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An efficient enhanced exponential distribution optimizer: applications in global, engineering, and combinatorial optimization problems

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JOURNAL OF BIG DATA 2025年第1期12卷 1-55页

作者： Emam, Marwa M. Saad, Mohammed R. Younan, Mina Houssein, Essam H. Minia Univ Fac Comp & Informat Al Minya Egypt Luxor Univ Fac Comp & Informat Luxor Egypt Minia Natl Univ Al Minya Egypt

This paper introduces an enhanced exponential distribution optimizer (EDO), termed the exponential distribution optimizer with Levy flight orthogonal learning (EDO-LFOL). EDO-LFOL enhances EDO by integrating the Levy flight (LF) strategy during the intensification phase and utilizing the orthogonal learning (OL) approach after the end of the optimization cycle. This adjustment aims to mitigate the risk of local optima entrapment and improve the quality of the solutions obtained. This paper presents EDO-LFOL as a viable global and practical optimization problem solution. To evaluate the effectiveness of EDO-LFOL, we compare it against seven robust optimizers across 12 unconstrained test functions associated with the IEEE Congress on Evolutionary Computation 2022 (CEC 2022). The optimizers include the improved multi-operator differential evolution algorithm (IMODE), adaptive guided differential evolution algorithm (AGDE), whale optimization algorithm (WOA), grey wolf optimizer (GWO), sinh cosh optimizer (SCHO), RIME optimization algorithm (RIME), and the original EDO. Additionally, EDO-LFOL is tested on three combinatorial optimization challenges: the job shop scheduling problem (JSSP), quadratic assignment problem (QAP), and bin packing problem (BPP), to evaluate its applicability. Furthermore, EDO-LFOL addresses four distinct design engineering challenges: the pressure vessel design, three-bar truss, welded beam, and speed reducer. The results, supported by significance tests, demonstrate that EDO-LFOL significantly outperforms the standard EDO and its competitors.

关键词： metaheuristic algorithms Exponential distribution optimizer Levy flight Orthogonal learning Combinatorial problems Engineering design problems

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Performance Optimization of the Nonlinear Response of Steel Buildings Controlled Using Nonlinear Energy Sinks

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JOURNAL OF STRUCTURAL ENGINEERING 2025年第6期151卷

作者： Amara, Ismail I. Mahmoud, Hussam Elanwar, Hazem Mourad, Sherif A. Cairo Univ Fac Engn Dept Struct Engn Giza 12316 Egypt Colorado State Univ Dept Civil & Environm Engn Ft Collins CO 80523 USA Colorado State Univ Dept Civil & Environm Engn Strctural Lab Ft Collins CO 80523 USA

There is a need for more effective seismic protection for nonlinear steel buildings using linear tuned mass dampers (TMDs) and nonlinear energy sinks (NES). This research builds on a analytical framework established focusing on enhancing the seismic resilience of structures through optimal damper design using a proposed multi-input optimization (MIO) process. The optimization of the dampers was performed the two well-known metaheuristic algorithms, genetic algorithms (GAs) and particle swarm optimization (PSO), aiming at minimizing the dynamic response and maximizing the energy absorption under varied earthquake excitations. Then, a sensitivity analysis was performed to reveal the downside of the damper optimization based on a single excitation. Also, a comparison between the effect of two types of passive control systems, i.e., TMD and NES, on an inelastic building is carried out. The proposed technique sheds a new light on damper optimization whereby the optimized dampers are robust and efficient for an applied set of earthquake-induced ground motions.

关键词： metaheuristic algorithms Inelasticity Tuned mass damper Nonlinear energy sink Earthquakes

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A novel application-aware retired lithium-ion batteries regrouping approach to enable improved second life

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JOURNAL OF CLEANER PRODUCTION 2025年 489卷

作者： Piombo, Gabriele Niri, Mona Faraji Marco, James Univ Warwick WMG Coventry CV4 7AL England

Amidst increasing environmental concerns, the transport sector is undergoing a substantial transformation leading to a larger market share of electric vehicles and, in turn, a growing number of retired lithium- ion batteries. The reuse of these batteries in second-life applications can improve circularity by reducing environmental impacts and costs. Nevertheless, the performance of Second Life Batteries (SLB) is compromised by cell-to-cell variations, particularly in parallel-connected blocks, caused by the early-stage of the screening and grouping methodologies at end-of-life. This paper contributes to address SLB performance disparities by proposing an innovative application-aware cell (re-)grouping methodology capable of optimising of series- parallel modules creation using Particle Swarm Optimisation (PSO) and Genetic algorithms (GA). The a posteriori grouping demonstrate superior performance over conventional application-agnostic screening and clustering methods by significantly reducing modules current (-34.4%) and thermal (-20.2%) gradients, and enhancing their Remaining Useful Life (RUL) (+22.7%) and capacity (+1.0%). The retired batches' exploitation is also improved, with a lower scrap rate and higher number of high-performance modules, potentially positively impacting the cost and reducing the environmental footprint of SLB solutions. The study not only underscores the framework's potential in optimising SLB lifecycle performance but also paves the way for a shift in the design paradigm towards a whole lifecycle perspective, welcoming future research to validate and refine these methodologies.

关键词： Lithium-ion battery Second-life battery metaheuristic algorithms Circular economy Digital twin

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Compiler-level DMA-aware multi-objective dynamic SPM allocation

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REAL-TIME SYSTEMS 2025年第1期61卷 53-117页

作者： Jadhav, Shashank Falk, Heiko Hamburg Univ Technol Inst Embedded Syst Hamburg Germany

Real-time embedded systems need to meet timing and energy constraints to avoid potential disasters. Compiler-level ScratchPad Memory (SPM) allocation can be used to optimize a program's Worst-Case Execution Time (WCET) and energy consumption. However, static allocation is limited by SPM size constraints. Dynamic SPM allocation resolves this by allocating code to SPM during runtime, but copying code using the CPU increases WCET and energy consumption. To address this, we integrate a Direct Memory Access (DMA) model and DMA analysis at the compiler level and propose a single-objective DMA Call Placement Optimization (DCPO). In this paper, we consider functions and loops as dynamic allocation candidates. DCPO finds appropriate places within the code to place DMA transfer calls such that the DMA controller and the CPU run parallelly-minimizing the total execution time required by the DMA controller for dynamic allocation of functions and loops during runtime. Additionally, we propose a compiler-level DMA-aware multi-objective dynamic SPM allocation that uses DCPO and simultaneously minimizes WCET and energy objectives, yielding Pareto optimal solutions. Comparative evaluations demonstrate the superiority of our approach over state-of-the-art multi- and single-objective optimizations.

关键词： Multi-objective optimization Embedded systems Dynamic SPM allocation DMA metaheuristic algorithms ILP

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EHHO-EL: a hybrid method for software defect detection in software product lines using extended Harris hawks optimization and ensemble learning

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JOURNAL OF SUPERCOMPUTING 2025年第4期81卷 1-34页

作者： Habibzadeh-khameneh, Mehdi Nabiollahi-Najafabadi, Akbar Tavoli, Reza Rastegari, Hamid Islamic Azad Univ Fac Comp Engn Najafabad Branch Najafabad Iran Islamic Azad Univ Big Data Res Ctr Najafabad Branch Najafabad Iran Islamic Azad Univ Dept Comp Engn Chalous Branch Chalous Iran

Software Product Lines (SPLs) aim to reduce development costs and timelines, while improving quality, but their inherent complexity often results in defects and delays. One of the key challenges in SPLs is selecting relevant features from a vast pool of software metrics for defect detection. To address this issue, this study introduces a hybrid approach combining Harris Hawks Optimization (HHO) with stacking-based ensemble learning. The HHO algorithm is further enhanced with the Chaos Optimization Algorithm (COA) to overcome local optima, making it effective for high-dimensional and complex problems. The method is evaluated using the LVAT and NASA repositories, with four datasets from each source. It achieves accuracies of 92.7%, 91.1%, 96.3%, and 98.4% on LTS1, LTM2, LTL3, and LTV4, and 97.91%, 99.01%, 94.21%, and 90.93% on CM1, JM1, KC1, and PC1. The results highlight the proposed method's enhanced efficiency and faster convergence compared to existing approaches.

关键词： Software product lines metaheuristic algorithms Software defect detection Harris hawks optimization Stacking-based ensemble learning Product defects

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Virtual Closed-Loop Supply Chain Considering Secondary Markets, Internet of Things and Reliability

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 200卷

作者： Shirooyehpoor, Kianmehr Samouei, Parvaneh Bu Ali Sina Univ Fac Engn Dept Ind Engn Hamadan Iran

Today, due to the progress of manufacturing industries, the increase of physical limitations, and the growing needs of customers, many of the traditional supply chains cannot trade and compete at international levels. Therefore, in this article, a virtual closed-loop supply chain has been designed using the secondary market and the Internet of Things (IoT) to overcome limitations. Because products are returned in this network, a secondary market approach has been used for returned products. By utilizing RFID tags, it becomes possible to identify and track each product to use their changeable or repairable pieces and parts after disassembling to manufacture some products with different prices and qualities for the secondary market. Hence, few pieces are expected to be destroyed, and less environmental damage will occur while costs will be reduced. The objective functions of the model are total profit maximization, system reliability in the face of power and internet disruptions maximization, and data registration and processing speed maximization by reducing latency. Numerical examples and parameter sensitivity analysis are solved using the LP-Metrics method and within large dimensions using three NSGA-II, SPEA2, and MOEA/D algorithms. The efficiency of these algorithms is evaluated based on five indicators. Statistical analyses indicate that these algorithms are not significantly different in terms of four indicators at a significance level of 5%. In contrast, the MOEA/D algorithm is different from the other algorithms regarding the NPS index.

关键词： Virtual Closed-Loop Supply Chain Secondary Market Internet of Things metaheuristic algorithms RFID Tag Reliability

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Residential building cooling load prediction with optimized KELM models and interpretability insights

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APPLIED THERMAL ENGINEERING 2025年 272卷

作者： Zhang, Yulin Li, Enming Gu, Jiehao Du, Kun Zhou, Jian Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China Univ Politecn Madrid ETSI Minas & Energia Rios Rosas 21 Madrid 28003 Spain

In building energy consumption, the cooling load (CL) for residential buildings plays a crucial role in energy efficiency, serving as an essential indicator for maintaining optimal indoor air conditions. However, traditional methods often struggle to provide accurate predictions. To address this challenge, a dataset comprising 3840 records from 721 villas in the Qassim region of Saudi Arabia is collected for the development of a CL prediction model. Following input parameter correlation analysis, six key residential building parameters are selected as input variables. The Welch test is then employed to ensure consistency between the training and testing sets. To optimize the kernel extreme learning machine (KELM) models, three novel meta-heuristic algorithms are applied: the red-tailed hawk algorithm (RTH), the sinh-cosh optimizer (SCHO), and the triangulation topology aggregation optimizer (TTAO). The results demonstrate that the RTH-KELM model exhibits the best overall CL prediction performance. Its evaluation metrics for the testing set are: R2 (coefficient of determination) = 0.99440, MAPE (mean absolute percentage error) = 2.21790, VAF (variance accounted for) = 99.43757, and RMSE (root mean square error) = 1.66606. The model is further validated using additional methods including Taylor Diagrams, error distribution analysis and overfitting assessment, confirming its robustness and generalization capability. When compared with other none-optimized machine learning and deep learning approaches, the proposed hybrid models demonstrate superior prediction accuracy, outperforming state-of-the-art studies in the field. Interpretability analysis is conducted using two techniques: SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). The results reveal that glazing area and window glazing U-value have a significant positive impact on CL prediction. However, when the glazing area exceeds 1.41 m2, this positive effect on CL outp

关键词： Residential building Cooling load metaheuristic algorithms Kernel extreme learning machine Interpretability analysis

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SL-COA:Hybrid Efficient and Enhanced Coati Optimization Algorithm for Structural Reliability Analysis

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Computer Modeling in Engineering & Sciences 2025年第4期143卷 767-808页

作者： Yunhan Ling Huajun Peng Yiqing Shi Chao Xu Jingzhen Yan Jingjing Wang Hui Ma China Academy of Machinery Beijing Research Institute of Mechanical&Electrical Technology Co.Ltd.Beijing100083China AVICGuizhou Anda Aviation Forging Co. Ltd.Anshun561000China School ofMechanical and Electrical Engineering University of Electronic Science and Technology of ChinaChengdu611731China

Thetraditional first-order reliability method(FORM)often encounters challengeswith non-convergence of results or excessive calculation when analyzing complex engineering *** improve the global convergence speed of structural reliability analysis,an improved coati optimization algorithm(COA)is proposed in this *** this study,the social learning strategy is used to improve the coati optimization algorithm(SL-COA),which improves the convergence speed and robustness of the newheuristic optimization ***,the SL-COAis comparedwith the latest heuristic optimization algorithms such as the original COA,whale optimization algorithm(WOA),and osprey optimization algorithm(OOA)in the CEC2005 and CEC2017 test function sets and two engineering optimization design *** optimization results show that the proposed SL-COA algorithm has a high ***,this study introduces the SL-COA algorithm into the MPP(Most Probable Point)search process based on FORM and constructs a new reliability analysis ***,the proposed reliability analysis method is verified by four mathematical examples and two engineering *** results show that the proposed SL-COA-assisted FORM exhibits fast convergence and avoids premature convergence to local optima as demonstrated by its successful application to problems such as composite cylinder design and support bracket analysis.

关键词： Hybrid reliability analysis single-loop interactive hybrid analysis most probability point metaheuristic algorithms coati optimization algorithm

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Enhanced Harmony Search Algorithm for Maximum Power Point Tracking Under Partial Shading in PV Systems

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IEEE ACCESS 2025年 13卷 51900-51910页

作者： Lee, Geon Hee Geem, Zong Woo Won, Jehyuk Gachon Univ Dept Next Generat Smart Energy Syst Convergence Seongnam 13120 South Korea Gachon Univ Coll IT Convergence Engn Seongnam 13120 South Korea Kangwon Natl Univ Coll IT Dept Elect & Elect Engn Chunchon 24341 South Korea

Multiple power peaks are introduced under nonuniform irradiation conditions in photovoltaic (PV) systems. However, conventional maximum power point tracking (MPPT) algorithms, such as the perturb-and-observe (P&O) algorithm, are only effective under homogeneous irradiation conditions. Therefore, there is a need for an efficient method for global maximum power point (GMPP) tracking under partial shading conditions (PSCs). This paper proposes a novel MPPT algorithm based on harmony search (HS). The proposed algorithm not only overcomes the power loss induced by a wide search but also avoids falling into local maximum power points under PSCs. A parameter-setting-free (PSF) HS algorithm is introduced, which is further improved for the MPPT problem. Furthermore, the algorithm integrates a reduced-range (RR) operator to reduce the convergence time. To validate the performance of the proposed algorithm, experiments are performed using a 250-W PV system with a PV emulator under different PSCs. The results show that the proposed algorithm outperforms conventional HS and particle swarm optimization algorithms in terms of the number of iterations and convergence time, while maintaining a similar level of accuracy.

关键词： Convergence Maximum power point trackers Bandwidth Voltage control Photovoltaic systems Linear programming Accuracy Tuning Particle swarm optimization metaheuristics Harmony search (HS) maximum power point tracking (MPPT) metaheuristic algorithms partial shading condition (PSC) photovoltaic (PV) system

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A Systematic Review and Evolutionary Analysis of the Optimization Techniques and Software Tools in Hybrid Microgrid Systems

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ENERGIES 2025年第7期18卷 1770-1770页

作者： Tahir, Kawakib Arar Univ Granada Dept Construct Engn & Engn Projects ETSICCP Campus Fuentenueva S-N Granada 18071 Spain

This study systematically reviews the optimization techniques (OTs) and software tools (STs) in hybrid microgrid systems (HMGSs) to enhance the efficiency, cost-effectiveness, and energy reliability. An advanced Scopus search was conducted using core keywords related to microgrids, renewable energy systems, and various OTs and STs, which identified 4134 relevant documents on OTs. These were classified into classical (16.87%), metaheuristic (47.12%), and artificial intelligence (AI)-based methods (36.01%), highlighting the dominance of metaheuristics and the growing role of AI-driven approaches in handling uncertainties and real-time decision-making. Additionally, 2667 documents on STs were analyzed, identifying MATLAB/Simulink (65.34%) and HOMER (22.08%) as the most widely used tools for simulation, modeling, and techno-economic analysis. This study identifies key research trends, highlights gaps in the optimization strategies, and emphasizes the need for AI integration, broader adoption of open-source tools, and scalable optimization frameworks. By mapping the evolution and effectiveness of OTs and STs, it provides valuable insights for researchers, policymakers, and industry professionals, supporting the development of sustainable and intelligent HMGS solutions.

关键词： renewable energy systems hybrid microgrid systems optimization techniques artificial intelligence (AI) metaheuristic algorithms software tools

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