A Comparative Study of Metaheuristic Optimization Algorithms for Solving Real-World Engineering Design Problems

Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to solve.Such design problems are widely experienced in many engineering fields,such as industry,automotive,construction,machinery,and interdisciplinary research.However,there are established optimization techniques that have shown effectiveness in addressing these types of issues.This research paper gives a comparative study of the implementation of seventeen new metaheuristic methods in order to optimize twelve distinct engineering design issues.The algorithms used in the study are listed as:transient search optimization(TSO),equilibrium optimizer(EO),grey wolf optimizer(GWO),moth-flame optimization(MFO),whale optimization algorithm(WOA),slimemould algorithm(SMA),harris hawks optimization(HHO),chimp optimization algorithm(COA),coot optimization algorithm(COOT),multi-verse optimization(MVO),arithmetic optimization algorithm(AOA),aquila optimizer(AO),sine cosine algorithm(SCA),smell agent optimization(SAO),and seagull optimization algorithm(SOA),pelican optimization algorithm(POA),and coati optimization algorithm(CA).As far as we know,there is no comparative analysis of recent and popular methods against the concrete conditions of real-world engineering problems.Hence,a remarkable research guideline is presented in the study for researchersworking in the fields of engineering and artificial intelligence,especiallywhen applying the optimization methods that have emerged recently.Future research can rely on this work for a literature search on comparisons of metaheuristic optimization methods in real-world problems under similar conditions.

The design and engineering strategies of metal tellurides for advanced metal-ion batteries

Owning various crystal structures and high theoretical capacity,metal tellurides are emerging as promising electrode materials for high-performance metal-ion batteries(MBs).Since metal telluride-based MBs are quite new,fundamental issues raise regarding the energy storage mechanism and other aspects affecting electrochemical performance.Severe volume expansion,low intrinsic conductivity and slow ion diffusion kinetics jeopardize the performance of metal tellurides,so that rational design and engineering are crucial to circumvent these disadvantages.Herein,this review provides an in-depth discussion of recent investigations and progresses of metal tellurides,beginning with a critical discussion on the energy storage mechanisms of metal tellurides in various MBs.In the following,recent design and engineering strategies of metal tellurides,including morphology engineering,compositing,defect engineering and heterostructure construction,for high-performance MBs are summarized.The primary focus is to present a comprehensive understanding of the structural evolution based on the mechanism and corresponding effects of dimension control,composition,electron configuration and structural complexity on the electrochemical performance.In closing,outlooks and prospects for future development of metal tellurides are proposed.This work also highlights the promising directions of design and engineering strategies of metal tellurides with high performance and low cost.

Research and application of key technologies for data delivery in railway engineering design based on metadata

In view of the problems of inconsistent data semantics,inconsistent data formats,and difficult data quality assurance between the railway engineering design phase and the construction and operation phase,as well as the difficulty in fully realizing the value of design results,this paper proposes a design and implementation scheme for a railway engineering collaborative design platform.The railway engineering collaborative design platform mainly includes functional modules such as metadata management,design collaboration,design delivery management,model component library,model rendering services,and Building Information Modeling(BIM)application services.Based on this,research is conducted on multi-disciplinary parameterized collaborative design technology for railway engineering,infrastructure data management and delivery technology,and design multi-source data fusion and application technology.The railway engineering collaborative design platform is compared with other railway design software to further validate its advantages and advanced features.The platform has been widely applied in multiple railway construction projects,greatly improving the design and project management efficiency.

Study on Influencing Factors and Control Points of Design Estimate of Landscaping Engineering

In recent years,China’s landscaping projects have developed vigorously,and the growth rate of urban garden green space areas has been maintained at about 5%.Overall,with the development of the national economy and the support of macro policies,people’s demand for close to nature and beautify the environment is gradually increasing,which has brought new growth momentum for the development of the landscaping industry.Simultaneously,from the perspective of future economic development and urban development,the landscaping industry still has a lot of room for development.However,with the rapid development of landscape engineering,the problem of cost control of landscape engineering is becoming more prominent,the phenomenon of budget overestimation is common,and there are many factors affecting the cost of landscape engineering,which brings difficulties and challenges to the analysis of its influencing factors and cost management.How to scientifically analyze the influencing factors and control the cost has become an important link in the landscaping project.To solve the above problems,this paper takes the design stage of landscaping engineering as the background,takes the design estimate of landscaping engineering as the research object,through literature research and data collection,fully excavates the main influencing factors of the design estimate stage of landscaping engineering,analyzes the key points of cost control,and provides reference ideas and directions for the later cost management and control.

An Improved Artificial Rabbits Optimization Algorithm with Chaotic Local Search and Opposition-Based Learning for Engineering Problems and Its Applications in Breast Cancer Problem

Artificial rabbits optimization(ARO)is a recently proposed biology-based optimization algorithm inspired by the detour foraging and random hiding behavior of rabbits in nature.However,for solving optimization problems,the ARO algorithm shows slow convergence speed and can fall into local minima.To overcome these drawbacks,this paper proposes chaotic opposition-based learning ARO(COARO),an improved version of the ARO algorithm that incorporates opposition-based learning(OBL)and chaotic local search(CLS)techniques.By adding OBL to ARO,the convergence speed of the algorithm increases and it explores the search space better.Chaotic maps in CLS provide rapid convergence by scanning the search space efficiently,since their ergodicity and non-repetitive properties.The proposed COARO algorithm has been tested using thirty-three distinct benchmark functions.The outcomes have been compared with the most recent optimization algorithms.Additionally,the COARO algorithm’s problem-solving capabilities have been evaluated using six different engineering design problems and compared with various other algorithms.This study also introduces a binary variant of the continuous COARO algorithm,named BCOARO.The performance of BCOARO was evaluated on the breast cancer dataset.The effectiveness of BCOARO has been compared with different feature selection algorithms.The proposed BCOARO outperforms alternative algorithms,according to the findings obtained for real applications in terms of accuracy performance,and fitness value.Extensive experiments show that the COARO and BCOARO algorithms achieve promising results compared to other metaheuristic algorithms.

Analysis on the development of chemically-improved soil in railway engineering

Purpose-Explore the development trend of chemically-improved soil in railway engineering.Design/methodology/approach–In this paper,the technical standards home and abroad were analyzed.Laboratory test,field test and monitoring were carried out.Findings–The performance design system of the chemically-improved soil should be established.Originality/value–On the basis of the performance design,the test methods and standards for various properties of chemically-improved soil should be established to evaluate the improvement effect and control the engineering quality.

An Improved Elite Slime Mould Algorithm for Engineering Design

The Swarm intelligence algorithm is a very prevalent field in which some scholars have made outstanding achievements.As a representative,Slime mould algorithm(SMA)is widely used because of its superior initial performance.Therefore,this paper focuses on the improvement of the SMA and the mitigation of its stagnation problems.For this aim,the structure of SMA is adjusted to develop the efficiency of the original method.As a stochastic optimizer,SMA mainly stimulates the behavior of slime mold in nature.For the harmony of the exploration and exploitation of SMA,the paper proposed an enhanced algorithm of SMA called ECSMA,in which two mechanisms are embedded into the structure:elite strategy,and chaotic stochastic strategy.The details of the original SMA and the two introduced strategies are given in this paper.Then,the advantages of the improved SMA through mechanism comparison,balance-diversity analysis,and contrasts with other counterparts are validated.The experimental results demonstrate that both mechanisms have a significant enhancing effect on SMA.Also,SMA is applied to four structural design issues of the welded beam design problem,PV design problem,I-beam design problem,and cantilever beam design problem with excellent results.

Designing interactive glazing through an engineering psychology approach:Six augmented reality scenarios that envision future car human-machine interface

Background With an increasing number of vehicles becoming autonomous,intelligent,and connected,paying attention to the future usage of car human-machine interface with these vehicles should become more relevant.Several studies have addressed car HMI but were less attentive to designing and implementing interactive glazing for every day(autonomous)driving contexts.Methods Reflecting on the literature,we describe an engineering psychology practice and the design of six novel future user scenarios,which envision the application of a specific set of augmented reality(AR)support user interactions.Additionally,we conduct evaluations on specific scenarios and experiential prototypes,which reveal that these AR scenarios aid the target user groups in experiencing a new type of interaction.The overall evaluation is positive with valuable assessment results and suggestions.Conclusions This study can interest applied psychology educators who aspire to teach how AR can be operationalized in a human-centered design process to students with minimal pre-existing expertise or minimal scientific knowledge in engineering psychology.

COOPERATIVE DESIGN UNDER CONCURRENT ENGINEERING ENVIRONMENT

This paper outlines a concurrent engineering environment that supports interaction between members of a geographically dispersed multidisciplinary team who is engaged in engineering design activities. Some ideas of engineering design, especially conceptual design are reviewed. A deeper scientific analysis of intelligent design is discussed. A new problem solving strategy and methodologies to implement cooperative design are proposed. Finally, a conceptual model of a blackboard system for accomplishing conceptual design automation is presented. Its fundamental principles, system organization and key implementation techniques are investigated in detail.

DYNAMIC RESPONSE OPTIMIZATION DESIGN FOR ENGINEERING STRUCTURES BASED ON RELIABILITY

In many practical structures, physical parameters of material and applied loads have random property.To optimize this kind of structures,an optimum mathematical model was built.This model has reliability constraints on dynamic stress and displacement and upper & lower limits of the design variables. The numerical characteristic of dynamic response and sensitivity of dynamic response based on probability of structure were deduced respectively. By equivalent disposing, the reliability constraints were changed into conventional forms. The SUMT method was used in the optimization process.Two examples illustrate the correctness and practicability of the optimum model and solving approach.

Integrated Assembly Structure Design Within Concurrent Engineering

Taking a microwave product as an example, a system of integrated assembly structure design is presented. Getting design constraints from the upstream design section through product data management(PDM), the system generates the assembly scheme using the case? based method, then assigns the design requirements into each component of the assembly. The detail design for each component can be performed under these design constraints. In order to practise concurrent design, the system sends the final design result to the upstream section and downstream section through PDM to achieve information sharing and integration.

O-O Analysis of Vehicle Body Design and Establishment of Engineering Database

To establish the object-oriented (O-O) engineering for vehicle body design Methods characteristics of vehicle body design data were analyzed by means of theO-Omethod The analytical results were transformed intothe engineering databasethat canbeusedsufficiently to describe the relative information of vehicle body design data.Results TheO-Oengineeringdatabaseforvehiclebodydesignis realized and practiced in the process ofbody design for a QCJ7082 economical car. Conclusion The method is successful in trans- forming vehicle body design object into engineering database.The database supports each procedure of vehicle body design perfectly

Active Machine Learning for Chemical Engineers:A Bright Future Lies Ahead!

By combining machine learning with the design of experiments,thereby achieving so-called active machine learning,more efficient and cheaper research can be conducted.Machine learning algorithms are more flexible and are better than traditional design of experiment algorithms at investigating processes spanning all length scales of chemical engineering.While active machine learning algorithms are maturing,their applications are falling behind.In this article,three types of challenges presented by active machine learning—namely,convincing the experimental researcher,the flexibility of data creation,and the robustness of active machine learning algorithms—are identified,and ways to overcome them are discussed.A bright future lies ahead for active machine learning in chemical engineering,thanks to increasing automation and more efficient algorithms that can drive novel discoveries.

Construction Method of Ideological and Political Theories Teaching in the Course of Environmental Microbiology for Environmental and Ecological Engineering Major

Environmental Microbiology is a professional basic course in the curriculum system of Environmental and Ecological Engineering major,which follows the requirements of ideological and political theories teaching in the course in higher education,and should be implemented in every course.Therefore,the construction of ideological and political theories teaching in the course should be an important task in the course construction of Environmental Microbiology.In view of the curriculum characteristics of Environmental Microbiology,such as multi-disciplinary feature,wide coverage and rapid development,it is an effective method for the construction of ideological and political theories teaching in the course of Environmental Microbiology by fully tapping the ideological and political elements of the curriculum,such as patriotism,scientific spirit and practical innovation,and integrating the ideological and political theories teaching in the course into the whole curriculum design and classroom teaching construction.

Performance-based seismic design of nonstructural building components:The next frontier of earthquake engineering

With the development and implementation of performance-based earthquake engineering,harmonization of performance levels between structural and nonstructural components becomes vital. Even if the structural components of a building achieve a continuous or immediate occupancy performance level after a seismic event,failure of architectural,mechanical or electrical components can lower the performance level of the entire building system. This reduction in performance caused by the vulnerability of nonstructural components has been observed during recent earthquakes worldwide. Moreover,nonstructural damage has limited the functionality of critical facilities,such as hospitals,following major seismic events. The investment in nonstructural components and building contents is far greater than that of structural components and framing. Therefore,it is not surprising that in many past earthquakes,losses from damage to nonstructural components have exceeded losses from structural damage. Furthermore,the failure of nonstructural components can become a safety hazard or can hamper the safe movement of occupants evacuating buildings,or of rescue workers entering buildings. In comparison to structural components and systems,there is relatively limited information on the seismic design of nonstructural components. Basic research work in this area has been sparse,and the available codes and guidelines are usually,for the most part,based on past experiences,engineering judgment and intuition,rather than on objective experimental and analytical results. Often,design engineers are forced to start almost from square one after each earthquake event: to observe what went wrong and to try to prevent repetitions. This is a consequence of the empirical nature of current seismic regulations and guidelines for nonstructural components. This review paper summarizes current knowledge on the seismic design and analysis of nonstructural building components,identifying major knowledge gaps that will need to be filled by future research. Furthermore,considering recent trends in earthquake engineering,the paper explores how performance-based seismic design might be conceived for nonstructural components,drawing on recent developments made in the field of seismic design and hinting at the specific considerations required for nonstructural components.

Application of computer-aided engineering optimum design method in aluminum profile extrusion mould

The finite element analysis and the optimum design of aluminum profile extrusion mould were investigated using the ANSYS software and its parameterized modeling method. The optimum dimensions of the mould were obtained. It is found that the stress distribution is very uneven, and the stress convergence is rather severe in the bridge of the aluminum profile extrusion mould. The optimum height of the mould is 70.527 mm, and the optimum radius of dividing holes are 70.182 mm and 80.663 mm. Increasing the height of the mould in the range of 61.282 mm to 70.422 mm can prolong its longevity, but when the height is over 70.422 mm, its longevity reduces.

Multiobjective extremal optimization with applications to engineering design

In this paper, we extend a novel unconstrained multiobjective optimization algorithm, so-called multiobjective extremal optimization （MOEO）, to solve the constrained multiobjective optimization problems （MOPs）. The proposed approach is validated by three constrained benchmark problems and successfully applied to handling three multiobjective engineering design problems reported in literature. Simulation results indicate that the proposed approach is highly competitive with three state-of-the-art multiobjective evolutionary algorithms, i.e., NSGA-11, SPEA2 and PAES. Thus MOEO can be considered a good alternative to solve constrained multiobjective optimization problems.

Design methodology for the safety of underground rock engineering

In order to optimise the safety of underground rock engineering construction and the long-term security of the resultant facilities, it is necessary to have a knowledge of the likely hazards. These risks or hazards fall into the four categories of 'known beforehand and relatively easily addressed', 'known beforehand and not easily addressed', 'not known beforehand and relatively easily addressed', and 'not known beforehand and not easily addressed'. This paper describes how these four types of hazard can be incorporated into a design methodology approach, including the process by which the relevant mechanical rock mass parameters can be recognised for modelling and hence predictive purposes. In particular, there is emphasis on the fact that information and judgement are the keys to safety——whether the hazards are known or unknown before construction proceeds.

Data Driven Uncertainty Evaluation for Complex Engineered System Design

Complex engineered systems are often difficult to analyze and design due to the tangled interdependencies among their subsystems and components. Conventional design methods often need exact modeling or accurate structure decomposition, which limits their practical application. The rapid expansion of data makes utilizing data to guide and improve system design indispensable in practical engineering. In this paper, a data driven uncertainty evaluation approach is proposed to support the design of complex engineered systems. The core of the approach is a data-mining based uncertainty evaluation method that predicts the uncertainty level of a specific system design by means of analyzing association relations along different system attributes and synthesizing the information entropy of the covered attribute areas, and a quantitative measure of system uncertainty can be obtained accordingly. Monte Carlo simulation is introduced to get the uncertainty extrema, and the possible data distributions under different situations is discussed in detail The uncertainty values can be normalized using the simulation results and the values can be used to evaluate different system designs. A prototype system is established, and two case studies have been carded out. The case of an inverted pendulum system validates the effectiveness of the proposed method, and the case of an oil sump design shows the practicability when two or more design plans need to be compared. This research can be used to evaluate the uncertainty of complex engineered systems completely relying on data, and is ideally suited for plan selection and performance analysis in system design.

Research on Distributed System Course Under the Background of New Engineering

Distributed System Course is a professional computer course of Harbin Institute of Technology.With the guidance of the education strategy under the background of New Engineering,we adhere to the concept of cultivating diverse and innovative outstanding engineering and technology talents.This course conducts research on the teaching content,teaching mode,and evaluation system.It combines the traditional teaching mode with the online teaching mode,as well as problem-driven theories with practice,and adopts a diversified evaluation system.The research of the course has fully mobilized students’learning enthusiasm,improved teaching quality,and achieved significant teaching results.