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dynamic multi-objective optimization method for production index of cement clinker firing process based on collaborative prediction strategy

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 151卷

作者： Liu, Gang Xie, Shangjian Hao, Xiaochen Yang, Mengke Yang, Xunian Xu, Xingxing Guo, Huan Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Peoples R China

The cement clinker firing system is complex, with interdependent indicators, making it challenging to optimize decision-making. Furthermore, the traditional static single-objective or multi-objective optimization methods are inadequate in adapting to the dynamic changes of complex working conditions. To address these issues, this paper proposes a dynamic multi-objective optimization method for the production index of the cement clinker firing process. The way first establishes a data-driven dynamic multi-objective optimization model, the content of free calcium oxide (f-CaO) in clinker and the coal consumption of the sintering system as optimization objectives, numerous operational indicators as decision variables, considers dynamic factors and constraints during manufacturing. Then a dynamic multi-objective evolutionary algorithm based on a collaborative prediction strategy (CPS-DMOEA) is designed to solve the model. Experimental results for some benchmark test problems show a significant improvement in dynamic optimization performance with CPS-DMOEA. Furthermore, experiments using actual cement production data show that the proposed method outperforms traditional static multiobjective optimization methods.

关键词： Cement clinker firing process dynamic multi-objective optimization Data-driven model Collaborative prediction strategy

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dynamic multi-objective optimization based on classification response of decision variables

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INFORMATION SCIENCES 2025年 691卷

作者： Li, Jianxia Liu, Ruochen Wang, Ruinan Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Shaanxi Peoples R China

In recent years, many dynamic multi-objective optimization algorithms (DMOAs) have been proposed to address dynamic multi-objective optimization problems (DMOPs). Most existing DMOAs treat all decision variables uniformly and respond to them in an identical manner. This paper proposes a dynamic multi-objective optimization algorithm based on the classification response of decision variables (CRDV-DMO). Firstly, CRDV-DMO categorizes the decision variables into convergence variables and diversity variables. Different decision variables adopt distinct response strategies. The response strategy of diversity variable (RSDV) uses Latin hypercube sampling to generate the diversity variables of the new environment. For each dimensional convergence variable, the response strategy of convergence variable (RSCV) first evaluates whether the basic center prediction strategy (CPS) yields positive feedback or negative feedback, further determining the predictability of that dimensional convergence variable. RSCV then decides to either use the basic CPS to generate the convergence variable for that dimension or to retain that dimensional convergence variable from the current environment, based on the predictability of that dimensional convergence variable. The proposed algorithm is extensively studied through comparison with several advanced DMOAs, demonstrating its effectiveness in dealing with the benchmark DMOPs and the parameter-tuning problem of the PID controller on a dynamic system.

关键词： dynamic multi-objective optimization Classification response Convergence variables Diversity variables PID controller

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dynamic multi-objective optimization in Brazier-Type Gasification and Carbonization Furnace

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MATERIALS 2023年第3期16卷 1164-1164页

作者： Zhang, Xi Zhang, Guiyun Zhang, Dong Zhang, Liping East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Shanghai 200237 Peoples R China Shanxi Agr Univ Inst Cotton Res Yuncheng 044000 Peoples R China Murdoch Univ Coll Sci Hlth Engn & Educ Discipline Engn & Energy Perth WA 6150 Australia

With the special porous structure and super-long carbon sequestration characteristic, the biochar has shown to have potential in improving soil fertility, reducing carbon emissions and increasing soil carbon sequestration. However, the biochar technology has not been applied on a large scale, due to the complex structure, long transportation distance of raw materials, and high cost. To overcome these issues, the brazier-type gasification and carbonization furnace is designed to carry out dry distillation, anaerobic carbonization and have a high carbonization rate under high-temperature conditions. To improve the operation and maintenance efficiency, we formulate the operation of the brazier-type gasification and carbonization furnace as a dynamic multi-objective optimization problem (DMOP). Firstly, we analyze the dynamic factors in the work process of the brazier-type gasification and carbonization furnace, such as the equipment capacity, the operating conditions, and the biomass treated by the furnace. Afterward, we select the biochar yield and carbon monoxide emission as the dynamic objectives and model the DMOP. Finally, we apply three dynamic multiobjective evolutionary algorithms to solve the optimization problem so as to verify the effectiveness of the dynamic optimization approach in the gasification and carbonization furnace.

关键词： biochar gasification and carbonization furnace dynamic multi-objective optimization Gaussian process evolutionary algorithm

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dynamic multi-objective optimization Framework With Interactive Evolution for Sequential Recommendation

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2023年第4期7卷 1228-1241页

作者： Zhou, Wei Liu, Yong Li, Min Wang, Yu Shen, Zhiqi Feng, Liang Zhu, Zexuan Shenzhen Univ Coll Comp Sci & Software Engn Natl Engn Lab Big Data Syst Comp Technol Shenzhen 518060 Peoples R China Nanyang Technol Univ Joint NTU UBC Res Ctr Excellence Act Living Elderl Singapore 639798 Singapore Jing Dong Retail Dept User Growth & Operat Beijing 100176 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China

In contrast to traditional recommender systems which usually pay attention to users' general and long-term preferences, sequential recommendation (SR) can model users' dynamic intents based on their behaviour sequences and suggest the next item(s) to them. However, most of existing sequential models learn the ranking score of an item only based on its relevance property, and the personalized user demands in terms of different learning objectives, such as diversity, tail novelty or recency, which have been played essential roles in multi-objective recommendation (MOR), are often neglected in SR. In this paper, we first discuss the importance of considering multiple different objectives within a learning model for recommender system. Next, to capture users' objective-level preferences by utilizing interactive information in the sequential context, we propose a novel dynamic multi-objective Recommendation (DMORec) framework with interactive evolution for SR. In particular, DMORec formulates a dynamic multi-objective optimization task to simultaneously optimize more than two varying objectives in an interactive recommendation process. Moreover, to resolve this optimization task in SR, we develop an evolutionary algorithm with supervised learning approach to obtain the Pareto-optimal solutions of the formulated problem. Comprehensive experiments on four real-world datasets demonstrate the effectiveness of the proposed DMORec for dynamic multi-objective recommendation in sequential recommender systems.

关键词： optimization Behavioral sciences Task analysis Tail Training Recommender systems Tuning dynamic multi-objective optimization multi-objective recommendation sequential recommendation

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dynamic multi-objective optimization Based on an Improved Lion Group Algorithm 7

Dynamic Multi-objective Optimization Based on an Improved Li...

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7th International Conference on Artificial Intelligence and Big Data (ICAIBD)

作者： Wang, Yumeng Yu, Xinchang Wang, Jingjing Xu, Huaqiang Shandong Normal Univ Jinan Peoples R China

ISBN: (纸本)9798350385113;9798350385106

In this paper, the conventional lion group algorithm is improved and used to solve dynamic multi-objective optimization Problems (DMOPs). An environment change detection strategy is adopted to determine whether the environment changes by calculating the average difference in fitness values of the hunting lions. Furthermore, the algorithm constructs a time series by recording the positions of the lion king at times t 1 and t to predict the lion king's position at time t + 1 to adapt to the changing environment. The performance of the proposed algorithm is studied by comparing it with four state-ofthe-art dynamic optimization algorithms. The experimental results show that the proposed algorithm outperforms other peer algorithms in most benchmark functions.

关键词： Lion group algorithm dynamic multi-objective optimization time series

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dynamic multi-objective optimization of Autocatalytic Esterification in a Semi-Batch Reactor

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CHEMICAL ENGINEERING & TECHNOLOGY 2022年第10期45卷 1795-1802页

作者： Rohman, Fakhrony Sholahudin Zahan, Khairul Azly Aziz, Norashid Univ Sains Malaysia Sch Chem Engn Engn Campus Perai 14300 Penang Malaysia Univ Brawijaya Dept Chem Engn Jalan Mayjen Haryono 167 Malang 65145 Indonesia Kyoto Univ Grad Sch Informat Dept Syst Sci Kyoto 6068501 Japan Univ Tun Hussein Onn Malaysia Fac Engn Technol Batu Pahat 86400 Johor Malaysia

The catalytic esterification of -butyl propionate in a semi-batch reactor is examined using dynamic, nonlinear programming-based optimization. To solve the conflicting bi-objective of minimizing the final time t(f) and maximizing the conversion X-c, the epsilon-constraint, weighted sum, and NSGA-II (non-dominated sorting genetic algorithm II) techniques were implemented. By computing performance measurements like hypervolume, pure diversity, and spacing, these multi-objective optimization techniques were compared to the characteristics of the Pareto solution. Each Pareto solution point comprises a unique combination of optimal feed flow rate and temperature trajectories. These solutions provide various options for evaluating trade-offs while establishing the best operating strategy.

关键词： Autocatalytic esterification dynamic multi-objective optimization Optimal control

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dynamic multi-objective optimization applied to a solar-geothermal multi-generation system for hydrogen production, desalination, and energy storage

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2022年第74期47卷 31730-31741页

作者： Sohani, Ali Delfani, Fatemeh Hosseini, Mohammadmehdi Sayyaadi, Hoseyn Karimi, Nader Li, Larry K. B. Doranehgard, Mohammad Hossein KN Toosi Univ Technol Lab Optimizat Thermal Syst Installat Fac Mech Engn Energy Div POB 19395-199915-19 Pardis StMollasadra AveVan Tehran *** Iran Islamic Azad Univ Dept Mech Engn Semnan Branch Semnan Iran Queen Mary Univ London Sch Engn & Mat Sci London E1 4NS England Univ Glasgow James Watt Sch Engn Glasgow G12 8QQ Lanark Scotland Hong Kong Univ Sci & Technol Dept Mech & Aerosp Engn Clear Water Bay Hong Kong Peoples R China Univ Alberta Sch Min & Petr Engn Dept Civil & Environm Engn Edmonton AB T6G 1H9 Canada

The design of optimal energy systems is vital to achieving global environmental and economic targets. In the design of solar-geothermal multi-generation systems, most pre-vious investigations have relied on the static multi-objective optimization approach (SMOA), which may leave considerable room for improvement under certain conditions. In this numerical study, the optimal condition at which to operate a solar-geothermal multi -generation system -which can simultaneously produce hydrogen, fresh water, electricity, and heat, along with storing energy -is determined via a dynamic multi-objective opti-mization approach (DMOA). optimization is performed using a combination of NSGA-II and TOPSIS, and the results are benchmarked against those of SMOA. The decision variables include the solar area, geothermal water extraction mass flow, and hydrogen storage pressure. The objective functions include the production of electricity, heat, hydrogen, and fresh water, along with the exergy and energy efficiencies and the payback period. It is found that when compared with SMOA, DMOA can significantly improve all the objective functions. The annual production of electricity, heat, hydrogen, and fresh water increases by 14.4, 16.1, 13.5, and 14.3%, respectively, while the average annual exergy and energy efficiencies increase by 5.2 and 3.0%, respectively. The use of DMOA also reduces the payback period from 5.56 to 4.43 years, with a 4.4% reduction in hydrogen storage pressure. This shows that compared with a static approach such as SMOA, DMOA can improve the exergy and energy efficiencies, economic viability, and safety of a solar-geothermal multi -generation system. (c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： multi-effect desalination technology dynamic multi-objective optimization Solar geothermal energies combination Techno-economic analysis Hydrogen production

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A dynamic multi-objective optimization approach for integrated timetabling and vehicle scheduling

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 95卷

作者： Shen, Yindong Xie, Wenliang Huazhong Univ Sci & Technol Inst Artificial Intelligence Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol MOE Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

dynamic integrated timetabling and vehicle scheduling (D-ITVS) is essential for mitigating the negative impacts of service disruptions arising from the stochastic nature of traffic flow and passenger demand fluctuations in public transit. Existing optimization approaches for the D-ITVS problem typically conduct optimization independently at each rescheduling stage. In contrast, this paper proposes a novel dynamic multi-objective optimization approach that considers evolving patterns across different rescheduling stages from a holistic perspective. This approach formulates the optimization problem for all rescheduling stages during a day's operation as a dynamic multi-objective optimization problem, modeled using a dynamic time-space network flow framework. To leverage these evolving patterns for achieving optimal solutions throughout, a dynamic multi-objective optimization approach for the D-ITVS problem (DMO-TVS) is introduced. The DMO-TVS approach learns the evolving patterns, and incorporates a dynamic solution representation alongside three key mechanisms: (1) change detection mechanism, (2) change response mechanism, and (3) multi-objective optimization mechanism. These mechanisms work in tandem to dynamically adjust the initial solution set at each rescheduling stage based on predicted optimal solutions derived from learned evolving patterns, balance conflicting objectives in the DITVS problem, and select optimal solutions with diversity throughout the optimization process. Experimental results demonstrate that the dynamic multi-objective optimization approach is capable of generating timetables and vehicle schedules with reduced costs, enhanced robustness, and improved convergence and diversity across all rescheduling stages. By balancing operational costs and passenger service quality, these improvements benefit transit operators, and during daily operations, passengers enjoy reduced travel costs and enhanced service reliability.

关键词： Public transport Timetabling Vehicle scheduling dynamic multi-objective optimization

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multi-high-Q Terahertz biosensors based on a dynamic multi-objective optimization strategy

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OPTICS AND LASER TECHNOLOGY 2025年 183卷

作者： Li, Chun Chen, Haonan Zhu, Yifan Wang, Tengyu Teng, Yan Liang, Lanju Zhang, Yonggang Yao, Haiyun Huang, Zhengwei Jiang, Ling Nanjing Forestry Univ Coll Informat Sci & Technol Nanjing 210037 Peoples R China Zaozhuang Univ Sch Optoelect Engn Zaozhuang 277160 Peoples R China Anhui Univ Sci & Technol Sch Elect & Informat Engn Huainan 232001 Anhui Peoples R China

Terahertz metamaterial sensors (TMS) playa key role in the highly sensitive detection of trace substances, especially in label-free, real-time, and in-situ measurements for disease, microbial, and pesticide residue diagnostics. To obtain excellent performance, the sensitivity of TMS is closely related to the resonance characteristics of the metamaterial, which depends on the optimal structural units. However, a desirable sensor often relies on experience and theory, and the design is gradually optimized through extensive manual calculations and simulations. Under the premise of ensuring the performance of terahertz devices, the use of algorithm to quickly generate customized device designs that meet specific needs has attracted widespread attention. Herein, we propose a strategy for designing multi-resonance TMS with high Q-factors and resonance depth based on dynamic multi-objective optimization (DMO). By introducing DMO, we designed high-Q TMS with 2-5 resonances simultaneously, saving about 122 min of design time compared with the traditional multi- objective strategy. To better demonstrate the performance of the sensor, we have characterized the 3-resonance TMS. The Q-factors of the optimized TMS can reach up to 301, 465, and 201, respectively. During the refractive index sensing process, the sensitivities of the three resonances (369, 376, and 387 GHz/RIU) have been achieved in the range of 0.9-1.7 THz. With the covered 2,4-D and thiabendazole analytes, the transmission spectra of the TMS demonstrate different "fingerprint peak"related characteristics. The proposed method can be used for dynamic multi-objective optimization challenges in various metamaterial device scenarios and provides a promising solution for designing multiple high-Q TMS.

关键词： THz metamaterial Biosensors dynamic multi-objective optimization

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A dynamic multi-objective optimization algorithm based on probability-driven prediction and correlation-guided individual transfer

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JOURNAL OF SUPERCOMPUTING 2025年第1期81卷 1-47页

作者： Ge, Fangzhen Zhao, Xuan Chen, Debao Shen, Longfeng Liu, Huaiyu Huaibei Normal Univ Sch Comp Sci & Technol Huaibei 235000 Peoples R China Huaibei Normal Univ Anhui Engn Res Ctr Intelligent Comp & Applicat Cog Huaibei 235000 Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China Huaibei Normal Univ Sch Phys & Elect Informat Huaibei 235000 Peoples R China

The primary challenge in addressing dynamic multi-objective optimization problems (DMOPs) is the rapid tracking of optimal solutions. Although methods based on transfer learning have shown remarkable performance in tackling DMOPs, most existing methods overlook the potential relationships between individuals within the population and those from historical environments. Consequently, they fail to adequately exploit historical information. To this end, this study proposes a dynamic multi-objective optimization algorithm based on probability-driven prediction and correlation-guided individual transfer (PDP&CGIT), which consists of two strategies: probability-driven prediction (PDP) and correlation-guided individual transfer (CGIT). Specifically, the PDP strategy analyzes the distribution of population characteristics and constructs a discriminative predictor based on a probability-annotation matrix to classify high-quality solutions from numerous randomly generated solutions within the decision space. Moreover, from the perspective of individual evolution, the CGIT strategy analyzes the correlation between current elite individuals and those from the previous moment. It learns the dynamic change pattern of the individuals and transfers this pattern to new environments. This is to maintain the diversity and distribution of the population. By integrating the advantages of these two strategies, PDP&CGIT can efficiently respond to environmental changes. Extensive experiments were performed to compare the proposed PDP&CGIT with five state-of-the-art algorithms across the FDA, F, and DF test suites. The results demonstrated the superiority of PDP&CGIT.

关键词： dynamic multi-objective optimization Transfer learning Evolutionary algorithms Prediction

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