are conducted after every curriculum step in all environments. We evaluate the algorithm on the DoorKey and DynamicObstacles environments within the Minigrid framework. It demonstrates adaptability and consistent improvement, particularly in the early stages, while reaching a stable performance later that is capable of outperforming other curriculum learners. In comparison to other curriculum schedules, RHEA CL has shown to yield performance improvements for the final Reinforcement learning (RL) agent at the cost of additional evaluation during training.

关键词： Curriculum Learning Reinforcement Learning evolutionary algorithms Rolling Horizon algorithms

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Emergency medical supplies scheduling during public health emergencies: algorithm design based on AI techniques

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2025年第2期63卷 628-650页

作者： Xia, Huosong Sun, Zelin Wang, Yuan Zhang, Justin Zuopeng Kamal, Muhammad Mustafa Jasimuddin, Sajjad M. Islam, Nazrul Wuhan Text Univ Sch Management Wuhan Peoples R China Res Ctr Enterprise Decis Support Key Res Inst Humanities & Social Sci Univ Hubei Pr Wuhan Peoples R China Wuhan Text Univ Res Inst Management & Econ Wuhan Peoples R China Univ North Florida Coggin Coll Business Dept Management Jacksonville FL USA Coventry Univ Sch Strategy & Leadership Coventry England Kedge Business Sch Marseille France Univ East London Royal Docks Sch Business & Law London England

Based on AI technology, this study proposes a novel large-scale emergency medical supplies scheduling (EMSS) algorithm to address the issues of low turnover efficiency of medical supplies and unbalanced supply and demand point scheduling in public health emergencies. We construct a fairness index using an improved Gini coefficient by considering the demand for emergency medical supplies (EMS), actual distribution, and the degree of emergency at disaster sites. We developed a bi-objective optimisation model with a minimum Gini index and scheduling time. We employ a heterogeneous ant colony algorithm to solve the Pareto boundary based on reinforcement learning. A reinforcement learning mechanism is introduced to update and exchange pheromones among populations, with reward factors set to adjust pheromones and improve algorithm convergence speed. The effectiveness of the algorithm for a large EMSS problem is verified by comparing its comprehensive performance against a super-large capacity evaluation index. Results demonstrate the algorithm's effectiveness in reducing convergence time and facilitating escape from local optima in EMSS problems. The algorithm addresses the issue of demand differences at each disaster point affecting fair distribution. This study optimises early-stage EMSS schemes for public health events to minimise losses and casualties while mitigating emotional distress among disaster victims.

关键词： Scheduling public health emergency medical supply heuristics evolutionary algorithms artificial intelligence

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Synchronous Remote Calibration for Electricity Meters: Application and Optimization

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APPLIED SCIENCES-BASEL 2025年第3期15卷 1259-1259页

作者： Zha, Zhiyong Ge, Hanfang Zou, Chengcheng Long, Fei He, Xingfeng Wu, Geng Dong, Chenxi Deng, Tianping Xu, Jiaxiang State Grid Hubei Informat & Telecommun Co Wuhan 430048 Peoples R China Huazhong Univ Sci & Technol Sch Elect Informat & Commun Hubei Key Lab Smart Internet Technol Wuhan 430074 Peoples R China

Remote calibration is an advanced methodology that leverages electricity meters, intelligent detection, and computing technologies to enhance calibration efficiency and precision significantly. However, current research predominantly focuses on isolated calibration architectures tailored to single-laboratory environments. In contrast, distributed remote calibration systems that integrate multiple nodes remain in their early developmental stages, despite their considerable potential for improving scalability and operational efficiency. The purpose of this paper is to propose a multi-point collaborative distributed remote calibration model that improves scalability and operational efficiency for remote sensing devices. It addresses the challenge of resource allocation for synchronous calibration across distributed nodes by introducing a hybrid genetic algorithm that optimises scheduling and resource management. Experimental results reveal that the proposed algorithm surpasses other comparable methods in its category, highlighting its capability to improve resource efficiency in distributed remote calibration systems. Additionally, the hybrid genetic algorithm offers profound insights and effective solutions to the intricate challenges of task scheduling in dual-container synchronisation, enhancing both scheduling performance and system dependability.

关键词： calibration systems distributed systems resource management evolutionary algorithms task scheduling

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A generalized bilevel optimization model for large-scale task scheduling in multiple agile earth observation satellites

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KNOWLEDGE-BASED SYSTEMS 2025年 309卷

作者： Chen, Jiawei Wang, Feiran Chen, Yingguo He, Lei Du, Yonghao Wu, Jian Chen, Yingwu Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Xidian Univ Hangzhou Res Inst Hangzhou 310000 Peoples R China

Scheduling large-scale tasks within a multi-agile earth observation satellite system poses a formidable challenge. Given the complexity of this optimization problem, the responding solving strategy should exhibit both efficiency and effectiveness in terms of computational time and solution quality. The current strategies can broadly be categorized into all-in-one and two-stage methodologies. The latter, more conducive to real- world scenarios, owing to reducing the problem complexity and practical operational flexibility, dissects this challenge into task allocation and single-satellite scheduling. However, existing two-stage strategies still consider the objectives and constraints at the equivalent level and only adapt to specific algorithms. In this way, the pivotal issues concerning the solution complexity and strategy compatibility remain fundamentally unaddressed. To address this limitation, we proposed a generalized bilevel optimization model called Question- and-Answer model, which establishes two distinct optimization model with different contains and objectives. In this model, the upper questions are highly indispensable in the response from the lower level and constraints are considered separately at two stages. To ascertain the generalization of this framework, we conduct a comprehensive range of experiments employing various proposed strategies and algorithms in two stages. Diverse algorithms ranging from heuristic principles, and evolutionary strategies, to reinforcement learning can be seamlessly combined and integrated within this framework. The results demonstrate that the scale of scenarios does not affect the effectiveness of any amalgamated algorithms within this framework. Furthermore, the transition of upper questions according to the lower answers indeed improves the objectives but concurrently intensify the computational time.

关键词： Large-scale scheduling evolutionary algorithms Reinforcement learning Bilevel optimization

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Self-adaptive single-diode model parameter identification under small mismatching conditions

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RENEWABLE ENERGY 2025年 245卷

作者： Garcia-Marrero, Luis E. Pavon-Vargas, Carlos I. Bastidas-Rodriguez, Juan D. Monmasson, Eric Petrone, Giovanni Univ Salerno Dipartimento Ingn Informaz Elettr & Matemat Applic I-84084 Fisciano Italy CY Cergy Paris Univ Syst & Applicat Technol Informat & Energie Lab F-95031 Cergy Pontoise France Univ Nacl Colombia Fac Ingn & Arquitectura Manizales 170003 Colombia

Current model-based methods for monitoring photovoltaic (PV) modules typically rely on the single-diode model (SDM) or its variants, assuming uniform operating conditions across the module. However, these ideal conditions are difficult to realize in real-world applications due to partial shading, soiling, degradation, and other phenomena. This paper proposes a 7-parameter self-adapting Double SDM model (D-SDM) to enhance the accuracy and reliability of parameter identification in PV modules under real operating conditions. A robust methodology based on evolutionary algorithms is proposed to estimate the parameters of the D-SDM, directly from the I-V characteristic of a PV module, applicable in both uniform and mismatched scenarios. The proposed methodology also includes a robust fitting error calculation that only considers the section of the I-V curve where all the cells operate with positive voltage. The methodology is validated using experimental and simulated I-V curves across various mismatching patterns, demonstrating the superior stability and reliability of the proposed method, which can be used for PV system monitoring and diagnosis in complex conditions.

关键词： Single-diode model Parameter identification Mismatching conditions PV diagnosis evolutionary algorithms

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Feature subset selection for big data via parallel chaotic binary differential evolution and feature-level elitism☆

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COMPUTERS & ELECTRICAL ENGINEERING 2025年 123卷

作者： Vivek, Yelleti Ravi, Vadlamani Krishna, P. Radha Inst Dev & Res Banking Technol Ctr Artificial Intelligence & Machine Learning Castle Hills Rd 1 Hyderabad 500057 India Natl Inst Technol Dept Comp Sci & Engn Warangal 506004 India

Feature subset selection (FSS) employing a wrapper approach is fundamentally a combinatorial optimization problem maximizing the area under the receiver operating characteristic curve (AUC) of a classifier built on this subset under single objective environment. To balance both the AUC and the cardinality of the selected feature subset, we propose a novel multiplicative fitness function that combines AUC and a decreasing function of cardinality. Although the differential evolution algorithm is robust, it is prone to premature convergence, which can result in entrapment in local optima. To address this challenge, we propose chaotic binary differential evolution coupled with feature-level elitism (CE-BDE), where the chaotic maps are introduced at the initialization and the crossover operator. We also introduce feature-level elitism to improve the exploitation capability. Feature-level elitism involves preserving those features, which are chosen based on their frequency of occurrence in the population in the evolution process. Dealing with big data entails computational complexity, which motivates us to propose an effective parallel/ distributed strategy island model. The results demonstrate that the parallel CE-BDE outperformed the rest of the algorithms in terms of mean AUC and cardinality. The speedup and computational gain yielded by the proposed parallel approach further accentuate its superiority. Overall, the topperforming algorithm with the multiplicative fitness function turned out to be statistically significant compared to that with the additive fitness function across 5 out of 6 datasets.

关键词： Feature subset selection Spark evolutionary algorithms Chaotic maps Feature-level elitism

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Neural Architecture Search for Generative Adversarial Networks: A Comprehensive Review and Critical Analysis

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APPLIED SCIENCES-BASEL 2025年第7期15卷 3623-3623页

作者： Alotaibi, Abrar Ahmed, Moataz King Fahd Univ Petr & Minerals Dept Informat & Comp Sci Dhahran 31261 Saudi Arabia Imam Abdulrahman bin Faisal Univ Coll Comp Sci & Informat Technol Comp Sci Dept Dammam 31441 Saudi Arabia King Fahd Univ Petr & Minerals SDAIA KFUPM Joint Res Ctr Artificial Intelligence Dhahran 31261 Saudi Arabia

Neural Architecture Search (NAS) has emerged as a pivotal technique in optimizing the design of Generative Adversarial Networks (GANs), automating the search for effective architectures while addressing the challenges inherent in manual design. This paper provides a comprehensive review of NAS methods applied to GANs, categorizing and comparing various approaches based on criteria such as search strategies, evaluation metrics, and performance outcomes. The review highlights the benefits of NAS in improving GAN performance, stability, and efficiency, while also identifying limitations and areas for future research. Key findings include the superiority of evolutionary algorithms and gradient-based methods in certain contexts, the importance of robust evaluation metrics beyond traditional scores like Inception Score (IS) and Fr & eacute;chet Inception Distance (FID), and the need for diverse datasets in assessing GAN performance. By presenting a structured comparison of existing NAS-GAN techniques, this paper aims to guide researchers in developing more effective NAS methods and advancing the field of GANs.

关键词： GANs Neural Architecture Search architecture search evolutionary algorithms reinforcement learning gradient-based search

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A Surrogate-Assisted Multi-objective evolutionary Algorithm Guided by Hybrid Reference Points 15th

A Surrogate-Assisted Multi-objective Evolutionary Algorithm ...

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15th International Conference on Advances in Swarm Intelligence (ICSI)

作者： Li, Shuxian Zhang, Yong Wang, Qing He, Linchun Li, Huijun Ye, Bin China Univ Min & Technol Xuzhou 221000 Jiangsu Peoples R China

ISBN: (纸本)9789819771806;9789819771813

Surrogate-assisted multi-objective evolutionary algorithms show excellent performance in solving expensive multi-objective optimization problems, but most of them do not work well for discontinuities Pareto fronts(PFs). To address this problem, a surrogate-assisted multi-objective evolutionary algorithm guided by hybrid reference points is proposed in this paper. The algorithm introduces a discontinuous region boundary point identification strategy to recognize the discontinuous information of PFs and set the reference points. Moreover, a two-stage multi-reference point-assisted management strategy is developed, which enables the algorithm to obtain better performance on different irregular discontinuous PFs. Experimental results show that the algorithm outperforms comparison algorithms on most of the problems with discontinuous PFs.

关键词： Expensive multi-objective Surrogate-assisted evolutionary algorithms

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