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A dynamic multi-objective optimization evolutionary algorithm based on classification of environmental change intensity and collaborative prediction strategy

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JOURNAL OF SUPERCOMPUTING 2025年第1期81卷 1-52页

作者： Wang, Yu Ma, Yongjie Li, Quanxiu Zhao, Yan Northwest Normal Univ Coll Phys & Elect Engn Lanzhou 730070 Peoples R China

The dynamic multi-objective optimization evolutionary algorithm (DMOEA) has garnered widespread attention due to its superiority in solving dynamic multi-objective optimization problems (DMOPs). Existing DMOEAs do not judge the intensity of environmental changes after they have been detected, which may lead to incorrect evolutionary directions of the population. To address this issue, this study proposes a DMOEA based on the classification of environmental change intensity and collaborative prediction strategy. Firstly, the algorithm optimizes the static optimization process, thereby determining the relative position of individuals in the objective space and enhancing the accuracy of environmental change detection. Upon detecting an environmental change, the algorithm proposes a method based on mutual information to further classify the intensity of the environmental change, and guides the particle swarm to adopt different velocity update methods for evolution based on the classification results. Secondly, a collaborative prediction strategy is proposed to ensure that the predicted population is closer to the Pareto optimal solution Set (PS) in the new environment. Lastly, a dual individual screening strategy is employed to select superior individuals from both the predicted population and the population before the environmental change to form the initial population in the new environment. Comparative experiments with advanced DMOEAs on 20 different types of test functions demonstrate the superiority of the proposed algorithm in solving complex DMOPs.

关键词： dynamic multi-objective optimization Classification of environmental change intensity Collaborative prediction strategy Dual individual screening strategy Particle swarm prediction

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Integrating machine learning with dynamic multi-objective optimization for real-time decision-making

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INFORMATION SCIENCES 2025年 690卷

作者： Sarkar, Puja Khanapuri, Vivekanand B. Tiwari, Manoj Kumar Indian Inst Management Mumbai Mumbai India Indian Inst Technol Kharagpur Dept Ind & Syst Engn Kharagpur 721302 West Bengal India

Real-time decision-making in dynamic multi-objective optimization problems (DMOPs) is challenging due to constantly changing objectives and constraints. This paper integrates machine learning with Non-dominated Sorting Genetic Algorithm II (NSGA-II) to solve DMOPs and make real-time decisions. Learning-based methods have gained popularity for predicting solutions in new environments and capturing changing patterns in optimal solutions. However, existing approaches often struggle with training difficulty and reduced prediction accuracy due to irrelevant or redundant variables. Therefore, we introduce a new interdependent prediction (IDP) technique to identify correlations between variables and prediction targets and select significant variables for a predictive model. In this way, a better initial population is predicted. The IDP strategy is integrated within the dynamic NSGA-II, introducing a new algorithm called IDP-DNSGA-II. This integration facilitates rapid convergence, finding optimal or near-optimal solutions. The proposed method is evaluated against standard benchmarks, demonstrating superior performance in convergence speed and solution diversity with the changes in the problem environment. The IDP-DNSGA-II is validated through real-world optimization challenges in sustainable automobile production distribution in order-to-delivery systems to enhance environmental sustainability and operational efficiency. This study identifies the minimum frequency of change required in real- world problems to adequately track the optimal decision in real-time.

关键词： Machine learning Correlation dynamic multi-objective optimization Evolutionary computation

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Solving dynamic multi-objective optimization problem of immersed tunnel elements via multi-source evolutionary information clustering method

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 140卷

作者： Fan, Qinqin Huang, Wentao Yu, Moduo Tang, Qirong Jiang, Qingchao Shanghai Maritime Univ Logist Res Ctr Shanghai 201306 Peoples R China Shanghai Jiao Tong Univ Key Lab Control Power Transmiss & Convers Shanghai 200240 Peoples R China Tongji Univ Lab Robot & Multibody Syst Shanghai 200092 Peoples R China East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China

dynamic multi-objective optimization problems (DMOPs) are time- and space-varying, thus maintaining/ improving the uncertainty degree of evolutionary information (i.e., information entropy) in the population and providing useful knowledge are two important tasks to make dynamic multi-objective evolutionary algorithms (DMOEAs) adapt to changing environments. To achieve the above objectives, a multi-source population clustering (MPC) method is proposed to assist DMOEAs in improving their tracking performance during the full-cycle optimization in the current study. In the MPC, three different information sources are used to provide diverse spatiotemporal evolutionary information, aiding DMOEAs in adapting to various changing environments. Subsequently, an enhanced spectral clustering approach is employed to group all evolutionary individuals from different information sources into many clusters/subspaces. Finally, the selected DMOEA is employed to search all subspaces in parallel via the high-performing computing method. The MPC is incorporated into a regularity model-based multi-objective estimation of distribution algorithm (called as MPC-RM-MEDA) and is compared with six famous DMOEAs on 14 10- and 30-dimensional DMOPs, which are proposed in IEEE Congress on Evolutionary computation 2018. Experimental results demonstrate that the overall tracking performance of the proposed MPC-RM-MEDA is significantly superior to that of other selected competitors in various dynamic environments. Additionally, the MPC-RM-MEDA is utilized to address a real-world DMOP involving an immersed tunnel element. The obtained results and comparison with the knee point-based transfer learning method verify that the MPC is an efficient and dependable approach for enhancing the tracking performance of other DMOEAs in solving actual DMOPs.

关键词： dynamic multi-objective optimization Evolutionary computation High-performing computing multi-source information Immersed tunnel element

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Division-selection transfer learning for prediction based dynamic multi-objective optimization

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COMPLEX & INTELLIGENT SYSTEMS 2025年第1期11卷 1-21页

作者： Li, Hongye Liang, Fan Liu, Yulu Zheng, Quanheng Guo, Kunru Xian Univ Posts & Telecommun Sch Comp Sci & Technol Xian 710121 Shaanxi Peoples R China Xian Univ Posts & Telecommun Shaanxi Key Lab Network Data Anal & Intelligent Pr Xian 710121 Shaanxi Peoples R China Xian Univ Posts & Telecommun Xian Key Lab Big Data & Intelligent Comp Xian 710121 Shaanxi Peoples R China

dynamic multi-objective optimization problems (DMOPs) are challenging as they require capturing the Pareto optimal front (POF) and Pareto optimal set (POS) during the optimization process. In recent years, transfer learning (TL) has emerged the empirical knowledge and is an effective approach to solve DMOPs. However, negative transfer can occur when the transfer method is not suitable for the transfer task. It may deviate the search path and seriously reduce the efficiency, so how to reduce the occurrence of negative transfer to save the running time of dynamic multi-objective evolutionary algorithm (DMOEA) is an important issue to be addressed. A division-selection transfer learning evolutionary algorithm for dynamic multi-objective optimization (DST-DMOEA) is designed towards this aim. Specifically, individuals with high Spearman correlation are relatively stable in different environments, selecting them to train the Support Vector Regression (SVR) model ensures a more accurate capture of solution features, predicting the objective values of historical solutions based on the model, and thus divide historical solutions into elite and non-elite solutions. Subsequently, for the elite solutions, individual TL that incorporates local information for optimization and transfer is used, while the non-elite solutions are handled with manifold TL method to obtain the overall data distribution and understand the internal structure. Then, merge the predicted individuals generated by two parts of TL will constitute as the initial population in the optimization process. Compared with other algorithms, the initial solution of DST-DMOEA is closer to the real POF, effectively reducing negative transfer. In addition, in 51 test instances, DST-DMOEA has shown superior performance in over 30 instances.

关键词： dynamic multi-objective optimization Division-selection External sampling Transfer learning Negative transfer Evolutionary algorithms

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A Discriminative Prediction Strategy Based on multi-View Knowledge Transfer for dynamic multi-objective optimization

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PROCESSES 2025年第3期13卷 744-744页

作者： Xu, Hua Zhang, Chenjie Huang, Lingxiang Tao, Juntai Zheng, Jianlu Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Peoples R China

dynamic multi-objective optimization problems (DMOPs) are widely encountered in engineering optimization processes, characterized by conflicting objectives that change over time. Evolutionary transfer optimization (ETO) has recently emerged as a promising optimization paradigm for addressing DMOPs. ETO-based dynamic multi-objective evolutionary algorithms (DMOEAs) usually adopt discriminative prediction strategies to select high-quality initial solutions in new environments by transferring knowledge from the source domain, thereby accelerating the evolutionary process. However, DMOPs often change in both decision and objective spaces. Existing methods rely on a single view from the source domain for knowledge transfer, which can overlook crucial knowledge from other views, potentially affecting the selection of high-quality initial solutions and limiting optimization performance. To address this, we propose a discriminative prediction strategy based on multi-view knowledge transfer for DMOEA, called MKT-DMOEA. Specifically, we construct discriminative predictors from the view of the decision space and objective space in the source domain. Each discriminative predictor effectively reduces the differences between environments under the current view through a transfer strategy based on geometric feature transformation. Meanwhile, these predictors make full use of the transferred knowledge to achieve accurate discriminative predictions under the current view. Finally, we integrate the discriminative prediction results from multiple views to select high-quality initial solutions. Experimental results demonstrate that our proposed algorithm outperforms four other state-of-the-art DMOEAs in terms of both diversity and convergence on the well-known CEC 2018 dynamic multi-objective optimization benchmark suite DF.

关键词： dynamic multi-objective optimization evolutionary transfer optimization evolutionary algorithm transfer learning prediction

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A dynamic interval multi-objective optimization algorithm based on environmental change detection

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INFORMATION SCIENCES 2025年 694卷

作者： Cai, Xingjuan Li, Bohui Wu, Linjie Chang, Teng Zhang, Wensheng Chen, Jinjun Taiyuan Univ Sci & Technol Shanxi Key Lab Big Data Anal & Parallel Comp Taiyuan 030024 Shanxi Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Chinese Acad Sci Inst Automat State Key Lab Intelligent Control & Management Com Beijing Peoples R China Swinburne Univ Technol Dept Comp Sci & Software Engn Melbourne Vic 3000 Australia

dynamic interval multi-objective optimization problems are a class of optimization problems whose interval parameters change with the environment. However, the existing algorithms fail to fully consider the characteristics of interval parameters and can not accurately assess the severity of environmental changes, resulting in a decline in the effectiveness of the detection mechanism. Therefore, effectively dealing with the inherent uncertainty of interval values becomes an important challenge. To address these problems, this paper proposes a dynamic interval multi- objective optimization algorithm based on environment change detection (IO-ECD). Firstly, a change severity detection operator is designed by using the average overlap degree of individual objective interval to classify different severity of environmental changes. Secondly, this paper uses the local search and the interval prediction mechanism based on feed-forward centroid and special points set to cope with various levels of environmental changes. Finally, inspired by hypervolume contribution and objective value inaccuracy, an interval crowding distance operator is constructed to guide population evolution. The algorithm is compared with six cutting-edge algorithms in eight test cases and a combinatorial optimization scenario. The experimental results show that the algorithm performs exceptionally well in most aspects and has strong competitiveness.

关键词： dynamic multi-objective optimization Interval optimization Change severity detection Change response

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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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dynamic multi-objective optimization arising in iron precipitation of zinc hydrometallurgy

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HYDROMETALLURGY 2017年 173卷 134-148页

作者： Han, Jie Yang, Chunhua Zhou, Xiaojun Gui, Weihua Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China

The additions of oxygen and zinc oxide for the goethite process determine the cost and efficiency of the iron precipitation process. As the two production targets (cost and efficiency) are conflicting and the chemical reaction is a continuous process that changes over time, the amounts of additive need to be dynamically optimized to satisfy the requirement of industrial application. In this paper, a discretization method based on control variables and control intervals is proposed to transform the dynamic optimization problem to a nonlinear mathematical programming problem. Then, a multi-objective optimization approach based on the state transition algorithm and constrained nondominated sorting is proposed to find the Pareto optimal solutions. Finally, an evaluation mechanism is proposed to obtain the best solution for industrial applications. The results from a series of simulation experiments show the effectiveness of the proposed approach, e.g. the daily average additions of oxygen and zinc oxide are decreased by 778.0854 m(3) and 4.9013 t, respectively.

关键词： dynamic multi-objective optimization Discretization method State transition algorithm Iron precipitation

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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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