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Finite-Element Analysis and multiobjective optimization of Solder Joint Temperature Difference and Cooling Stress During PCBA Reflow Process

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IEEE TRANSACTIONS ON COMPONENTS PACKAGING AND MANUFACTURING TECHNOLOGY 2025年第3期15卷 479-487页

作者： Lan, Jingyi Huang, Chunyue Liang, Ying Gao, Chao Wang, Gui Cao, Zhiqin Guilin Univ Elect Technol Sch Elect & Mech Engn Guilin 541004 Peoples R China Chengdu Aeronaut Polytech Sch Elect & Informat Engn Chengdu 610021 Peoples R China Sichuan Prov Engn Technol Res Ctr Vanadium & Titan Panzhihua 617000 Peoples R China

A finite-element analysis model of a printed circuit board assembly (PCBA) was established. The model was subjected to a reflow soldering temperature profile to analyze the temperature distribution at the solder joint solidification moment and the cooling stress distribution at the end of the reflow soldering process. Validation experiments confirmed the accuracy of the simulation results. The response surface methodology (RSM) combined with the NSGA-II algorithm was employed to optimize the reflow soldering process parameters with the dual objectives of minimizing solder joint temperature difference and cooling stress. The results reveal uneven temperature distribution at the solder joint solidification onset and concentrated cooling stress due to the mismatch in thermal expansion coefficients. The optimized reflow soldering process parameters were determined as: soak time of 80 s, reflow time of 35 s, reflow temperature of 230 degrees C, and cooling rate of 2 degrees C/s. Simulation validation demonstrated that with the optimal reflow soldering process parameters, the solder joint temperature difference and cooling stress were reduced by 1.058 degrees C and 1.245 MPa, respectively. The results of this study on the optimization of the reflow soldering process parameters of the PCBA have a certain degree of significance in guiding.

关键词： Stress Reflow soldering Cooling Finite element analysis Semiconductor device modeling Temperature distribution Packaging Lead Integrated circuit modeling Thermal conductivity multiobjective optimization printed circuit board assembly (PCBA) solder joints reflow soldering temperature difference and cooling stress

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HK-MOEA/D: A historical knowledge-guided resource allocation for decomposition multiobjective optimization

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 139卷

作者： Li, Wei Zeng, Xiaolong Huang, Ying Cheung, Yiu-ming Jiangxi Univ Sci & Technol Sch Informat Engn Ganzhou 341000 Peoples R China Gannan Normal Univ Sch Math & Comp Sci Ganzhou 341000 Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China

Decomposition-based multiobjective evolutionary algorithms is one of the prevailing algorithmic frameworks for multiobjective optimization. This framework distributes the same amount of evolutionary computing resources to each subproblems, but it ignores the variable contributions of different subproblems to population during the evolution. Resource allocation strategies (RAs) have been proposed to dynamically allocate appropriate evolutionary computational resources to different subproblems, with the aim of addressing this limitation. However, the majority of RA strategies result in inefficiencies and mistakes when performing subproblem assessment, thus generating unsuitable algorithmic results. To address this problem, this paper proposes a decomposition-based multiobjective evolutionary algorithm (HK-MOEA/D). The HK-MOEA/D algorithm uses a historical knowledge-guided RA strategy to evaluate the subproblem's evolvability, allocate evolutionary computational resources based on the evaluation value, and adaptively select genetic operators based on the evaluation value to either help the subproblem converge or move away from a local optimum. Additionally, the density-first individual selection mechanism of the external archive is utilized to improve the diversity of the algorithm. An external archive update mechanism based on theta-dominance is also used to store solutions that are truly worth keeping to guide the evaluation of subproblem evolvability. The efficacy of the proposed algorithm is evaluated by comparing it with seven state-of-the-art algorithms on three types of benchmark functions and three types of real-world application problems. The experimental results show that HK-MOEA/D accurately evaluates the evolvability of the subproblems and displays reliable performance in a variety of complex Pareto front optimization problems.

关键词： Decomposition Subproblem evolvability Evolutionary computational resource Allocation multiobjective optimization

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Data Stream Driven Dynamic multiobjective optimization Using Surrogate Transfer

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年

作者： Liu, Zhening Wang, Handing Gong, Maoguo Jin, Yaochu Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Xidian Univ Sch Elect Engn Key Lab Collaborat Intelligence Syst Minist Educ Xian 710071 Peoples R China Inner Mongolia Normal Univ Acad Artificial Intelligence Coll Math Sci Hohhot 010011 Peoples R China Westlake Univ Sch Engn Trustworthy & Gen Artificial Intelligence Lab Hangzhou 310030 Peoples R China

Most existing dynamic multiobjective evolutionary algorithms assume that the real objective function evaluations are easily acquired during the optimization process. However, in some situations, the optimization algorithm cannot invoke the objective functions and is exclusively driven by the solutions within the continuously incoming data stream. Although these solutions come with their own objective values, the objective function associated with the solutions in the data stream experiences continuous changes across time. These problems are called data stream driven dynamic multiobjective optimization problems (DDMOPs). The difficulties of the DDMOPs are tracking the optimal solutions based on the dynamic data streams and extracting the historical information to improve the optimization performance. In this work, a multiobjective evolutionary algorithm framework (ST-MOEA) is proposed to address the above challenges. ST-MOEA uses the Kriging and RBFN models to separately substitute the objective functions and guide the optimization process. Two effective mechanisms named the surrogate transfer and reliable solution selection strategies are also proposed to further improve the algorithm performance. The surrogate transfer strategy transfers the surrogate hyperparameters used in the historical data streams to the current surrogate models to enhance the surrogate accuracies, and the reliable solution selection strategy identifies dependable solutions from the populations generated by two surrogates in order to ultimately construct the optimal solution set. During the experiment process, the proposed framework is embedded into three different multiobjective evolutionary algorithms and compared with two surrogate-assisted evolutionary algorithms on a wide range of test problems with varying decision dimensions and different levels of severity in objective function changes. The experiment results validate the effectiveness of the two proposed strategies and demonstrate the

关键词： Data stream driven dynamic multiobjective optimization framework multiobjective optimization multiobjective optimization surrogate transfer surrogate transfer surrogate transfer

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Control of existing tunnel deformation caused by shield adjacent undercrossing construction using interpretable machine learning and multiobjective optimization

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AUTOMATION IN CONSTRUCTION 2025年 170卷

作者： Chen, Hongyu Liu, Jun Shen, Geoffrey Qiping Feng, Zongbao Hong Kong Polytech Univ Dept Bldg & Real Estate Hong Kong 999077 Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Hubei Peoples R China

A hybrid intelligent framework is proposed in this paper to reduce the existing tunnel deformation caused by shield adjacent undercrossing construction (SAUC). A Bayesian optimization natural gradient boosting (BONGBoost) model for existing tunnel deformation prediction is developed, and the Shapley additive explanations (SHAP) approach is used to analyze the interpretability of the prediction model. The multiobjective evolutionary algorithm based on decomposition (MOEA/D) is used to optimize the construction parameters. The applicability and validity of the proposed method are tested in a case study from the Wuhan Metro. The results indicate that (1) the established BO-NGBoost existing tunnel deformation prediction model shows high accuracy. (2) Through SHAP analysis, the importance of each input parameter to the existing tunnel deformation is identified, and the key shield optimization parameters are defined. (3) By using the developed BO-NGBoost-MOEA/D algorithm to optimize the key parameters, the existing tunnel deformation is effectively controlled.

关键词： Shield adjacent undercrossing construction Existing tunnel deformation SHAP BO-NGBoost-MOEA/D multiobjective optimization

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Congressional Apportionment: A multiobjective optimization Approach

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MANAGEMENT SCIENCE 2025年第2期71卷 iv-vi, 955页

作者： Shechter, Steven M. Univ British Columbia Sauder Sch Business Vancouver BC V6T 1Z2 Canada

Two events, with major implications for U.S. voters, occur after each decennial census. First, congressional "apportionment" takes place, followed by congressional "districting." Apportionment determines how to allocate the 435 seats in the House of Representatives across the 50 states, whereas districting determines the geographic boundaries assigned to representatives within each state. Although districting and the practice of gerrymandering often receive great attention in the media and courts, the best way to apportion representatives across states has been debated for nearly 250 years. Historical methods (including the current method) each satisfy some desirable optimality criteria that the others are not guaranteed to satisfy. Moreover, none are guaranteed to optimize certain reasonable fairness measures (e.g., minimum range, minimum bias). To our knowledge, we are the first to formulate and analyze a multiobjective optimization approach to apportionment, allowing policymakers to identify Pareto-optimal allocations and quantify their trade-offs between several competing criteria. Some of these models can be formulated and solved as mixed-integer linear programs, whereas others require the solution of mixed-integer, nonconvex, quadratically constrained quadratic programs. We take advantage of recent software advances that allow one to solve these problems with optimality guarantees. Policy implications of our work include Pareto curves from historical censuses and simulations, which suggest opportunities for improvement in some objectives at little sacrifice to others.

关键词： congressional apportionment multiobjective optimization integer programming quadratically constrained quadratic programming

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Evolutionary multiobjective optimization for Large-Scale Portfolio Selection With Both Random and Uncertain Returns

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2025年第1期29卷 76-90页

作者： Liu, Weilong Zhang, Yong Liu, Kailong Quinn, Barry Yang, Xingyu Peng, Qiao Guangdong Univ Technol Sch Management Guangzhou 510520 Guangdong Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China Queens Univ Queens Business Sch Belfast BT9 5EE North Ireland Queens Univ Belfast Grp Informat Technol Analyt & Operat Belfast BT9 5EE North Ireland

With the advent of Big Data, managing large-scale portfolios of thousands of securities is one of the most challenging tasks in the asset management industry. This study uses an evolutionary multiobjective technique to solve large-scale portfolio optimization problems with both long-term listed and newly listed securities. The future returns of long-term listed securities are defined as random variables whose probability distributions are estimated based on sufficient historical data, while the returns of newly listed securities are defined as uncertain variables whose uncertainty distributions are estimated based on experts' knowledge. Our approach defines security returns as theoretically uncertain random variables and proposes a three-moment optimization model with practical trading constraints. In this study, a framework for applying arbitrary multiobjective evolutionary algorithms to portfolio optimization is established, and a novel evolutionary algorithm based on large-scale optimization techniques is developed to solve the proposed model. The experimental results show that the proposed algorithm outperforms state-of-the-art evolutionary algorithms in large-scale portfolio optimization.

关键词： optimization Portfolios Security Random variables Uncertainty Evolutionary computation Mathematical models Evolutionary computations large-scale investment multiobjective optimization portfolio optimization uncertain random variable

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Extending interval branch-and-bound from two to few objectives in nonlinear multiobjective optimization

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JOURNAL OF GLOBAL optimization 2025年 1-26页

作者： Araya, Ignacio Reyes, Victor Montero, Javier Pontificia Univ Catolica Valparaiso Valparaiso Chile Univ Diego Portales Santiago Chile

Nonlinear multiobjective optimization presents significant challenges, particularly when addressing problems with three or more objectives. Building on our previous work using Interval Branch and Bound (B&B) techniques for biobjective optimization, we extend these methods to handle the increased complexity of multiobjective scenarios. Interval B&B methods involve dividing the original problem into smaller subproblems and solving them recursively to achieve a desired level of accuracy. These methods offer the advantage of guaranteeing global optimality and providing rigorous bounds on solution quality. We introduce a refined representation of non-dominated regions using two sets of vectors: the non dominated feasible vectors found so far and its associated set of extreme vectors. To accurately define the feasible non-dominated region within each subspace, we adapt an "envelope constraint" from biobjective solvers. Additionally, we propose a novel method for computing the distance from a subspace to the dominated region, and enhance a peeling technique for contracting the subspace based on the dominated region and the envelope constraint. Our approach is evaluated on a set of benchmark problems, and our results show a significant improvement in solution quality and convergence speed compared to a basic approach. Specifically, our enhanced strategy achieves a 42% reduction in relative CPU time, with a remarkable average time reduction of 63% in problems with three objectives. The code of our solver can be found in our git repository (https://***/INFPUCV/ibex-lib/tree/master/plugins/optim-mop).

关键词： Interval methods Branch and Bound multiobjective optimization

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Causal Inference-Based Large-Scale multiobjective optimization

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2025年第2期29卷 444-458页

作者： Li, Bingdong Yang, Yanting Yang, Peng Li, Guiying Tang, Ke Zhou, Aimin East China Normal Univ Shanghai Frontiers Sci Ctr Mol Intelligent Synth Sch Comp Sci & Technol Shanghai Inst AI Educ Shanghai 200062 Peoples R China East China Normal Univ Key Lab Adv Theory & Applicat Stat & Data Sci Minist Educ Shanghai 200062 Peoples R China East China Normal Univ Shanghai Inst AI Educ Shanghai Frontiers Sci Ctr Mol Intelligent Synth Shanghai 200062 Peoples R China East China Normal Univ Sch Comp Sci & Technol Shanghai 200062 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Dept Stat & Data Sci Shenzhen 518055 Peoples R China Peng Cheng Lab Dept Networked Intelligence Shenzhen 518000 Peoples R China

Large-scale multiobjective optimization problems (LSMOPs), characterized by a substantial number of decision variables, pose significant challenges for many existing evolutionary algorithms. However, the search efficiency of these algorithms is not yet satisfactory. This is mainly because that the search efficiency of these algorithms may deteriorate dramatically since the search space increases exponentially with the number of decision variables. Having this in mind, we proposed a large-Scale multiobjective optimization framework named causal inference-based competitive swarm optimizer (CI-CSO). Specifically, a causal-information-(CI)-based operator is designed for competitive swarm optimizers. First, a causal inference technique named information geometric causal inference (IGCI) is introduced to adequately explore the CI between decision variables and fitness values. To further distinguish the positive or negative impacts of these critical variables on solution quality, a CI processing module is designed, facilitating targeted optimization. To enhance search efficiency, CI-based offspring generator are employed, leveraging the variance of causal effects to dynamically adjust the search step size and sampling range. To evaluate its performance, the proposed CI-based operator is embedded into two multiobjective evolutionary algorithms (MOEAs) (LSTPA and LMOCSO). To demonstrate the effectiveness of the proposed framework, experimental results are presented using the LSMOP test suite and five real-world problems, each involving up to 10 000 decision variables. In addition, six classic algorithms are included for comparison.

关键词： optimization Convergence Inference algorithms Evolutionary computation Robustness Data models Vectors Transfer functions Space exploration Search problems Causal inference competitive swarm optimizer large-scale optimization multiobjective optimization

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On Stationarity Conditions and Constraint Qualifications for multiobjective optimization Problems with Cardinality Constraints

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APPLIED MATHEMATICS AND optimization 2025年第1期91卷 1-26页

作者： Garmanjani, Rohollah Krulikovski, Evelin H. M. Ramos, Alberto NOVA Sch Sci & Technol NOVA FCT Ctr Math & Applicat NOVA Math Caparica Portugal Univ Fed Parana Dept Math Curitiba Brazil Univ Tarapaca Dept Matemat Casilla 7D Arica Chile

The purpose of this paper is to develop Pareto optimality conditions and constraint qualifications (CQs) for multiobjective Programs with Cardinality Constraints (MOPCaC). In general, such problems are difficult to solve, not only because they involve a cardinality constraint that is neither continuous nor convex, but also because there may be a potential conflict between the various objective functions. Thus, we reformulate the MOPCaC based on the problem with continuous variables, namely the relaxed problem. Furthermore, we consider different notions of optimality (weak/strong Pareto optimal solutions). Thereby, we define new stationarity conditions that extend the classical Karush-Kuhn-Tucker (KKT) conditions of the scalar case. Moreover, we also introduce new CQs, based on the recently defined multiobjective normal cone, to ensure compliance with such stationarity conditions. Important statements are illustrated by examples.

关键词： multiobjective optimization Cardinality constraints Nonlinear programming Sparse solutions Constraint qualifications Stationarity

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A Novel multiobjective optimization Approach for EV Charging and Vehicle-to-Grid Scheduling Strategy

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2025年第1期2025卷

作者： Aurangzeb, Muhammad Wang, Yifei Iqbal, Sheeraz Shafiullah, Md Alghamdi, Sultan Ullah, Zahid Southeast Univ Sch Elect Engn Nanjing 210000 Peoples R China King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Sustainable Energy Syst Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Control & Instrumentat Engn Dept Dhahran 31261 Saudi Arabia King Abdulaziz Univ Ctr Res Excellence Renewable Energy & Power Syst Dept Elect & Comp Engn Jeddah 21589 Saudi Arabia King Abdulaziz Univ Fac Engn Dept Elect & Comp Engn Jeddah 21589 Saudi Arabia Politecn Milan Dipartimento Elettron Informaz & Bioingn I-20133 Milan Italy

In this study, we proposed a novel multiobjective optimization technique for electric vehicles' (EVs) charging and vehicle-to-grid (V2G) scheduling. The ring seal search (RSS) algorithm ensures the optimum compatibility of the EV charging and discharging profiles revolving around multiple objectives, such as cost of charging, peak load demand reduction, and grid stability. The proposed algorithm is tested on the distribution model through the IEEE 33-bus system. A comprehensive model with real-time data from EV charging station operators (CSOs) is also ported in this research so that the EVs can be illustrated in the power distribution network. A convenient energy management strategy (EMS) called multiobjective optimization has been introduced to provide practical solutions for CSOs and EV users. The strategy had been developed based on multiple objectives to counter different trade-offs, including EV charging and discharging profiles suitable for numerous objectives encompassing charging costs, peak load demand reduction, and grid stability. The efficiency of the deterministic approach has been verified via extensive simulations and analysis, and the outcomes incorporate a definite enhancement in metrics since the RSS algorithm considerably optimized EV charging and V2G scheduling. The EV charging and discharging profiles had been optimized to take better advantage of available resource requirements by accommodating priority-based scheduling. The RSS will be able to investigate the modified parameter and the modified real system, which tells about the versatility and adaptability of the proposed method, i.e., it will be able to incorporate the changing implementation and its real-world efficacy, which is an immense merit for the adaptation of the real system. The proposed method offers a comprehensive, advanced EV charging and V2G scheduling solution. It addresses the limitations of previous methods by considering multiple objectives, utilizing a novel optimization

关键词： multiobjective optimization power distribution networks renewable energy ring seal search vehicle-to-grid (V2G)

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