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multiobjective optimization of Three-Pass Perforated Muffler Design for Improved Acoustic Performance and Reduced Fluid Pressure Drop Using Genetic Algorithms

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INTERNATIONAL JOURNAL OF AUTOMOTIVE AND MECHANICAL ENGINEERING 2025年第1期22卷 12074-12090页

作者： Suprayitno Pratama, Muhammad Yandi Univ Negeri Malang Dept Mech & Ind Engn Malang 65145 Indonesia

Noise pollution is a serious problem as the vehicle population increases every year. The vehicle exhaust system is a major contributor to noise pollution, while mufflers play a vital role in reducing it. Among various designs, a three-pass perforated is a muffler design that is often used to reduce noise in the vehicle exhaust system. However, a good muffler design should also consider minimizing the pressure drop, where these two requirements conflict. To solve this problem, a multiobjective optimization approach was applied using a non-dominated sorting genetic algorithm (NSGA-II) to find the optimal muffler design solution. Analysis of Variance (ANOVA) was also presented in this study as a statistical tool to determine the muffler design parameters that have a significant effect. To predict the acoustic transmission loss (TL) and fluid pressure drop (PD) inside the muffler, the three-pass perforated was simulated and experimentally verified. The results present two optimal muffler designs that were selected and discussed. The best designs are TL and experiment no.26. The best design TL, produced a muffler with a noise reduction capability (TL) better than the initial design by 9 dBA or 48%, with a PD improvement of 87.3 Pa or 2%. Experimental design no. 26 offers mufflers with noise reduction capability (TL) better than the initial design by 3.2 dBA or 18%, with an improvement in PD of 19.9 Pa or 0.5%. These results offer an alternative muffler design solution that has better noise reduction capability with a small increase in PD. ANOVA results with a significance level of 0.05 show that the design hole diameter parameter has a significant influence on TL performance, as evidenced by a p-value smaller than 0.05. Meanwhile, the ANOVA results for PD performance concluded that none of the design parameters or their interactions had a significant influence on PD performance. Only the design parameter center width and its squared interaction are assumed to have a sign

关键词： multiobjective optimization Three-Pass Perforated Muffler Transmission Loss Pressure Drop NSGA-II

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multiobjective optimization of water–salt interactions enhances tomato yield and quality

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Irrigation Science 2025年 1-18页

作者： Tian, Di Gong, Yidan Jiang, Junxi Xu, Hongkai Wang, Weihua Faculty of Modern Agricultural Engineering Kunming University of Science and Technology Kunming650500 China

Efficient use of saline resources for water-saving irrigation is a priority in modern green agriculture, making it crucial to understand the complex relationships among water, salt, and crop performance. To address this, a multiobjective optimization model was developed for sustainable agricultural practices. Tomatoes were selected for the study, with greenhouse experiments conducted in Kunming, Yunnan Province, China, in 2021 and 2022. Three key factors were examined: growth stages: seedling (T1), flowering and fruit-setting (T2), and maturity (T3). Irrigation water salinity levels were set at 1.0, 2.0, and 4.0 g/L (S1–S3), with S1 representing local shallow groundwater. Irrigation volumes included no deficit irrigation at 70%–80% field capacity (FC), moderate water deficit at 60%–70% FC, and severe water deficit at 50%–60% FC (W1–W3). A control group maintained full growth, with no deficit irrigation (S1W1–S3W1). Results showed that tomato yield and quality peaked at T1S1W2 and T2S2W3, respectively, whereas sucrose metabolism-related enzyme activity was highest at T3S3W3. Optimal factor intervals for each growth stage, determined using membership function and multiple regression spatial analysis, were as follows: T1: S = 3.23–3.55 g/L, W = 64.02%–75.56% FC;T2: S = 2.05–3.29 g/L, W = 65.40%–78.52% FC;T3: S = 3.17–3.32 g/L, W = 72.54%–75.83% FC. In tomato crops, regulating water and salt levels during the seedling stage markedly boosted yield and quality. The widest adjustable irrigation ranges occurred during the flowering and fruiting stage (S = 2.05–3.29 g/L, W = 65.40%–78.52% FC), whereas the narrowest range occurred during the maturity stage (S = 3.17–3.32 g/L, W = 72.54%–75.83% FC). Precise water–salt management at different growth stages could substantially advance agriculture. This study’s findings provide a scientific foundation for modern multiobjective precision agriculture and the efficient use of saline water. © The Author(s), under exclusive licence to

关键词： multiobjective optimization

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multiobjective optimization Using the R2 Utility

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SIAM-ASA Journal on Uncertainty Quantification 2025年第2期67卷 213-255页

作者： Tu, Ben Kantas, Nikolas Lee, Robert M. Shafei, Behrang Department of Mathematics Imperial College London London United Kingdom BASF SE am Rhein Ludwigshafen Germany

The goal of multiobjective optimization is to identify a collection of points which describe the best possible trade-offs among the multiple objectives. In order to solve this vector-valued optimization problem, practitioners often appeal to the use of scalarization functions in order to transform the multiobjective problem into a collection of single-objective problems. This set of scalarized problems can then be solved using traditional single-objective optimization techniques. In this paper, we formalize this convention into a general mathematical framework. We show how this strategy effectively recasts the original multiobjective optimization problem into a single-objective optimization problem defined over sets. An appropriate class of objective functions for this new problem is that of the R2 utilities, which are utility functions that are defined as a weighted integral over the scalarized optimization problem. As part of our work, we show that these utilities are monotone and submodular set functions that can be optimized effectively using greedy optimization algorithms. We then analyze the performance of these greedy algorithms both theoretically and empirically. Our analysis largely focuses on Bayesian optimization, which is a popular probabilistic framework for black-box optimization. © 2025 Society for Industrial and Applied Mathematics.

关键词： multiobjective optimization

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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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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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A non-monotone proximal gradient algorithm for solving nonsmooth multiobjective optimization problems with an extending application to robust multiobjective optimization

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2025年 460卷

作者： Chen, Jiawei Liu, Jie Qin, Xiaolong Yao, Jen-Chih Southwest Univ Sch Math & Stat Chongqing 400715 Peoples R China Hangzhou Normal Univ Dept Math Hangzhou Peoples R China Nanjing Ctr Appl Math Nanjing Peoples R China China Med Univ China Med Univ Hosp Res Ctr Interneural Comp Taichung 40447 Taiwan

In this paper, a new non-monotone proximal gradient method (NPGM) for solving nonsmooth multiobjective optimization problems (MOPs) is introduced by the non-monotone backtracking linear search technique allowing the increase of objective function values in some iterative processes. The level-bounded notion of vector-valued functions is also presented. The asymptotical characterizations of the corresponding sequences generated by NPGM are obtained under some mild conditions. We establish the global convergence of the iterative sequence generated by NPGM and the sufficient condition for the existence of Pareto critical point which is the accumulation point of the iterative sequence. The linear convergence of NPGM is also obtained under the strong convexity and smoothness of the objective functions. As an application, we present a tractable second-order cone programming formulation of robust multiobjective optimization problem (RMOP) by the constraint scalarization method, and then the RMOP is also solved by NPGM. Finally, some numerical experiments are reported to demonstrate the feasibility and efficiency of NPGM for solving MOPs.

关键词： multiobjective optimization Proximal gradient methods Non-monotone line search Robust optimization Pareto critical point Linear convergence

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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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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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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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