Journal of Optimization

The multiobjective (MO) optimizers show great promise in solving constrained engineering structural problems. This paper introduces a MO version of the Brown Bear Optimization (BBO) algorithm, inspired by the foraging behavior of brown bears. The proposed Multiobjective Brown Bear Optimization (MOBBO) algorithm is applied to five structural optimization problems, including 10‐bar, 25‐bar, 60‐bar, 72‐bar, and 942‐bar trusses, aiming to minimize both mass and maximum nodal deflection simultaneously. Comparative evaluations against six benchmark algorithms demonstrate MOBBO’s superior convergence, solution diversity, and effectiveness in addressing highly constrained problems. The hypervolume (HV) and inverted generational distance (IGD) metrics place MOBBO in first rank according to the Friedman test, with an average standard deviation of 0.0002. Moreover, the spacing‐to‐extent (STE) and generational distance (GD) metrics rank MOBBO second. The final Friedman rank test highlights MOBBO’s overall dominance, achieving a first rank. Best Pareto plots, diversity graphs, and box plot analyses further suggest MOBBO’s superior performance and convergence compared to existing algorithms. Therefore, the MOBBO algorithm can be effectively applied to various MO optimization tasks in industry, offering refined global optimization solutions and contributing valuable insights to the field of MO algorithms.

Pranav Mehta

Sumit Kumar

Ghanshyam G. Tejani

None Mohammad khishe

MOBBO: A Multiobjective Brown Bear Optimization Algorithm for Solving Constrained Structural Optimization Problems

MOBBO：一种求解约束结构优化问题的多目标棕熊优化算法

Multiwalled carbon nanotubes (MWCNTs)/epoxy thin film nanocomposites were prepared using spin coating technique. The effects of process parameters such as sonication duration (5–35 min) and filler loadings (1-2 vol%) were studied using the design of experiment (DOE). Full factorial design was used to create the design matrix for the two factors with three-level experimentation, resulting in a total of 9 runs (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msup><mml:mrow><mml:mn fontstyle="italic">3</mml:mn></mml:mrow><mml:mrow><mml:mn fontstyle="italic">2</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math>) of experimentation. Response surface methodology (RSM) combined with E.C. Harrington’s desirability function called desirability optimization methodology (DOM) was used to optimize the multiple properties (tensile strength, elastic modulus, elongation at break, thermal conductivity, and electrical conductivity) of MWCNTs/epoxy thin film composites. Based on response surface analysis, quadratic model was developed. Analysis of variance (ANOVA),<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:mi>R</mml:mi></mml:mrow></mml:math>-squared (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mrow><mml:mi>R</mml:mi></mml:mrow></mml:math>-Sq), and normal plot of residuals were applied to determine the accuracy of the models. The range of lower and upper limits was determined in an overlaid contour plot. Desirability function was used to optimize the multiple responses of MWCNTs/epoxy thin film composites. A global solution of 12.88 min sonication and 1.67 vol% filler loadings was obtained to have maximum desired responses with composite desirability of 1.

C. P. Khor

Mariatti bt Jaafar

Sivakumar Ramakrishnan

Optimization of Conductive Thin Film Epoxy Composites Properties Using Desirability Optimization Methodology

利用满意度优化方法优化导电薄膜环氧复合材料性能

In this paper, a novel approach of hybridization of two efficient metaheuristic algorithms is proposed for energy system analysis and modelling based on a hydro and thermal based power system in both single and multiobjective environment. The scheduling of hydro and thermal power is modelled descriptively including the handling method of various practical nonlinear constraints. The main goal for the proposed modelling is to minimize the total production cost (which is highly nonlinear and nonconvex problem) and emission while satisfying involved hydro and thermal unit commitment limitations. The cascaded hydro reservoirs of hydro subsystem and intertemporal constraints regarding thermal units along with nonlinear nonconvex, mixed-integer mixed-binary objective function make the search space highly complex. To solve such a complicated system, a hybridization of Gray Wolf Optimization and Artificial Bee Colony algorithm, that is, h -ABC/GWO, is used for better exploration and exploitation in the multidimensional search space. Two different test systems are used for modelling and analysis. Experimental results demonstrate the superior performance of the proposed algorithm as compared to other recently reported ones in terms of convergence and better quality of solutions.

Suman Sutradhar

Nalin B. Dev Choudhury

Nidul Sinha

Modelling of Hydrothermal Unit Commitment Coordination Using Efficient Metaheuristic Algorithm: A Hybridized Approach

高效元启发式算法在水热机组协调调度建模中的应用：一种混合化方法

Nonnegative matrix factorization (NMF) has been successfully applied in signal processing as a simple two-layer nonnegative neural network. Projective NMF (PNMF) with fewer parameters was proposed, which projects a high-dimensional nonnegative data onto a lower-dimensional nonnegative subspace. Although PNMF overcomes the problem of out-of-sample of NMF, it does not consider the nonlinear characteristic of data and is only a kind of narrow signal decomposition method. In this paper, we combine the PNMF with deep learning and nonlinear fitting to propose a bidirectional nonnegative deep learning (BNDL) model and its optimization learning algorithm, which can obtain nonlinear multilayer deep nonnegative feature representation. Experiments show that the proposed model can not only solve the problem of out-of-sample of NMF but also learn hierarchical nonnegative feature representations with better clustering performance than classical NMF, PNMF, and Deep Semi-NMF algorithms.

Xianhua Zeng

Zhengyi He

Hong Yu

Shengwei Qu

Bidirectional Nonnegative Deep Model and Its Optimization in Learning

双向非负深度模型及其在学习中的优化

In this paper, complexity curtailing techniques are introduced to create faster version of insertion heuristics, that is, cheapest insertion heuristic (CIH) and largest insertion heuristic (LIH), effectively reducing their complexities from<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>O</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:msup><mml:mrow><mml:mi>n</mml:mi></mml:mrow><mml:mrow><mml:mn fontstyle="italic">3</mml:mn></mml:mrow></mml:msup><mml:mo stretchy="false">)</mml:mo></mml:math>to<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mi>O</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:msup><mml:mrow><mml:mi>n</mml:mi></mml:mrow><mml:mrow><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msup><mml:mo stretchy="false">)</mml:mo></mml:math>with no significant effect on quality of solution. This paper also examines relatively not very known heuristic concept of max difference and shows that it can be culminated into a full-fledged max difference insertion heuristic (MDIH) by defining its missing steps. Further to this the paper extends the complexity curtailing techniques to MDIH to create its faster version. The resultant heuristic, that is, fast max difference insertion heuristic (FMDIH), outperforms the “farthest insertion” heuristic (FIH) across a wide spectrum of popular datasets with statistical significance, even though both the heuristics have the same worst case complexity of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mi>O</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:msup><mml:mrow><mml:mi>n</mml:mi></mml:mrow><mml:mrow><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msup><mml:mo stretchy="false">)</mml:mo></mml:math>. It should be noted that FIH is considered best among lowest order complexity heuristics. The complexity curtailing techniques presented here open up the new area of research for their possible extension to other heuristics.

Ziauddin Ursani

David W. Corne

Introducing Complexity Curtailing Techniques for the Tour Construction Heuristics for the Travelling Salesperson Problem

引入旅行商问题巡回构造启发式的复杂度削减技术

We consider the second-order cone function (SOCF) <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"><a:mi>f</a:mi><a:mo>:</a:mo><a:msup><a:mrow><a:mi mathvariant="double-struck">R</a:mi></a:mrow><a:mrow><a:mi>n</a:mi></a:mrow></a:msup><a:mo>⟶</a:mo><a:mi mathvariant="double-struck">R</a:mi></a:math> defined by <e:math xmlns:e="http://www.w3.org/1998/Math/MathML" id="M2"><e:mi>f</e:mi><e:mfenced open="(" close=")" separators="|"><e:mrow><e:mi>x</e:mi></e:mrow></e:mfenced><e:mo>=</e:mo><e:msup><e:mrow><e:mi>c</e:mi></e:mrow><e:mrow><e:mi>T</e:mi></e:mrow></e:msup><e:mi>x</e:mi><e:mo>+</e:mo><e:mi>d</e:mi><e:mo>−</e:mo><e:mrow><e:mfenced open="‖" close="‖" separators="|"><e:mrow><e:mi>A</e:mi><e:mi>x</e:mi><e:mo>+</e:mo><e:mi>b</e:mi></e:mrow></e:mfenced></e:mrow></e:math> , with parameters <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" id="M3"><m:mi>c</m:mi><m:mo>∈</m:mo><m:msup><m:mrow><m:mi mathvariant="double-struck">R</m:mi></m:mrow><m:mrow><m:mi>n</m:mi></m:mrow></m:msup></m:math> , <p:math xmlns:p="http://www.w3.org/1998/Math/MathML" id="M4"><p:mi>d</p:mi><p:mo mathvariant="double-struck">∈</p:mo><p:mi mathvariant="double-struck">R</p:mi></p:math> , <t:math xmlns:t="http://www.w3.org/1998/Math/MathML" id="M5"><t:mi>A</t:mi><t:mo>∈</t:mo><t:msup><t:mrow><t:mi mathvariant="double-struck">R</t:mi></t:mrow><t:mrow><t:mi>m</t:mi><t:mo>×</t:mo><t:mi>n</t:mi></t:mrow></t:msup></t:math> , and <w:math xmlns:w="http://www.w3.org/1998/Math/MathML" id="M6"><w:mi>b</w:mi><w:mo>∈</w:mo><w:msup><w:mrow><w:mi mathvariant="double-struck">R</w:mi></w:mrow><w:mrow><w:mi>m</w:mi></w:mrow></w:msup></w:math> . Every SOCF is concave. We give necessary and sufficient conditions for strict concavity of <z:math xmlns:z="http://www.w3.org/1998/Math/MathML" id="M7"><z:mi>f</z:mi></z:math> . The parameters <bb:math xmlns:bb="http://www.w3.org/1998/Math/MathML" id="M8"><bb:mi>A</bb:mi></bb:math> and <db:math xmlns:db="http://www.w3.org/1998/Math/MathML" id="M9"><db:mi>b</db:mi></db:math> are not uniquely determined. We show that every SOCF can be written in the form <fb:math xmlns:fb="http://www.w3.org/1998/Math/MathML" id="M10"><fb:mi>f</fb:mi><fb:mfenced open="(" close=")" separators="|"><fb:mrow><fb:mi>x</fb:mi></fb:mrow></fb:mfenced><fb:mo>=</fb:mo><fb:msup><fb:mrow><fb:mi>c</fb:mi></fb:mrow><fb:mrow><fb:mi>T</fb:mi></fb:mrow></fb:msup><fb:mi>x</fb:mi><fb:mo>+</fb:mo><fb:mi>d</fb:mi><fb:mo>−</fb:mo><fb:msqrt><fb:mrow><fb:msup><fb:mrow><fb:mi>δ</fb:mi></fb:mrow><fb:mrow><fb:mn>2</fb:mn></fb:mrow></fb:msup><fb:mo>+</fb:mo><fb:msup><fb:mrow><fb:mfenced open="(" close=")" separators="|"><fb:mrow><fb:mi>x</fb:mi><fb:mo>−</fb:mo><fb:msub><fb:mrow><fb:mi>x</fb:mi></fb:mrow><fb:mi>∗</fb:mi></fb:msub></fb:mrow></fb:mfenced></fb:mrow><fb:mrow><fb:mi>T</fb:mi></fb:mrow></fb:msup><fb:mi>M</fb:mi><fb:mrow><fb:mfenced open="(" close=")" separators="|"><fb:mrow><fb:mi>x</fb:mi><fb:mo>−</fb:mo><fb:msub><fb:mrow><fb:mi>x</fb:mi></fb:mrow><fb:mi>∗</fb:mi></fb:msub></fb:mrow></fb:mfenced></fb:mrow></fb:mrow></fb:msqrt></fb:math> . We give necessary and sufficient conditions for the parameters <qb:math xmlns:qb="http://www.w3.org/1998/Math/MathML" id="M11"><qb:mi>c</qb:mi></qb:math> , <sb:math xmlns:sb="http://www.w3.org/1998/Math/MathML" id="M12"><sb:mi>d</sb:mi></sb:math> , <ub:math xmlns:ub="http://www.w3.org/1998/Math/MathML" id="M13"><ub:mi>δ</ub:mi></ub:math> , <wb:math xmlns:wb="http://www.w3.org/1998/Math/MathML" id="M14"><wb:mi>M</wb:mi><wb:mo>=</wb:mo><wb:msup><wb:mrow><wb:mi>A</wb:mi></wb:mrow><wb:mrow><wb:mi>T</wb:mi></wb:mrow></wb:msup><wb:mi>A</wb:mi></wb:math> , and <yb:math xmlns:yb="http://www.w3.org/1998/Math/MathML" id="M15"><yb:msub><yb:mrow><yb:mi>x</yb:mi></yb:mrow><yb:mi>∗</yb:mi></yb:msub></yb:math> to be uniquely determined. We also give necessary and sufficient conditions for <ac:math xmlns:ac="http://www.w3.org/1998/Math/MathML" id="M16"><ac:mi>f</ac:mi></ac:math> to be bounded above.

Shafiu Jibrin

James W. Swift

On Second-Order Cone Functions

关于二阶锥函数

In the field of operation research, both linear and fractional programming problems have been more encountered in recent years because they are more realistic in expressing real-life problems. Fractional programming problem is used when several rates need to be optimized simultaneously such as resource allocation planning, financial and corporate planning, healthcare, and hospital planning. There are several techniques to solve the multiobjective linear fractional programming problem. However, because of the use of scalarization, these techniques have some limitations. This paper proposed two new mean and median techniques to solve the multiobjective linear fractional programming problem by overcoming the limitations. After utilizing mean and median techniques, the problem is converted into an equivalent linear fractional programming problem; then, the linear fractional programming problem is transformed into linear programming problem and solved by the conventional simplex method or mathematical software. Some numerical examples have been illustrated to show the efficiency of the proposed techniques and algorithm. The performance of these solutions was evaluated by comparing their results with other existing methods. The numerical results have shown that the proposed techniques are better than other techniques. Furthermore, the proposed techniques solve a pure multiobjective maximization problem, which is even impossible with some existing techniques. The present investigation can be improved further, which is left for future research.

Getaye Fentaw

Adane Akate

New Mean and Median Techniques to Solve Multiobjective Linear Fractional Programming Problem and Comparison with Other Techniques

解决多目标线性分式规划问题的新均值和中位数技术及与其他技术的比较

As smart cities expand rapidly, the demand for strong fire protection has become even more essential. A significant challenge lies in ensuring that fire detection systems integrate seamlessly into our modern infrastructures. Leveraging multisensor systems can yield reliable data on potential fires. Particularly in smart buildings, the effectiveness of multisensor fire detection algorithms becomes paramount. This research introduces a fuzzy logic-driven method that harnesses the power of smoke, flame, and temperature sensors. While smoke, flames, and elevated temperatures are primary fire indicators, they can manifest concurrently or sequentially. We have amalgamated various fuzzy logic rules (IF-THEN structures) to gauge the intensity of fires. When a fire ignites, the sensors spring into action, identifying its source and promptly notifying users via the Internet and GSM modems. Moreover, they relay the fire’s precise geographic coordinates to fire departments. The fire’s status will be consistently updated on a dedicated online portal. It was observed from our results that the system proficiently sends fire alerts to residents, and the fire status undergoes regular updates at 45-second intervals. This refreshing is initiated by the identification of a designated percentage of smoke and flame. These outcomes validate the system’s effectiveness in enhancing the precision and responsiveness of fire detection capabilities.

Naveen Kumar Vaegae

Visalakshi Annepu

Kalapraveen Bagadi

Fadl Dahan

Multisensor Fuzzy Logic Approach for Enhanced Fire Detection in Smart Cities

智能城市中增强火灾检测的多传感器模糊逻辑方法

The study shows that refrigerants R1234ze (E) and R1234ze (Z), known for their low global warming potential, are likely to become primary choices in heat pump air conditioning for residential and commercial use. The research evaluates the thermodynamic properties of R1234ze (Z) using experimental, thermodynamic, and numerical analyses. It addresses F-gas regulation requirements and aligns with the Paris Agreement goals by exploring various refrigerants, including R227ea, R114, R236fa, R134a, R1234ze (Z), and R245fa, as potential candidates for evolving industry needs. The analysis indicates that R1234ze (Z) outperforms other working fluids in heat pump applications, with an optimized theoretical coefficient of performance (COP) at a condensation temperature 22 K lower than critical temperatures. However, actual COP deviates due to a significant pressure drop, especially with inadequate volumetric capacity. A key finding is that a substantial portion of the pressure reduction is attributed to mitigating irreversible losses, estimated at a condensation temperature of 70–75°C, emphasizing the complex relationship between pressure and performance. The study suggests that R1234ze (Z) is more suitable for high-temperature applications than traditional air conditioning systems. In a parallel assessment, the initial calculation of the coefficient of performance for R245fa evaluates the reliability of a new refrigeration industry arrangement. Test results for heat pump technology reveal that R1234ze (Z) achieves an impressive COP of up to 3.60 at a buildup temperature of 90°C with a temperature differential of 45 K. This positions R1234ze (Z) as a suitable choice for heat pump applications prioritizing simplicity in system design.

短名
Journal Impact	0.84
国际分区	ENGINEERING, MULTIDISCIPLINARY(Q3)

涉及主题	计算机科学数学数学优化算法人工智能经济工程类机器学习程序设计语言操作系统物理统计量子力学哲学生物数学分析粒子群优化管理几何学认识论组合数学计算机网络
出版信息	出版商: Wiley-Hindawi，出版周期: ，期刊类型: journal，开源期刊: 否
基本数据	创刊年份: 2022，原创研究文献占比: 100.00%，自引率:0.00%， Gold OA占比: 100.00%

Journal of Optimization

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MOBBO: A Multiobjective Brown Bear Optimization Algorithm for Solving Constrained Structural Optimization Problems

Optimization of Conductive Thin Film Epoxy Composites Properties Using Desirability Optimization Methodology

Modelling of Hydrothermal Unit Commitment Coordination Using Efficient Metaheuristic Algorithm: A Hybridized Approach

Bidirectional Nonnegative Deep Model and Its Optimization in Learning

Introducing Complexity Curtailing Techniques for the Tour Construction Heuristics for the Travelling Salesperson Problem

On Second-Order Cone Functions

New Mean and Median Techniques to Solve Multiobjective Linear Fractional Programming Problem and Comparison with Other Techniques

Multisensor Fuzzy Logic Approach for Enhanced Fire Detection in Smart Cities

Performance of Heat Pump Air Conditioning with R1234ze (HFO) as a Refrigerant

MOBBO: A Multiobjective Brown Bear Optimization Algorithm for Solving Constrained Structural Optimization Problems

ISSN	2314-6486, 2356-752X
h-index	1