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Bi-objective minimization of energy consumption and cycle time for the robotic assembly line balancing problem: pseudo-polynomial case and reduced search space metaheuristic

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2025年第0期

作者： Lahrichi, Youssef Gamoura, Samia Chehbi Damand, David Barth, Marc ISCAE Km 95 Route Nouasseur BP 8114 Casablanca Morocco Univ Strasbourg EM Strasbourg Business Sch 61 Ave Foret Noire F-67000 Strasbourg France

Energy consumption and cycle time are two contradictory optimization objectives for the robotic assembly line balancing problem (RALBP). Indeed, minimizing the cycle time leads to choosing the fastest robots, while minimizing the energy consumption leads to choosing the robots with the smallest powers. In the context of RALBP, cycle time minimization has been extensively studied while energy minimization has been much less considered. Studies dealing with simultaneous minimization of the two later are even scarcer. A bi-objective RALBP considering simultaneous minimization of cycle time and energy consumption is studied in this paper. The energy consumption is calculated based on recent papers from the literature. It includes energy consumed during both operation time and idle time. In this paper, a pseudo-polynomial case is solved thanks to an exact algorithm called split. This latter enumerates all Pareto-optimal solutions corresponding to a given giant sequence of operations. Split is then used as a decoder in a metaheuristic operating in a reduced search space where giant sequences encode solutions. An experimental study is performed on instances taken from the literature to test the suggested encoding-decoding scheme. It shows that the suggested approach yields competitive results compared to the literature.

关键词： assembly line balancing metaheuristics nondominated sorting genetic algorithm ii pseudo-polynomial case energy consumption multiobjective optimization

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Diesel Engine Urea Injection Optimization Based on the Crested Porcupine Optimizer and genetic algorithm

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APPLIED SCIENCES-BASEL 2025年第9期15卷 5195-5195页

作者： Chen, Xu Ma, Changhai Dou, Quanli Bai, Shuzhan Sun, Ke Li, Zhenguo Shandong Univ Sch Nucl Sci & Energy Power Jinan 250061 Peoples R China Weichai Power Co Ltd Weifang 261041 Peoples R China China Automot Technol & Res Ctr Co Ltd Natl Engn Lab Mobile Source Emiss Control Technol Tianjin 300300 Peoples R China

As a major emission pollutant from diesel engines, NOx is extremely harmful to the environment and human health. In order to reduce NOx emissions, countries around the world have been implementing increasingly stringent emissions regulations. The urea injection strategies of the Selective Catalytic Reduction (SCR) system are the main factors affecting NOx emissions and NH3 slips of diesel engines. In this study, test data were obtained from an engine test stand and a Support Vector Machine (SVM) was developed using the test data to predict NOx conversion efficiency and NH3 slip. The SVM model was optimized using the Crested Porcupine Optimizer (CPO) to improve its prediction accuracy and was made to replace the mathematical model to save computational time. Finally, the nondominated sorting genetic algorithm ii (NSGA-ii) was used to optimize the urea injection volume for all conditions. The optimized urea injection volume maximizes the NOx conversion efficiency of the SCR system while controlling the NH3 slip within 10 ppm. In addition, based on this method, the urea injection pulse spectrum under full operating conditions was obtained, and the optimized urea injection amount can effectively reduce the NOx accumulation of the WHTC cycle by about 7.5%, as shown through bench testing.

关键词： crested porcupine optimizer support vector machine nondominated sorting genetic algorithm ii selective catalytic reduction urea injection

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Hybrid Optimization Approach Using Multiobjective genetic algorithm NSGA-ii, SCAPS-1D Simulation, and Response Surface Methodology for Organic Solar Cell Analysis

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PHYSICA STATUS SOLIDI A-APPLICATIONS AND MATERIALS SCIENCE 2024年第2期222卷

作者： Moulebhar, Samia Bendenia, Chahrazed Merad-Dib, Hanaa Bendenia, Souhila Merabet, Sarra Khantar, Sid Ahmed Univ Abd El Hamid Ibn Badis Fac Sci & Technol Mostaganem 27000 Algeria

In the field of simulation, it is difficult to find the relevant values for the properties of materials and in this context this approach has been proposed on optimizing the performance of organic solar cells, a promising technology in the field of renewable energy, to increase their efficiency. It adopts a hybrid approach combining the response surface methodology (RSM) with a Box-Behnken design (BBD) and the nondominated sorting genetic algorithm ii (NSGA-ii). The RSM BBD method is used to identify objective functions to be optimized, considering interactions between selected parameters such as the thickness of the active layer, electron-transport layer (ETL), hole-transport layer (HTL), and the doping of these layers. Concurrently, the NSGA-ii genetic algorithm aims to maximize the performance of the solar cell based on these parameters. The specific importance of NSGA-ii lies in its ability to solve complex multiobjective optimization problems. Indeed, NSGA-ii is designed to simultaneously manage several performance objectives, which is crucial for organic solar cells. Its ability to generate a diverse set of optimal solutions enables efficient configurations to be found that may not be obvious with simpler optimization approaches. The results of this study show that optimum solar cell performance is achieved with active layer, ETL layer, and HTL layer thicknesses of 100.86, 79.9, and 20.24 nm, respectively, and active layer doping of 8.71E + 21 cm-3, HTL layer doping of 9.90E + 21 cm-3, and ETL layer doping of 9.49E + 21 cm-3. Analysis using Solar Cell Capacitance Simulator-1D (SCAPS-1D) software shows that optimum performance is achieved with these specific parameter values. After optimization with NSGA-ii, the power conversion efficiency increases by 39% compared to previous work. This study provides evidence of the effectiveness of the proposed hybrid approach for optimizing the performance of organic solar cells. By showing remarkable agreement between the

关键词： nondominated sorting genetic algorithm ii optimization organic solar cells response surface methodology solar cell capacitance simulator-1D

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Strategy and capacity optimization of renewable hybrid combined cooling, heating and power system with multiple energy storage

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APPLIED THERMAL ENGINEERING 2024年 242卷

作者： Wang, Zhi Li, Shengxu Cai, Wenkui Li, Peng Deng, Jun North China Elect Power Univ Sch Energy Power & Mech Engn Dept Power Engn Baoding 071003 Peoples R China

Combined cooling, heating, and power systems offer significant potential for integration with renewable energy sources, such as solar and geothermal energy, alongside energy storage devices. However, the effectiveness and feasibility of these systems depend crucially on the operational strategies and capacity planning for each component. Therefore, an innovative hourly dynamic simulation model that integrates solar energy, geothermal energy, and multiple energy storage devices is established. Using the multi-objective comprehensive evaluation method that combines Technique for Order Preference by Similarity to Ideal Solution methods and nondominated sorting genetic algorithm-ii including elite strategy, establish five dimensions of multi-objective optimization model, including energy-saving, economy, environmental protection, system independence and renewable energy utilization scale. Meanwhile, the system and reference system in a community in Beijing are used as models to analyze the equipment capacity under the three operating strategies. The findings reveal that, compared to the reference system, the new system attains optimal performance when following the electrical load strategy. Its comprehensive index reaches 69%, surpassing the thermal load and hybrid load strategies by 8% and 11%, respectively. When the target is set as "independence-renewable energy-cost," the utilization rate of solar energy and geothermal energy reaches an impressive 79%, showcasing excellent renewable energy utilization. The presented model holds substantial significance for advancing research in the realms of operating strategies and equipment capacity planning within such systems.

关键词： Combined cooling heating and power system Capacity optimization Operation strategy nondominated sorting genetic algorithm ii

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The multi-objective optimization of an axial cyclone separator in the gas turbine

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2022年第3期46卷 3428-3442页

作者： Xing, Xiaolong Pu, Wenhao Zhang, Qi Yang, Yu Han, Dong Nanjing Univ Aeronaut & Astronaut Key Lab Thermal Management & Energy Utilizat Airc Minist Ind & Informat Technol Coll Energy & Power Engn Nanjing 210016 Jiangsu Peoples R China Nanjing Normal Univ Sch Energy & Mech Engn Nanjing Peoples R China

Herein the multi-objective optimization of an axial cyclone separator is performed to enhance the overall performance with different velocities. And the separation efficiency and pressure drop are expected as the objective functions. In this paper, the difference value between internal and external blade outlet angles is regarded as one of the parameters to optimize. The regression expressions, which are obtained by Box-Behnken Design and the two-fluid model, are used in nondominated sorting genetic algorithm ii to maximize separation efficiency and minimize pressure drop. It is found that the separation efficiency has a peak value with the rise of inlet velocity, and the response degrees of the internal angle, difference of the two angles, and blade number is disparate at different velocities. The pressure drops of the optimal axial cyclone tubes are smaller than the initial ones, but the separation efficiency is almost the same. Meanwhile, the accuracies of the optimization and numerical results are verified by the experiments. For the samples in 3, 5, and 7 m/s, pressure drop can be reduced by 24.1%, 22.7%, and 34.3% respectively. The simulation results indicate that the radial pressure difference and swirl number have been reduced. Besides, the optimal designs have more stable and even near wall particle distribution after multi-objective optimization.

关键词： axial cyclone separator blade outlet angles multi-objective optimization nondominated sorting genetic algorithm ii two-fluid model

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Leveraging the hierarchical symmetric 2-Additive Choquet Integral: Enhancing explainability and parallelizability in predictive models

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INFORMATION SCIENCES 2024年 678卷

作者： Huang, Jih-Jeng Chen, Chin-Yi Soochow Univ Dept Comp Sci & Informat Management 56 Kueiyang StSect 1 Taipei 100 Taiwan Chung Yuan Christian Univ Dept Business Adm 200 Chung Pei Rd Taoyuan 320 Taiwan

The demand for transparent and efficient predictive models has grown significantly in the era of big data and complex decision-making. Explainable artificial intelligence (XAI) has emerged as a crucial field to address the "black box" nature of many state-of-the-art models, particularly in domains such as healthcare, where understanding the reasoning behind predictions is essential. However, a key challenge lies in developing models that balance explainability and accuracy while also being computationally efficient. This research introduces a pioneering algorithm that leverages the hierarchical symmetric 2-additive Choquet integral to enhance interpretability and parallelizability in predictive modeling, thereby addressing this critical research gap. Empirical evaluations on diverse datasets, both simulated and real, demonstrate that our algorithm outperforms traditional models in prediction accuracy. This advancement underscores the potential of our algorithm to serve as a versatile tool in the field of XAI, where clarity in the decisionmaking process is paramount. Our work thus presents a significant stride in developing algorithms that are not only accurate but also intuitively understandable, catering to the increasing demand for transparency in artificial intelligence applications.

关键词： Explainable artificial intelligence (XAI) Choquet integral nondominated sorting genetic algorithm ii (NSGA-ii) Neural networks Fuzzy measures

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Multiobjective optimization of bridge and viaduct design: Comparative study of metaheuristics and parameter calibration

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ENGINEERING STRUCTURES 2024年 312卷

作者： Trentini, Eduardo Vicente Wolf Parsekian, Guilherme Aris Bittencourt, Tulio Nogueira Univ Fed Uberlandia UFU Fac Engn Civil Ave Joao Naves Avila2121 Santa Mon Uberlandia MG Brazil Univ Fed Sao Carlos UFSCar Programa Posgraduacao Engn Civil Rodovia Washington Luiskm 235 Sao Carlos SP Brazil Univ Sao Paulo Escola Politecn Ave Prof Luciano Gualberto380 Butanta Sao Paulo SP Brazil

This article focuses on multiobjective optimization in the design of bridges and viaducts. The problem is characterized as a multi-objective optimization, with the objective functions being the construction cost, the environmental impact (CO2 emissions) and the design service life. For optimization, three commonly used metaheuristics in structural optimization problems were tested: Multiple Objective Particle Swarm Optimization (MOPSO), nondominated sorting genetic algorithm ii (NSGA-ii) and Strength Pareto Evolutionary algorithm 2 (SPEA2). The results showed that MOPSO outperformed the other methods, achieving the highest hypervolume and Pure Diversity values. Once the best performing metaheuristic was defined, the calibration of the MOPSO parameters was then developed to improve the quality of the solutions found, reduce the execution time and increase the robustness of the algorithm. Using the Taguchi method with an orthogonal matrix consisting of 54 experiments, five parameters were evaluated at three different levels, totaling 270 analyses. The results indicated that parameter calibration led to an increase in the average hypervolume compared to the results before calibration. Moreover, the coverage of two sets revealed the superior performance of the calibrated set, demonstrating better trade-offs among the objective functions.

关键词： Bridge Viaduct Multiple objective particle swarm optimization nondominated sorting genetic algorithm ii Strength pareto evolutionary algorithm 2

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Optimization of Thermal Non-Uniformity Challenges in Liquid-Cooled Lithium-Ion Battery Packs Using NSGA-ii

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Journal of Electrochemical Energy Conversion and Storage 2025年第4期22卷 041002页

作者： Zhou, Long Li, Shengnan Jain, Ankur Sun, Guanghua Chen, Guoqiang Guo, Desui Kang, Jincan Zhao, Yong School of Mechanical and Power Engineering Henan Polytechnic University Jiaozuo Henan 454003 China Department of Mechanical and Aerospace Engineering The University of Texas at Arlington Arlington TX 76019 https://***/05vr1c885Henan Polytechnic University School of Business Administration Henan Polytechnic University Jiaozuo Henan 454003 China https://***/05vr1c885Henan Polytechnic University School of Mechanical and Power Engineering Henan Polytechnic University Jiaozuo Henan 454003 China Henan THB Electric Company Hebi Henan 453104 China

Heat removal and thermal management are critical for the safe and efficient operation of lithium-ion batteries and packs. Effective removal of dynamically generated heat from cells presents a substantial challenge for thermal management optimization. This study introduces a novel liquid cooling thermal management method aimed at improving temperature uniformity in a battery pack. A complex nonlinear hybrid model is established through traditional full-factor design and back propagation neural network (BPNN) approximation. This model links input parameters such as the number of baffles, baffle angle, and inlet speed to output parameters including maximum temperature, temperature difference, and pressure drop. Global multiobjective optimization is carried out using the nondominated sorting genetic algorithm ii to sidestep locally optimal solutions. Pareto optimal solutions are sorted using multiple criteria decision-making techniques. Through thermal management optimization, the maximum temperature rise of the battery relative to the initial temperature is controlled within 7.68 K, the temperature difference is controlled within 4.22 K (below the commonly required 5 K), and the pressure drop is only 83.92 Pa. Results presented in this work may help enhance the performance and efficiency of battery-based energy conversion and storage. The optimization technique used in this work helps maximize the benefit of an innovative battery thermal management technique.

关键词： Thermal management Design Heat Batteries Optimization Cooling systems nondominated sorting genetic algorithm ii Cooling optimization Pressure drop Lithium-ion batteries lithium-ion cells Temperature liquid-cooled thermal management back propagation neural network genetic algorithms

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Dynamic networking of islanded regional multi-microgrid networks based on graph theory and multi-objective evolutionary optimization

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2021年第1期31卷 p1-27页

作者： Ge, Leijiao Song, Zhaoshan Xu, Xiandong Bai, Xingzhen Yan, Jun Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Shandong Univ Sci & Technol Sch Elect & Engn Shandong Peoples R China Concordia Univ Concordia Inst Informat Syst Engn Montreal PQ Canada

With the rapid development of microgrids, dynamic regional multi-microgrid networks are emerging as an efficient and flexible solution in smart distribution networks. To optimize the dynamic networking of multi-microgrid for better economics and reliability, this article proposes an evolutionary optimization method based on graph theory. This article first reviews the need for dynamic networking in multi-microgrids. Then a dynamic networking model is proposed based on graph theory and solved by the nondominated sorting genetic algorithm ii, which can provide a set of Pareto-optimal solutions efficiently. The proposed method is tested by a regional multi-microgrid network in an industrial park in Northern China. The results showed that the daily operating costs of the regional multi-microgrid can be reduced by 17.4% with the proposed dynamic networking method. When faults are considered, the daily operation costs can be reduced by 11.0%, and the system average interruption frequency index value is reduced from 0.27 to 0.19. The results also demonstrated the efficiency of the proposed algorithm over other evolutionary computing methods in terms of both runtime and convergence with promising applications in real world scenarios. Through simulations in the IEEE 33-node system, different operation programs are provided according to the preferences of operators under normal conditions. The operating cost and active power loss are reduced under failure conditions, which shows the effectiveness of the proposed method. The work in this article is expected to provide some reference for the application of regional multi-microgrid.

关键词： dynamic networking graph theory nondominated sorting genetic algorithm ii regional multi‐ microgrid

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Optimal low-impact development Facility Design in Urban Environments: A multidimensional optimization approach employing slime mould and nondominated sorting genetic algorithms

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URBAN CLIMATE 2024年 55卷

作者： Eskandaripour, Mehrtash Soltaninia, Shahrokh Isfahan Univ Technol IUT Dept Civil Engn Esfahan Iran Islamic Azad Univ Dept Nat Resource Environm Sci & Res Branch Tehran Iran

This study presents the application of the slime mould algorithm (SMA) and the nondominated sorting genetic algorithm ii (NSGA-ii) to optimize low-impact development (LID) strategies in urban areas. The focus is on minimizing costs and improving water quality, using three LID practices (vegetated swales, bioretention systems, and porous pavements) in combination with a drainage system. The effectiveness of this stormwater management model (SWMM)-SMA-NSGA-ii model is demonstrated in Tehran, Iran, where bioretention is found to be the most successful approach for improving water quality. The results also show that the SMA outperforms the NSGAii in optimizing cost-effective LID solutions and is more computationally efficient, potentially due to its crossover operator. This research highlights the importance of considering qualitative aspects of urban runoff management. The study demonstrates that the combination of multiple LID strategies and the use of SMA and NSGA-ii have the potential to achieve optimal solutions for managing stormwater in urban areas.

关键词： Urban runoff Low-impact development Storm water management model Multi-objective optimization Slime mould algorithm nondominated sorting genetic algorithm ii

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