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优化 18

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Comparative seismic design optimization of spatial steel dome structures through three recent metaheuristic

《结构与土木工程前沿（英文）》 2022年第16卷第1期页码 57-74 doi: 10.1007/s11709-021-0784-y

摘要： Steel dome structures, with their striking structural forms, take a place among the impressive and aesthetic load bearing systems featuring large internal spaces without internal columns. In this paper, the seismic design optimization of spatial steel dome structures is achieved through three recent metaheuristic algorithms that are water strider (WS), grey wolf (GW), and brain storm optimization (BSO). The structural elements of the domes are treated as design variables collected in member groups. The structural stress and stability limitations are enforced by ASD-AISC provisions. Also, the displacement restrictions are considered in design procedure. The metaheuristic algorithms are encoded in MATLAB interacting with SAP2000 for gathering structural reactions through open application programming interface (OAPI). The optimum spatial steel dome designs achieved by proposed WS, GW, and BSO algorithms are compared with respect to solution accuracy, convergence rates, and reliability, utilizing three real-size design examples for considering both the previously reported optimum design results obtained by classical metaheuristic algorithms and a gradient descent-based hyperband optimization (HBO) algorithm.

关键词： steel dome optimization water strider algorithm grey wolf algorithm brain storm optimization algorithm hyperband optimization algorithm

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Application of adaptive neuro-fuzzy inference system and cuckoo optimization algorithm for analyzing

Reza TEIMOURI, Hamed SOHRABPOOR

《机械工程前沿（英文）》 2013年第8卷第4期页码 429-442 doi: 10.1007/s11465-013-0277-3

摘要：

Electrochemical machining process (ECM) is increasing its importance due to some of the specific advantages which can be exploited during machining operation. The process offers several special privileges such as higher machining rate, better accuracy and control, and wider range of materials that can be machined. Contribution of too many predominate parameters in the process, makes its prediction and selection of optimal values really complex, especially while the process is programmized for machining of hard materials. In the present work in order to investigate effects of electrolyte concentration, electrolyte flow rate, applied voltage and feed rate on material removal rate (MRR) and surface roughness (SR) the adaptive neuro-fuzzy inference systems (ANFIS) have been used for creation predictive models based on experimental observations. Then the ANFIS 3D surfaces have been plotted for analyzing effects of process parameters on MRR and SR. Finally, the cuckoo optimization algorithm (COA) was used for selection solutions in which the process reaches maximum material removal rate and minimum surface roughness simultaneously. Results indicated that the ANFIS technique has superiority in modeling of MRR and SR with high prediction accuracy. Also, results obtained while applying of COA have been compared with those derived from confirmatory experiments which validate the applicability and suitability of the proposed techniques in enhancing the performance of ECM process.

关键词： electrochemical machining process (ECM) modeling adaptive neuro-fuzzy inference system (ANFIS) optimization cuckoo optimization algorithm (COA)

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Estimation of distribution algorithm enhanced particle swarm optimization for water distribution networkoptimization

Xuewei QI,Ke LI,Walter D. POTTER

《环境科学与工程前沿（英文）》 2016年第10卷第2期页码 341-351 doi: 10.1007/s11783-015-0776-z

摘要： The optimization of a water distribution network (WDN) is a highly nonlinear, multi-modal, and constrained combinatorial problem. Particle swarm optimization (PSO) has been shown to be a fast converging algorithm for WDN optimization. An improved estimation of distribution algorithm (EDA) using historic best positions to construct a sample space is hybridized with PSO both in sequential and in parallel to improve population diversity control and avoid premature convergence. Two water distribution network benchmark examples from the literature are adopted to evaluate the performance of the proposed hybrid algorithms. The experimental results indicate that the proposed algorithms achieved the literature record minimum (6.081 M$) for the small size Hanoi network. For the large size Balerma network, the parallel hybrid achieved a slightly lower minimum (1.921M?) than the current literature reported best minimum (1.923M?). The average number of evaluations needed to achieve the minimum is one order smaller than most existing algorithms. With a fixed, small number of evaluations, the sequential hybrid outperforms the parallel hybrid showing its capability for fast convergence. The fitness and diversity of the populations were tracked for the proposed algorithms. The track record suggests that constructing an EDA sample space with historic best positions can improve diversity control significantly. Parallel hybridization also helps to improve diversity control yet its effect is relatively less significant.

关键词： particle swarm optimization (PSO) diversity control estimation of distribution algorithm (EDA) water distribution network (WDN) premature convergence hybrid strategy

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Design and optimization of reactive distillation: a review

《化学科学与工程前沿（英文）》 2022年第16卷第6期页码 799-818 doi: 10.1007/s11705-021-2128-9

摘要： Reactive distillation process, a representative process intensification technology, has been widely applied in the chemical industry. However, due to the strong interaction between reaction and separation, the extension of reactive distillation technology is restricted by the difficulties in process analysis and design. To overcome this problem, the design and optimization of reactive distillation have been widely studied and illustrated for plenty of reactive mixtures over the past three decades. These design and optimization methods of the reactive distillation process are classified into three categories: graphical, optimization-based, and evolutionary/heuristic methods. The primary objective of this article is to provide an up-to-date review of the existing design and optimization methods. Desired and output information, advantages and limitations of each method are stated, the modification and development for original methodologies are also reviewed. Perspectives on future research on the design and optimization of reactive distillation method are proposed for further research.

关键词： reactive distillation process intensification design method reactive phase diagram optimization algorithm

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粒子群优化算法综述

杨维,李歧强

《中国工程科学》 2004年第6卷第5期页码 87-94

摘要：

粒子群优化（PSO）算法是一种新兴的优化技术，其思想来源于人工生命和演化计算理论。PSO通过粒子追随自己找到的最好解和整个群的最好解来完成优化。该算法简单易实现，可调参数少，已得到广泛研究和应用。详细介绍了PSO的基本原理、各种改进技术及其应用等，并对其未来的研究提出了一些建议。

关键词：群体智能演化算法粒子群优化

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Compressive strength prediction and optimization design of sustainable concrete based on squirrel searchalgorithm-extreme gradient boosting technique

《结构与土木工程前沿（英文）》 doi: 10.1007/s11709-023-0997-3

摘要： Concrete is the most commonly used construction material. However, its production leads to high carbon dioxide (CO₂) emissions and energy consumption. Therefore, developing waste-substitutable concrete components is necessary. Improving the sustainability and greenness of concrete is the focus of this research. In this regard, 899 data points were collected from existing studies where cement, slag, fly ash, superplasticizer, coarse aggregate, and fine aggregate were considered potential influential factors. The complex relationship between influential factors and concrete compressive strength makes the prediction and estimation of compressive strength difficult. Instead of the traditional compressive strength test, this study combines five novel metaheuristic algorithms with extreme gradient boosting (XGB) to predict the compressive strength of green concrete based on fly ash and blast furnace slag. The intelligent prediction models were assessed using the root mean square error (RMSE), coefficient of determination (R²), mean absolute error (MAE), and variance accounted for (VAF). The results indicated that the squirrel search algorithm-extreme gradient boosting (SSA-XGB) yielded the best overall prediction performance with R² values of 0.9930 and 0.9576, VAF values of 99.30 and 95.79, MAE values of 0.52 and 2.50, RMSE of 1.34 and 3.31 for the training and testing sets, respectively. The remaining five prediction methods yield promising results. Therefore, the developed hybrid XGB model can be introduced as an accurate and fast technique for the performance prediction of green concrete. Finally, the developed SSA-XGB considered the effects of all the input factors on the compressive strength. The ability of the model to predict the performance of concrete with unknown proportions can play a significant role in accelerating the development and application of sustainable concrete and furthering a sustainable economy.

关键词： sustainable concrete fly ash slay extreme gradient boosting technique squirrel search algorithm parametric analysis

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Split-order consolidation optimization for online supermarkets: Process analysis and optimization models

《工程管理前沿（英文）》页码 499-516 doi: 10.1007/s42524-022-0221-5

摘要： The large-scale online supermarket is a newly emerging online retailing mode which brings great convenience to people. Online supermarkets are characterized by having large amounts of daily orders with potentially multiple items, diverse delivery times, and a high order-split rate. Multiple shipments for one order caused by order splitting result in high cost and disturbance and a large number of discarded consumable packages at online retailers and customers, causing severe damage to the environment. Accordingly, research on split-order consolidation fulfilment is critical for the advancement of the practice and theory in the context of highly complex online retailing. This paper first analyzes the characteristics and the challenges associated with the split-order consolidation problem that online supermarket is confronting and summarizes the new operational process of split-order consolidation fulfilment. Then, a time–space network optimization model is built, and its corresponding solution algorithm is presented to solve the questions of where and when to consolidate the split orders. Finally, the computation results of the numerical experiments are provided to verify the effectiveness of the algorithm, and a sensitivity analysis of the relevant parameters is performed. This work highlights the effect of order consolidation processes and fulfilment methods on the order fulfilment decision-making for online supermarkets. The purpose of this article is to help pave the way for more effective online supermarket management and order implementation.

关键词： online supermarkets split-order consolidation time–space network genetic algorithm

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Enterprise-wide optimization of integrated planning and scheduling for refinery-petrochemical complexwith heuristic algorithm

《化学科学与工程前沿（英文）》 2023年第17卷第10期页码 1516-1532 doi: 10.1007/s11705-022-2283-7

摘要： This paper focuses on the integrated problem of long-term planning and short-term scheduling in a large-scale refinery-petrochemical complex, and considers the overall manufacturing process from the upstream refinery to the downstream petrochemical site. Different time scales are incorporated from the planning and scheduling subproblems. At the end of each discrete time period, additional constraints are imposed to ensure material balance between different time scales. Discrete time representation is applied to the planning subproblem, while continuous time is applied to the scheduling of ethylene cracking and polymerization processes in the petrochemical site. An enterprise-wide mathematical model is formulated through mixed integer nonlinear programming. To solve the problem efficiently, a heuristic algorithm combined with a convolutional neural network (CNN), is proposed. Binary variables are used as the CNN input, leading to the integration of a data-driven approach and classical optimization by which a heuristic algorithm is established. The results do not only illustrate the detailed operations in a refinery and petrochemical complex under planning and scheduling, but also confirm the high efficiency of the proposed algorithm for solving large-scale problems.

关键词： planning scheduling refinery-petrochemical convolutional neural network heuristic algorithm

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Optimization of aero-engine pipeline for avoiding vibration based on length adjustment of straight-line

《机械工程前沿（英文）》 2022年第17卷第1期页码 11-11 doi: 10.1007/s11465-021-0667-x

摘要： In the design and troubleshooting of aero-engine pipeline, the vibration reduction of the pipeline system is often achieved by adjusting the hoop layout, provided that the shape of pipeline remains unchanged. However, in reality, the pipeline system with the best antivibration performance may be obtained only by adjusting the pipeline shape. In this paper, a typical spatial pipeline is taken as the research object, the length of straight-line segment is taken as the design variable, and an innovative optimization method of avoiding vibration of aero-engine pipeline is proposed. The relationship between straight-line segment length and parameters that determine the geometric characteristics of the pipeline, such as the position of key reference points, bending angle, and hoop position, are derived in detail. Based on this, the parametric finite element model of the pipeline system is established. Taking the maximum first-order natural frequency of pipeline as the optimization objective and introducing process constraints and vibration avoidance constraints, the optimization model of the pipeline system is established. The genetic algorithm and the golden section algorithm are selected to solve the optimization model, and the relevant solution procedure is described in detail. Finally, two kinds of pipelines with different total lengths are selected to carry out a case study. Based on the analysis of the influence of straight-line segment length on the vibration characteristics of the pipeline system, the optimization methods developed in this paper are demonstrated. Results show that the developed optimization method can obtain the optimal single value or interval of the straight-line segment length while avoiding the excitation frequency. In addition, the optimization efficiency of the golden section algorithm is remarkably higher than that of the genetic algorithm for length optimization of a single straight-line segment.

关键词： length adjustment spatial pipeline aero-engine vibration avoidance optimization genetic algorithm golden section algorithm

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Application of the invasive weed optimization algorithm to economic dispatch problems

T. JAYABARATHI, Afshin YAZDANI, V. RAMESH

《能源前沿（英文）》 2012年第6卷第3期页码 255-259 doi: 10.1007/s11708-012-0202-1

摘要： In this paper the invasive weed optimization algorithm has been applied to a variety of economic dispatch (ED) problems. The ED problem is concerned with minimizing the fuel cost by optimally loading the electrical generators which are committed to supply a given demand. Some involve prohibited operating zones, transmission losses and valve point loading. In general, they are non-linear non-convex optimization problems which cannot be directly solved by conventional methods. In this work the invasive weed algorithm, a meta-heuristic method inspired by the proliferation of weeds, has been applied to four numerical examples and has resulted in promising solutions compared to published results.

关键词： economic dispatch (ED) invasive weed optimization non-convexity prohibited operating zones (POZ) valve point loading meta-heuristic

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基于嵌入协作的多方法协作优化方法

罗文彩,罗世彬,王振国

《中国工程科学》 2004年第6卷第4期页码 51-55

摘要：

提出一种基于嵌入协作的多方法协作优化方法。算法采用嵌入方式组织各个优化方法之间的协作，利用优化方法之间的协作效应提高优化性能。进行遗传算法、模式搜索法和Powell法嵌入协作组成的多方法协作优化方法设计。计算实例表明，基于嵌入协作的多方法协作优化方法取得了优于单个优化方法的全局最优特性。

关键词：多方法协作优化方法嵌入协作遗传算法模式搜索法 Powell法

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Optimal design of steel skeletal structures using the enhanced genetic algorithm methodology

Tugrul TALASLIOGLU

《结构与土木工程前沿（英文）》 2019年第13卷第4期页码 863-889 doi: 10.1007/s11709-019-0523-9

摘要： This study concerns with the design optimization of steel skeletal structures thereby utilizing both a real-life specification provisions and ready steel profiles named hot-rolled I sections. For this purpose, the enhanced genetic algorithm methodology named EGAwMP is utilized as an optimization tool. The evolutionary search mechanism of EGAwMP is constituted on the basis of generational genetic algorithm (GGA). The exploration capacity of EGAwMP is improved in a way of dividing an entire population into sub-populations and using of a radial basis neural network for dynamically adjustment of EGAwMP’s genetic operator parameters. In order to improve the exploitation capability of EGAwMP, the proposed neural network implementation is also utilized for prediction of more accurate design variables associating with a new design strategy, design codes of which are based on the provisions of LRFD_AISC V3 specification. EGAwMP is applied to determine the real-life ready steel profiles for the optimal design of skeletal structures with 105, 200, 444, and 942 members. EGAwMP accomplishes to increase the quality degrees of optimum designations Furthermore, the importance of using the real-life steel profiles and design codes is also demonstrated. Consequently, EGAwMP is suggested as a design optimization tool for the real-life steel skeletal structures.

关键词： design optimization genetic algorithm multiple populations neural network

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Multi-objective optimization of combustion, performance and emission parameters in a jatropha biodieselengine using Non-dominated sorting genetic algorithm-II

Sunil Dhingra,Gian Bhushan,Kashyap Kumar Dubey

《机械工程前沿（英文）》 2014年第9卷第1期页码 81-94 doi: 10.1007/s11465-014-0287-9

摘要：

The present work studies and identifies the different variables that affect the output parameters involved in a single cylinder direct injection compression ignition (CI) engine using jatropha biodiesel. Response surface methodology based on Central composite design (CCD) is used to design the experiments. Mathematical models are developed for combustion parameters (Brake specific fuel consumption (BSFC) and peak cylinder pressure (P_max)), performance parameter brake thermal efficiency (BTE) and emission parameters (CO, NO_x, unburnt HC and smoke) using regression techniques. These regression equations are further utilized for simultaneous optimization of combustion (BSFC, P_max), performance (BTE) and emission (CO, NO_x, HC, smoke) parameters. As the objective is to maximize BTE and minimize BSFC, P_max, CO, NO_x, HC, smoke, a multi-objective optimization problem is formulated. Non-dominated sorting genetic algorithm-II is used in predicting the Pareto optimal sets of solution. Experiments are performed at suitable optimal solutions for predicting the combustion, performance and emission parameters to check the adequacy of the proposed model. The Pareto optimal sets of solution can be used as guidelines for the end users to select optimal combination of engine output and emission parameters depending upon their own requirements.

关键词： jatropha biodiesel fuel properties response surface methodology multi-objective optimization non-dominated sorting genetic algorithm-II

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A simple multi-wave algorithm for the uncapacitated facility location problem

Fred GLOVER, Saïd HANAFI, Oualid GUEMRI, Igor CREVITS

《工程管理前沿（英文）》 2018年第5卷第4期页码 451-465 doi: 10.15302/J-FEM-2018038

摘要：

The multi-wave algorithm (Glover, 2016) integrates tabu search and strategic oscillation utilizing repeated waves (nested iterations) of constructive search or neighborhood search. We propose a simple multi-wave algorithm for solving the Uncapacitated Facility Location Problem (UFLP) to minimize the combined costs of selecting facilities to be opened and of assigning each customer to an opened facility in order to meet the customers’ demands. The objective is to minimize the overall cost including the costs of opening facilities and the costs of allocations. Our experimental tests on a standard set of benchmarks for this widely-studied class of problems show that our algorithm outperforms all previous methods.

关键词： discrete optimization UFLP multi-wave optimization strategic oscillation tabu search

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退火-遗传算法寻优及其实现

王英

《中国工程科学》 2008年第10卷第7期页码 57-59

摘要：

分析了遗传算法及退火算法的优缺点，提出用退火算法改进遗传算法局部的最优值搜索效率低问题。退火算法与遗传算法融合后，使算法在寻优结果上更加迅速精确。通过水泥的配比工程实例，与单纯的遗传算法的结果进行对比，说明该方法是有效的。

关键词：遗传算法退火算法遗传算法改进

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标题作者时间类型操作

Comparative seismic design optimization of spatial steel dome structures through three recent metaheuristic

期刊论文

Application of adaptive neuro-fuzzy inference system and cuckoo optimization algorithm for analyzing

Reza TEIMOURI, Hamed SOHRABPOOR

期刊论文

Estimation of distribution algorithm enhanced particle swarm optimization for water distribution networkoptimization

Xuewei QI,Ke LI,Walter D. POTTER

期刊论文

Design and optimization of reactive distillation: a review

期刊论文

粒子群优化算法综述

杨维,李歧强

期刊论文

Compressive strength prediction and optimization design of sustainable concrete based on squirrel searchalgorithm-extreme gradient boosting technique

期刊论文

Split-order consolidation optimization for online supermarkets: Process analysis and optimization models

期刊论文

Enterprise-wide optimization of integrated planning and scheduling for refinery-petrochemical complexwith heuristic algorithm

期刊论文

Optimization of aero-engine pipeline for avoiding vibration based on length adjustment of straight-line

期刊论文

Application of the invasive weed optimization algorithm to economic dispatch problems

T. JAYABARATHI, Afshin YAZDANI, V. RAMESH

期刊论文

基于嵌入协作的多方法协作优化方法

罗文彩,罗世彬,王振国

期刊论文

Optimal design of steel skeletal structures using the enhanced genetic algorithm methodology

Tugrul TALASLIOGLU

期刊论文

Multi-objective optimization of combustion, performance and emission parameters in a jatropha biodieselengine using Non-dominated sorting genetic algorithm-II

Sunil Dhingra,Gian Bhushan,Kashyap Kumar Dubey

期刊论文

A simple multi-wave algorithm for the uncapacitated facility location problem

Fred GLOVER, Saïd HANAFI, Oualid GUEMRI, Igor CREVITS

期刊论文

退火-遗传算法寻优及其实现

王英

期刊论文