Multi-objective Optimization Techniques for Solving Complex Engineering Problems

Engineering education certification is a special certification for the implementation of engineering education in higher education institutions, which requires undergraduate education to be implemented in the analysis and solution of complex engineering problems. This paper analyzes the problems existing in the teaching process of "modern control theory", and puts forward the reform measures of "modern control theory", that is, optimizing the teaching content on the basis of guaranteeing the theoretical system of the course; Multiple measures to stimulate students' interest in learning; Strengthen the engineering attribute and times characteristic of the course content; Improve students' ability to analyze and solve complex automation engineering problems.

Like FEM, the boundary element method (BEM) provides a general numerical tool for the solution of complex engineering problems. In the last decades, the range of its applications has remarkably been enlarged. Therefore dynamic and nonlinear problems can be tackled. Nevertheless, they still demand an explicit expression of a fundamental solution, which is only known in simple cases. Therefore, the present book proposes an alternative BEM-formulation based on the Fourier transform, which can be applied to almost all cases relevant in engineering mechanics. The basic principle is presented for the heat equation. Applications are taken from solid mechanics (e.g. poroelasticity, thermoelasticity). Transient and stationary examples are given as well as linear and nonlinear. Completed with a mathematical and mechanical glossary, the book will serve as a comprehensive text book linking applied mathematics to real world engineering problems.

As the world is progressing towards faster and more efficient computing techniques, cloud computing has emerged as an efficient and cheaper solution to such increasing and demanding requirements. Cloud computing is a computing model which facilitates not only the end-users but also organizational and other enterprise users with high availability of resources on demand basis. This involves the use of scientific workflows that require large amount of data processing, which can be costly and time-consuming if not properly scheduled in cloud environment. Various scheduling strategies have been developed, which include swarm-based optimization approaches as well. Due to the presence of multiple and conflicting requirements of users, multi-objective optimization techniques have become popular for workflow scheduling. This paper deals with cat swarm-based multi-objective optimization approach to schedule workflows in a cloud computing environment. The objectives considered are minimization of cost, makespan and CPU idle time. Proposed technique gives improved performance, compared with multi-objective particle swarm optimization (MOPSO) technique.

<p>Overview And Use of Nature Inspired Algorithms for Developing Optimized Solution of Complex Engineering Problems: A Review</p>

The intensive development of the adjacent Arctic zone of Russia (ASR) is connected with the necessity of the use of information and logical model. Effi cient organization of the process of development requires, first of all, the comprehensive development of all areas of transport: air, sea, rail, road, pipeline and is based on the solution of complex engineering problems encountered in exploration and development of mineral deposits and transportation of refi ned products in arctic conditions.

Complex problem solving has been consistently listed by World Economic Forum as one of the top ten skills that employers look for in graduates in 2015, 2020, 2025 and 2030. Washington Accord requires that engineering students come up with design solutions for complex engineering problems. Similarly, ABET emphasized that engineering students must be prepared for practice through a curriculum culminating in a major design experience. This study assessed the perception of complex engineering problems among capstone design students. Preliminary results show that while senior engineering students believe that they have experienced solving complex engineering problems in earlier courses, they do not fully understand what it meant and how to classify a problem as a complex engineering problem. The completion of this research is expected to benefit both students and teachers as it provides insights on how to improve the curriculum and practices in the implementation of complex engineering problems.

Multi-objective optimization technique has become an essential step in the selection of cutting parameters. The intension of this research study is to analyze the performance characteristics of coated carbide inserts on their measured output responses during machining AISI 1015 steel. This paper targets to optimize the machining parameters such as speed, cutting depth, feed rate, cutting fluid flow rate, and coating material when multiple responses like surface roughness and flank wear were considered at the same time during turning. This research study also intends to examine scientifically the effect of machining parameters on quality measures during machining structural AISI 1015 steel. Cathodic arc evaporation–coated titanium aluminum nitride (TiAlN), titanium aluminum nitride/tungsten carbide-carbon (TiAlN/WC-C), and uncoated CNC inserts were used for the study. SEM and energy-dispersive X-ray analysis were performed to confirm the presence of coated elements. Micro-hardness was measured for coated, pure inserts, and TiAlN/WC-C-coated tool exhibited a higher hardness of 22.11 GPa compared with pure and coated tools. Five process parameters were used for this study, each at three stages. The experimental design was laid based on Taguchi’s L27 orthogonal array. In this research study, a multi-objective hybrid optimization technique comprising grey relation and fuzzy logic conjugated with the Taguchi design of experiments was used. The process parameters were optimized by grey relation analysis followed by fuzzification using Mamdani fuzzy engine and then optimized through Taguchi analysis. The parameter combination of speed 500 rpm, depth of cut of 1 mm, a feed rate of 0.05 mm/rev, cutting fluid flow rate at high level, and TiAlN/WC-C coating was found to be the optimum input parameters. The confirmatory test was also performed to validate the hybrid optimization approach.

In data center companies, cloud computing can host multiple types of heterogeneous virtual machines (VMs) and provide many features, including flexibility, security, support, and even better maintenance than traditional centers. However, some issues need to be considered, such as the optimization of energy usage, utilization of resources, reduction of time consumption, and optimization of virtual machine placement. Therefore, this paper proposes an alternative multiobjective optimization (MOP) approach that combines the salp swarm and sine-cosine algorithms (MOSSASCA) to determine a suitable solution for virtual machine placement (VMP). The objectives of the proposed MOSSASCA are to maximize mean time before a host shutdown (MTBHS), to reduce power consumption, and to minimize service level agreement violations (SLAVs). The proposed method improves the salp swarm and the sine-cosine algorithms using an MOP technique. The SCA works by using a local search approach to improve the performance of traditional SSA by avoiding trapping in a local optimal solution and by increasing convergence speed. To evaluate the quality of MOSSASCA, we perform a series of experiments using different numbers of VMs and physical machines. The results of MOSSASCA are compared with well-known methods, including the nondominated sorting genetic algorithm (NSGA-II), multiobjective particle swarm optimization (MOPSO), a multiobjective evolutionary algorithm with decomposition (MOEAD), and a multiobjective sine-cosine algorithm (MOSCA). The results reveal that MOSSASCA outperforms the compared methods in terms of solving MOP problems and achieving the three objectives. Compared with the other methods, MOSSASCA exhibits a better ability to reduce power consumption and SLAVs while increasing MTBHS. The main differences in terms of power consumption between the MOSCA, MOPSO, MOEAD, and NSGA-II and the MOSSASCA are 0.53, 1.31, 1.36, and 1.44, respectively. Additionally, the MOSSASCA has higher MTBHS value than MOSCA, MOPSO, MOEAD, and NSGA-II by 362.49, 274.70, 585.73 and 672.94, respectively, and the proposed method has lower SLAV values than MOPSO, MOEAD, and NSGA-II by 0.41, 0.28, and 1.27, respectively.

Quantum computing is a fast evolving subject with a promise of highly efficient solutions to difficult and complex problems in science and engineering. With the advent of large-scale quantum computation, a lot of effort is invested in finding new applications of quantum algorithms. In this article, we propose an algorithm based on Grover’s adaptative search for multiobjective optimization problems where access to the objective functions is given via two different quantum oracles. The proposed algorithm, considering both types of oracles, are compared against NSGA-II, a highly cited multiobjective optimization evolutionary algorithm. Experimental evidence suggests that the quantum optimization method proposed in this work is at least as effective as NSGA-II in average, considering an equal number of executions.

ESA: Expert structural analysis for engineers

Various evolving approaches have been extensively applied to evolve densely connected communities in complex networks. However these techniques have been primarily single objective optimization techniques, which optimize only a specific feature of the network missing on other important features. Multiobjective optimization techniques can overcome this drawback by simultaneously optimizing multiple features of a network. This paper proposes MGSO, a multiobjective variant of Group Search Optimization (GSO) algorithm to globally search and evolve densely connected communities. It uses inherent animal food searching behavior of GSO to simultaneously optimize two negatively correlated objective functions and overcomes the drawbacks of single objective based CD algorithms. The algorithm reduces random initializations which results in fast convergence. It was applied on 6 real world and 33 synthetic network datasets and results were compared with varied state of the art community detection algorithms. The results established show the efficacy of MGSO to find accurate community structures.

Abstract: The Engineering Accreditation Council Standard 2020 requires the stipulated 12 programme outcomes that address complex engineering problems (CEP) and complex engineering activities (CEA) to be incorporated in the engineering programmes. However, the implementation of outcome-based education with regards to CEP and CEA is still in the infancy stage. This study was carried out to give an overview of the implementation of complex engineering problems and activities in Malaysian engineering programmes in relation to the types of assessment used to address CEP and CEA, and the typical weightage and taxonomy level of CEP and CEA employed in these assessments. A survey questionnaire was administered to 256 engineering educators from 25 universities to enquire about the implementation of CEP and CEA in their engineering programmes, particularly in the online teaching and learning during the Covid19 pandemic. The findings showed that most of the programmes addressed complex engineering problems in assignments or projects as compared to final examinations or mid-term tests. Complex engineering activities were found to be addressed in Final Year Project, Industrial Training and Integrated Design Project and laboratory courses. The findings in this study could act as a guideline for educators to enhance the teaching and learning activities incorporating CEP and CEA elements, and hence facilitating the continual quality improvement for an engineering programme.
 
 Keywords: Complex Engineering Problems, Complex Engineering Activity, Engineering Accreditation Council, Outcome-Based Education

This article has been retracted by the publisher.

This paper aims at developing an innovative technique of concurrent multi-objective optimisation for updating the multi-scale model of long-span bridges. A multi-scale model is established for the purpose of concurrently analysing the global response of the structure and nonlinear local damages in order to assess structural state and local damage evolution or deteriorating, respectively. A multi-objective optimisation technique is proposed in this work for concurrent multi-scale model updating, in which several key issues including the determination of the objective functions and constraint conditions, the multi-objective optimisation algorithm and how to find the optimal solution from many non-inferior solutions are studied. The proposed concurrent multi-objective optimisation technique is applied to update the initial multi-scale model of Runyang Suspension Bridge (RYSB) near Shanghai, and the updated model is validated by the data from the field tests conducted for obtaining the response in global (dynamic properties) and local levels.

Priority based [formula omitted] dominance: A new measure in multiobjective optimization