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multi-objective orbital maneuver optimization of multi-satellite using an adaptive feedback learning NSGA-II

SWARM AND EVOLUTIONARY COMPUTATION 2025年 93卷

作者： Yin, Qian Wu, Guohua Sun, Guang Gu, Yi Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Peoples R China Cent South Univ Sch Automat Changsha 410075 Peoples R China Space Star Technol Co Ltd Beijing 100086 Peoples R China

Earth observation satellite (EOS) systems playa crucial role in performing emergency monitoring tasks such as natural disasters. In terms of urgent observation tasks within a limited period, manipulating the orbit EOSs to meet emergency requirements is an efficient scheme. The traditional multiple satellite orbit maneuver optimization problem (MSOMOP) almost considers single objective optimization, neglecting the optimization conflicting objectives in practical applications. This paper is devoted to conducting multi-objective optimization research for the MSOMOP. First, a multi-objective mathematical model is established, where the response time, imaging resolution, and fuel cost are considered as optimization objectives. Subsequently, an adaptive feedback learning of non-dominated sorting genetic algorithm-II (AFL-NSGA-II) is proposed, which introduces the idea of adaptive strategy and a feedback learning mechanism into the traditional NSGA-II. The AFL-NSGAII incorporates an increased learning mechanism and adaptive strategies, which facilitates efficient solution search and reduces the risk of converging to a local optimum. Moreover, several problem-specific designed operators are incorporated into the algorithm to enhance the search capability. Finally, we conduct extensive experimental studies to verify the efficiency of the proposed algorithm. Experiment results demonstrate that the proposed AFL-NSGA-II outperforms three existing algorithms and exhibits superior performance in typical scheduling scenarios.

关键词： Earth observation satellite (EOS) Orbital maneuver multi-objective optimization Improved NSGA-II Adaptive strategy

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Towards climate-responsive net zero energy rural schools: A multi-objective passive design optimization with bio-based insulations, shading, and roof vegetation

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SUSTAINABLE CITIES AND SOCIETY 2025年 120卷

作者： Es-sakali, Niima Pfafferott, Jens Mghazli, Mohamed Oualid Cherkaoui, Moha Green Energy Pk IRESEN UM6P Benguerir 43150 Morocco Offenburg Univ Appl Sci Inst Energy Syst Technol INES Offenburg Germany Univ Lyon Ecole Cent Lyon ENTPE CNRSLTDSUMR 5513 F-69518 Vaulx En Velin France Rabat Natl Sch Mines LMAID Lab Rabat 10070 Morocco

Given the importance of reducing energy bills in the building sector, especially for schools located in rural areas, where detachment from the grid electricity is recommended, achieving energy self-sufficiency is crucial to provide a conducive indoor environment for students while minimizing energy costs. Therefore, this paper presents a comprehensive methodology aimed at enhancing building energy efficiency, indoor thermal comfort, and achieving net zero energy self-sufficiency for a rural school building, by developing a climate-responsive architectural paradigm for rural schools, ensuring adaptability to diverse environmental conditions while striving for energy independence through passive design strategies. Employing multi-objective optimization with the NSGA-II genetic algorithm, passive design parameters such as construction type, glazing type, insulation specifications, roof vegetation, window overhang, and outdoor shading structures were evaluated across six distinct climatic zones in Morocco. Integration of EnergyPlus, jEPlus, and jEPlus+EA software facilitated the optimization process. Pareto fronts of optimal solutions were generated, prioritizing the minimization of heating and cooling energy consumption alongside discomfort hours. Results demonstrate that the optimized solutions effectively enhance building energy efficiency and indoor thermal comfort while achieving net zero energy status across all studied climatic zones. Optimal solutions enhanced building energy efficiency by 18.6 % - 35.6 %, tailored to climate and school design.

关键词： Rural school Passive design multi-objective optimization Net zero energy building Indoor comfort

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MOCBOA:multi-objective Chef-Based optimization Algorithm Using Hybrid Dominance Relations for Solving Engineering Design Problems

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Computer Modeling in Engineering & Sciences 2025年第4期143卷 967-1008页

作者： Nour Elhouda Chalabi Abdelouahab Attia Abdulaziz S.Almazyad Ali Wagdy Mohamed Frank Werner Pradeep Jangir Mohammad Shokouhifar Computer Science Department University of M’silaIchebiliaM’sila28000Algeria Computer ScienceDepartment Mohamed El Bachir El IbrahimiUniversity of Bordj BouArreridjBordj BouArreridj34000Algeria Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan Faculty of Mathematics Otto-von-Guericke UniversityMagdeburg39016Germany University Centre for Research and Development Chandigarh UniversityMohali140413India Department of Electrical and Computer Engineering Shahid Beheshti UniversityTehran14399Iran DTU AI and Data Science Hub(DAIDASH) Duy Tan UniversityDa Nang550000Vietnam

multi-objective optimization is critical for problem-solving in engineering,economics,and *** study introduces the multi-objective Chef-Based optimization Algorithm(MOCBOA),an upgraded version of the Chef-Based optimization Algorithm(CBOA)that addresses distinct *** approach is unique in systematically examining four dominance relations—Pareto,Epsilon,Cone-epsilon,and Strengthened dominance—to evaluate their influence on sustaining solution variety and driving convergence toward the Pareto *** comparison investigation,which was conducted on fifty test problems from the CEC 2021 benchmark and applied to areas such as chemical engineering,mechanical design,and power systems,reveals that the dominance approach used has a considerable impact on the key optimization measures such as the hypervolume *** paper provides a solid foundation for determining themost effective dominance approach and significant insights for both theoretical research and practical applications in multi-objective optimization.

关键词： multi-objective optimization chef-based optimization algorithm(CBOA) pareto dominance epsilon dominance cone-epsilon dominance strengthened dominance

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An improved artificial bee colony algorithm for the multi-objective cooperative disassembly sequence optimization problem considering carbon emissions and profit

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ENGINEERING optimization 2025年第3期57卷 649-670页

作者： Chen, Zhaofang Cheng, Hao Liu, Yongfeng Aljuaid, Mohammed Fujian Univ Technol Sch Management Fuzhou Peoples R China Minist Transport Transport Planning & Res Inst Beijing Peoples R China King Saud Univ Coll Business Adm Dept Hlth Adm Riyadh Saudi Arabia

The disassembly and recycling of electronic waste are essential for realizing residual value, lowering carbon emissions and fostering sustainable development. This article addresses multi-objective cooperative disassembly planning and algorithm optimization for waste mobile phones. The model aims to reduce carbon emissions during the disassembly process and to increase profits. Moreover, an improved artificial bee colony (IABC) algorithm is introduced to address the model. The model is validated using Apple smartphones as a case study. The results indicate that cooperative disassembly can lower carbon emissions by 21.04% and increase profits by 40.04% more than sequential disassembly. The efficiency of the improved algorithm was assessed using Friedman and Nemenyi tests. Significant differences were found between the IABC and the other three algorithms, with significant P-values of P = 0.001, P = 0.006 and P = 0.024. It is demonstrated that the proposed model and improved algorithm exhibit reliability and superiority.

关键词： multi-objective optimization cooperative disassembly carbon emissions improved artificial bee colony algorithm sustainable development

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Pedestrian Detection Using multi-objective optimization 20th

Pedestrian Detection Using Multi-Objective Optimization

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20th Iberoamerican Congress on Pattern Recognition (CIARP)

作者： Negri, Pablo Consejo Nacl Invest Cient & Tecn Av Rivadavia 1917 RA-1033 Buenos Aires DF Argentina INTEC UADE Buenos Aires DF Argentina

ISBN: (纸本)9783319257518;9783319257501

Pedestrian detection on urban video sequences challenges classification systems because of the presence of cluttered backgrounds which drop their performances. This article proposes a multi-objective optimization (MOO) technique reducing this limitation. It trains a pool of cascades of boosted classifiers using different positive datasets. A Pareto Front is obtained from the locally non-dominated operational points of the Receptive objective Curve (ROC) of those classifiers. Using information about the dynamic of the scene, different pairs of operational points from the Pareto Front are employed to improve the performance of the system. Results on a real sequences outperform traditional detector systems.

关键词： multi-objective optimization Pedestrian detection

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Structural optimization of dimple-plate heat exchanger via artificial neural network and multi-objective genetic algorithm

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APPLIED THERMAL ENGINEERING 2025年 263卷

作者： He, Lin Luo, Qing Zhao, Shunan Li, Yicong Liu, Wei Liu, Zhichun Huazhong Univ Sci & Technol Sch Energy & Power Engn Wuhan 430074 Peoples R China State Key Lab Air Conditioning Equipment & Syst En Zhuhai 519070 Peoples R China Gree Elect Appliances Inc Zhuhai Zhuhai 519070 Peoples R China

The development of plate heat exchangers has facilitated the extended application of air-source heat pump water heaters. This study proposes a new type of continuous dimple-plate heat exchanger (DPHE) with diamond- shaped weld points. Based on computational fluid dynamics simulation, the effects of three key structural parameters of the DPHE on its thermal-hydraulic performance is investigated. R410A is employed as the working fluid, and the Reynolds Number (Re) is varied from 1500 to 9950. The results indicate that the Nusselt number (Nu) increases as both the channel height H and weld point size W increase while the dimple size D decreases, and that the variation in the friction factor (f) is analogous. When H increases from 0.8 to 1.1 mm, the Nu increases by 37.8% to 43.5%, with f increasing by 26.9% on average. When W increases from 0.2 to 0.5 mm, f increases by 14.1 % to 29.4 %, with the Nu increasing by 6.9 % at the maximum. When D increases from 2.5 to 4.0 mm, the Nu decreases by 8.9% to 16.9% and f decreases by 37.5% to 42.7%. A multi-objective optimization was conducted using an artificial neural network and a multi-objective genetic algorithm with the Nu and f as the objectives. The optimal structural parameters are identified as H = 1.10 mm, W = 0.49 mm, and D = 2.51 mm. The performance evaluation criterion under the optimal structure is 1.198, which is 217.4 % higher than afforded by the conventional chevron-plate heat exchanger.

关键词： Dimple-plate heat exchanger Geometric parameter multi-objective optimization CFD

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Resource Allocation for Metaverse Experience optimization: A multi-objective multi-Agent Evolutionary Reinforcement Learning Approach

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第4期24卷 3473-3488页

作者： Feng, Lei Jiang, Xiaoyi Sun, Yao Niyato, Dusit Zhou, Yu Gu, Shiyi Yang, Zhixiang Yang, Yang Zhou, Fanqin Beijing Univ Posts & Telecommun BUPT State Key Lab Networking & Switching Technol Beijing Peoples R China Univ Glasgow James Watt Sch Engn Glasgow City Scotland Nanyang Technol Univ Coll Comp & Data Sci Singapore City Singapore

In the Metaverse, real-time, concurrent services such as virtual classrooms and immersive gaming require local graphic rendering to maintain low latency. However, the limited processing power and battery capacity of user devices make it challenging to balance Quality of Experience (QoE) and terminal energy consumption. In this paper, we investigate a multi-objective optimization problem (MOP) regarding power control and rendering capacity allocation by formulating it as a multi-objective optimization problem. This problem aims to minimize energy consumption while maximizing Meta-Immersion (MI), a metric that integrates objective network performance with subjective user perception. To solve this problem, we propose a multi-objective multi-Agent Evolutionary Reinforcement Learning with User-Object-Attention (M2ERL-UOA) algorithm. The algorithm employs a prediction-driven evolutionary learning mechanism for multi-agents, coupled with optimized rendering capacity decisions for virtual objects. The algorithm can yield a superior Pareto front that attains the Nash equilibrium. Simulation results demonstrate that the proposed algorithm can generate Pareto fronts, effectively adapts to dynamic user preferences, and significantly reduces decision-making time compared to several benchmarks.

关键词： Metaverse Quality of experience Delays Resource management optimization Heuristic algorithms Rendering (computer graphics) Wireless communication Energy consumption Costs Metaverse experience multi-objective optimization resource allocation Meta-Immersion energy consumption

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An Approach to Mitigating Unwanted Interactions between Search Operators in multi-objective optimization 15

An Approach to Mitigating Unwanted Interactions between Sear...

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17th Genetic and Evolutionary Computation Conference (GECCO)

作者： Byers, Chad M. Cheng, Betty H. C. Michigan State Univ BEACON Ctr Study Evolut Act Dept Comp Sci & Engn E Lansing MI 48824 USA

ISBN: (纸本)9781450334723

At run time, software systems often face a myriad of adverse environmental conditions and system failures that cannot be anticipated during the system's initial design phase. These uncertainties drive the need for dynamically adaptive systems that are capable of providing self-* properties (e.g., self-monitoring, self-adaptive, self-healing, etc.). Prescriptive techniques to manually preload these systems with a limited set of configurations often result in brittle, rigid designs that are unable to cope with environmental uncertainty. An alternative approach is to embed a search technique capable of exploring and generating optimal reconfigurations at run time. Increasingly, DAS applications are defined by multiple competing objectives (e.g., cost vs. performance) in which a set of valid solutions with a range of trade-offs are to be considered rather than a single optimal solution. While leveraging a multi-objective optimization technique, NSGA-II, to manage these competing objectives, hidden interactions were observed between search operators that prevented fair competition among solutions and restricted search from regions where valid optimal configurations existed. In this follow-on work, we demonstrate the role that niching can play in mitigating these unwanted interactions by explicitly creating favorable regions within the objective space where optimal solutions can equally compete and co-exist.

关键词： Decision making Genetic Algorithms multi-objective optimization multiple solutions / Niching Reference point-based niching NSGA-III Remote Data Mirroring

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Fast multi-objective optimization of Shaped Offset Gregorian Reflector Systems

Fast Multi-Objective Optimization of Shaped Offset Gregorian...

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IEEE International Symposium on Antennas and Propagation / USNC/URSI National North American Radio Science Meeting

作者： de Villiers, Dirk I. L. Koziel, Slawomir Univ Stellenbosch Dept Elect & Elect Engn ZA-7600 Stellenbosch South Africa Reykjavik Univ Engn Optimizat & Modeling Ctr Reykjavik Iceland

ISBN: (纸本)9781479978151

A computationally efficient method for the multi-objective optimization of shaped offset Gregorian reflector systems is presented. The method uses space mapping to construct a fast surrogate model of the responses of interest aperture efficiency and side lobe level. The Pareto front of the surrogate is found, and the model iteratively re-aligned along the front. Ideal Gaussian feeds are used, and when the reflector system is large enough the Pareto front is found with only 19 simulations of the system when two input parameters are used.

关键词： multi-objective optimization radio astronomy reflector antennas space mapping surrogate modeling

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Design optimization of Bionic Liquid Cooling Plate Based on PSO-BP Neural Network Surrogate Model and multi-objective Genetic Algorithm

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BATTERIES-BASEL 2025年第4期11卷 141-141页

作者： Liu, Jiaming Yuan, Wenlin Zhou, Yapeng Zhang, Hengyun Shanghai Univ Engn Sci Sch Mech & Automot Engn Shanghai 201620 Peoples R China China Merchants Testing Vehicle Technol Res Inst L Chongqing 400039 Peoples R China

In this study, the particle swarm optimization (PSO) and back propagation neural network (BPNN) surrogate model in combination with a multi-objective genetic algorithm are developed for the design optimization of a bionic liquid cooling plate with a spider-web channel structure. The single-factor sensitivity analysis is first conducted based on the numerical simulation approach, identifying three key factors as design variables for optimizing design objectives such as maximum temperature (Tmax), maximum temperature difference (Delta Tmax), and pressure drop (Delta P). Subsequently, the PSO algorithm is used to optimize the parameters of the BPNN structure, thereby constructing the PSO-BPNN surrogate model. Next, the non-dominated sorting genetic algorithm II (NSGA-II) is employed to obtain the Pareto optimal set, and the TOPSIS with the entropy weight method is used to determine the optimal solution, eliminating subjective preferences in decision-making. The results show that the PSO-BPNN model outperforms the traditional BPNN in prediction accuracy for all three objectives. Compared to the initial structure, the Tmax and Delta Tmax are reduced by 1.09 degrees C and 0.41 degrees C in the optimized structure, respectively, with an increase in Delta P by 21.24 Pa.

关键词： liquid cooling plate particle swarm optimization back propagation neural network multi-objective optimization entropy weight-TOPSIS method

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