Wide Solution Space Research Articles

A Tabu-Matching Heuristic Algorithm Based on Temperature Feasibility for Efficient Synthesis of Heat Exchanger Networks

The non-structural model of heat exchanger networks (HENs) offers a wide solution space for optimization due to the random matching of hot and cold streams. However, this stochastic matching can sometimes result in infeasible structures, leading to inefficient optimization. To address this issue, a tabu matching based on a heuristic algorithm for HENs is proposed. The proposed tabu-matching method involves three main steps: First, the critical temperature levels—high, medium, and low-temperature intervals—are determined based on the inlet and outlet temperatures of streams. Second, the number of nodes is set according to the temperature intervals. Third, the nodes of streams are flexibly matched within the tabu rules: the low-temperature interval of hot streams with the high-temperature interval of cold streams; the streams crossing cannot be matched. The results revealed that by incorporating the tabu rules and adjusting the number of nodes, the ratio of the feasible zone in the whole solution domain increases, and the calculation efficiency is enhanced. To evaluate the effectiveness of the method, three benchmark problems were studied. The obtained total annual costs (TACs) of these case studies exhibited a decrease of USD 4290/yr (case 1), USD 1435/yr (case 2), and USD 11,232/yr (case 3) compared to the best published results. The results demonstrate that the proposed tabu-matching heuristic algorithm is effective and robust.

Gaussian Processes for Transfer Path Analysis Applied on Vehicle Body Vibration Problems

<div class="section abstract"><div class="htmlview paragraph">Transfer path analyses of vehicle bodies are widely considered as an important tool in the noise, vibration and harshness design process, as they enable the identification of the dominating transfer paths in vibration problems. It is highly beneficial to model uncertain parameters in early development stages in order to account for possible variations on the final component design. Therefore, parameter studies are conducted in order to account for the sensitivities of the transfer paths with respect to the varying input parameters of the chassis components. To date, these studies are mainly conducted by performing sampling-based finite element simulations. In the scope of a sensitivity analysis or parameter studies, however, a large amount of large-scale finite element simulations is required, which leads to extremely high computational costs and time expenses. This contribution presents a method to drastically reduce the computational burden of typical sampling-based simulations. For this purpose, Gaussian processes are introduced to find a probabilistic function approximation of the transfer paths. Initial results reveal that a wider solution space can be explored by only observing a few transfer path samples. This entails a time-efficient and robust technique, which inherently incorporates the variability of the input parameters. As such, Gaussian processes offer a versatile solution strategy for transfer path analyses, where simulation data as well as experimental measurements can be holistically investigated.</div></div>

Comparative study on metaheuristic algorithms for optimising wave energy converters

The optimisation of WECs is a focus current research. With the increase in optimisation variables, traditional optimisation methods are insufficient to find the best design in a wide and complex solution space, and more advanced algorithms are required. In addition, the iterative optimisation of the WEC requires a reliable and accurate WEC simulation method to ensure the effectiveness of optimisation. Therefore, a MATLAB-APDL-AQWA united simulation system (MAA-system) is developed to calculate the WEC generation power accurately. Then, a memory mechanism is designed to avoid calculating the fitness of duplicate individuals in the search process by creating a memory base to store individual coding information and fitness information. Seven metaheuristic algorithms — the genetic algorithm, differential evolution, immune algorithm (IA), covariance matrix adaptation evolution strategy (CMA-ES), particle swarm optimisation, ant colony optimisation, and simulated annealing — with memory mechanisms are utilised to optimise the draft, power take-off parameter, and layout of WEC globally in random wave conditions. The performances of these algorithms in different search spaces are analysed. The results show CMA-ES is the best choice for solving the unimodal optimisation problem of WEC, and IA is more suitable for multimodal optimisation such as the WEC array layout.

A digital-based design methodology for the optimization of high-performance multi-material structures

A central process in composite lightweight engineering is the design of different fibre-reinforced parts. Certain steps during the design process have the potential that the overall workload can significantly be reduced, by the use of modern software tools. This often means that a compromise between optimization and increasing development costs must be found in order to balance structural complexity, number of design iteration loops and subsequent changes in the requirements. We introduce a computer-driven automation process for a multi-domain, parameter-driven design optimization. The proposed concept was built around the idea that the methodology can be used with different software tools that are already in operation for the design process of lightweight structures and therefore allows an easy implementation into already existing development chains. The developed process was successfully demonstrated by designing high-speed glass fibre rotors with respect to their structural and dynamic performance. The results showed a significant decrease in time spending during the design phase with the benefit to quickly adapt the design to subsequent changes in the optimization goal. Additionally, a wider solution space can be taken into account, which increases the quality of the optimization results.

Predesign Methodology of Voltage Inverters Using a Gradient-Based Optimization Algorithm

This article presents an optimization process applied to the sizing of high-power silicon carbide inverters. The case study is the design of an aeronautical converter. A gradient-based optimization method is chosen for its ability to explore a wide solution space with a large amount of constraints, which is the case during the predesign phase. Many topologies are selected and the associated models are developed. Among all these models, active and passive component design and spectral analysis of voltages and currents are presented. The optimization results, with the goal of minimizing the global mass of the converter, are provided. The different topologies are compared to find the best one for the selected case study. The relevance of the optimization result is evaluated with the study of the optimality of the solution. Finally, the experimental results are compared with the optimization ones, which validates the good accuracy of analytical models.

Optimal location and dimensioning of capacitors in microgrids using a multicriteria decision algorithm

This work presents a methodology for optimal compensation of reactive power in Electric Microgrids using a multicriteria decision algorithm based on heuristic methods. It was verified the optimal location and dimensioning of fixed capacitor banks in a microgrid with 14 buses with objective functions based on cost, efficiency and quality criteria in a maximum demand operating condition and also considering the constraints of objective variables in minimum demand scenarios. The proposed capacitance to be installed was considered as a discrete variable. The location of discretized reactive capacitances was simulated at candidate nodes analyzing the power flow in the case study, resulting in a wide solution space that was tackled by means of multicriteria optimization, using dominance elimination techniques and the weighted sum method for decision making. The variables analyzed were: cost, maximum and average deviations of the voltage profile, power factor, total losses in the lines of the system and THD. All these variables were also verified in minimum demand scenarios. The proposed solution provides significant improvements in the variables analyzed and verifies the optimal performance of the technique. The mathematical analysis demonstrates the need of addressing the reactive compensation problem by means of multicriteria decision and the proposal provides a very novel tool for calculating the location and dimensioning of reactive compensation devices in distribution systems and microgrids. The programming was done in the Matlab environment with simulations using Simulink. The case study analyzed is a very novel validated Microgrid system of which it is known the variables that take part of this analysis, as an approximation of the study of a very real Microgrid.

Optimal Location and Dimensioning of Capacitors in Microgrids Using a Multicriteria Decision Algorithm

This work presents a methodology for optimal compensation of reactive power in Electric Microgrids using a multicriteria decision algorithm based on heuristic methods. It was verified the optimal location and dimensioning of fixed capacitor banks in a microgrid with 14 buses with objective functions based on cost, efficiency and quality criteria in a maximum demand operating condition and also considering the constraints of objective variables in minimum demand scenarios. The proposed capacitance to be installed was considered as a discrete variable. The location of discretized reactive capacitances was simulated at candidate nodes analyzing the power flow in the case study, resulting in a wide solution space that was tackled by means of multicriteria optimization, using dominance elimination techniques and the weighted sum method for decision making. The variables analyzed were: cost, maximum and average deviations of the voltage profile, power factor, total losses in the lines of the system and THD. All these variables were also verified in minimum demand scenarios. The proposed solution provides significant improvements in the variables analyzed and verifies the optimal performance of the technique. The mathematical analysis demonstrates the need of addressing the reactive compensation problem by means of multicriteria decision and the proposal provides a very novel tool for calculating the location and dimensioning of reactive compensation devices in distribution systems and microgrids. The programming was done in the Matlab environment with simulations using Simulink. The case study analyzed is a very novel validated Microgrid system of which it is known the variables that take part of this analysis, as an approximation of the study of a very real Microgrid.

Composition Assessment of a Power Distribution System with Optimal Dispatching of Distributed Generation

Increasing penetration of distributed generation (DG) is imminent in the new age of power distribution networks, which are smarter than the conventional grids. They enable the integration of DG into the power distribution network. This paper presents an assessment methodology for determining the optimal capacity and location of DG to ensure high reliability in a radial distribution network. The approach considers cost and the impact of aging on the DG and network topology for interconnection using genetic algorithm, which is a robust technique with wide solution space searchability and can potentially find global optima with fewer chances of getting trapped into local optima. A case study is simulated using three different scenarios to evaluate the impact of DG interconnection on the 13.8 kV power distribution network. The scenarios comprise of situations without any DG, with DG interconnection and optimization of DG interconnection. The case study shows that the penetration of DG increases the reliability of the distribution network while reducing the expected energy not supplied (EENS). Although, the difference between EENS in the optimized DG integration and non-optimized DG integration is not very significant in a small network, however, it becomes apparent with the aging curve that optimized allocation of DG possesses significant benefits.

Modularity in making: simplifying solution space for user innovation

An increasingly popular form of open innovation in the digital age is ‘making,’ where users innovate across multiple disciplines and make products that meet their needs, using mechanical, electronic, and digital components. These users have at their disposal, a wide solution space for innovation through various modular toolkits enabled by digital‐age technologies. This study explores and outlines how these users simplify this wide solution space to innovate and make tangible products. Following a modularity theory perspective, it draws on case studies of users and their innovations: (1) Users with initial prototype product designs based on the Internet of things (IoT) from a maker event and (2) users with established product designs from the online community platform Thingiverse. The studies found that users reused the design in the form of existing off‐the‐shelf products and utilized digital fabrication and low‐cost electronics hardware as a ‘glue’ to create physical and informational interfaces wherever needed, enabling bottom‐up modularity. They iteratively refined their innovations, gradually replacing re‐used designs with own integrated designs, reducing modularity, and reducing wastage. The study contributes to open innovation and modularity with implications on the design of products and toolkits enabled by the digital age.

Cross-Layer Optimization for High Speed Adders: A Pareto Driven Machine Learning Approach

In spite of maturity to the modern electronic design automation (EDA) tools, optimized designs at architectural stage may become sub-optimal after going through physical design flow. Adder design has been such a long studied fundamental problem in VLSI industry yet designers cannot achieve optimal solutions by running EDA tools on the set of available prefix adder architectures. In this paper, we enhance a state-of-the-art prefix adder synthesis algorithm to obtain a much wider solution space in architectural domain. On top of that, a machine learning-based design space exploration methodology is applied to predict the Pareto frontier of the adders in physical domain, which is infeasible by exhaustively running EDA tools for innumerable architectural solutions. Considering the high cost of obtaining the true values for learning, an active learning algorithm is utilized to select the representative data during learning process, which uses less labeled data while achieving better quality of Pareto frontier. Experimental results demonstrate that our framework can achieve Pareto frontier of high quality over a wide design space, bridging the gap between architectural and physical designs.

A Boolean network inference from time-series gene expression data using a genetic algorithm.

Inferring a gene regulatory network from time-series gene expression data is a fundamental problem in systems biology, and many methods have been proposed. However, most of them were not efficient in inferring regulatory relations involved by a large number of genes because they limited the number of regulatory genes or computed an approximated reliability of multivariate relations. Therefore, an improved method is needed to efficiently search more generalized and scalable regulatory relations. In this study, we propose a genetic algorithm-based Boolean network inference (GABNI) method which can search an optimal Boolean regulatory function of a large number of regulatory genes. For an efficient search, it solves the problem in two stages. GABNI first exploits an existing method, a mutual information-based Boolean network inference (MIBNI), because it can quickly find an optimal solution in a small-scale inference problem. When MIBNI fails to find an optimal solution, a genetic algorithm (GA) is applied to search an optimal set of regulatory genes in a wider solution space. In particular, we modified a typical GA framework to efficiently reduce a search space. We compared GABNI with four well-known inference methods through extensive simulations on both the artificial and the real gene expression datasets. Our results demonstrated that GABNI significantly outperformed them in both structural and dynamics accuracies. The proposed method is an efficient and scalable tool to infer a Boolean network from time-series gene expression data. Supplementary data are available at Bioinformatics online.

Nanoarrays for Systolic Biosequence Analysis

Applications like biosequence alignment are currently addressed using traditional technology at the price of a huge overhead in terms of area and power dissipation. Nanoarrays are expected to outperform current limits especially in terms of processing capabilities. The purpose of this work is to assess the real terms of these expectations. Our contribution deals with: (i) a new model for nanowire FETs used to evaluate transistor’s essential performance; (ii) a new switch-level simulator for nanoarray structure used to evaluate its switching activity; (iii) a nanoarray implementation for biosequence alignment based on a systolic array and the modeling of its essential performance based on (i) and (ii); (iv) the evaluation of the potential improvement of the nanoarray-based systolic structure with respect to an equivalent CMOS one in terms of processing capabilities, area, and power dissipation. Depending on the possible technological scenario, the performance of nanoarray is impressive, especially considering the density achievable in terms of processing per unit area. A wide solution space can be explored to find the optimal solution in terms of trading power and performance considering the technological limitations of a realistic implementation.

Extended Genetic Algorithm for solving open-shop scheduling problem

Open-shop scheduling problem (OSSP) is a well-known topic with vast industrial applications which belongs to one of the most important issues in the field of engineering. OSSP is a kind of NP problems and has a wider solution space than other basic scheduling problems, i.e., Job-shop and flow-shop scheduling. Due to this fact, this problem has attracted many researchers over the past decades and numerous algorithms have been proposed for that. This paper investigates the effects of crossover and mutation operator selection in Genetic Algorithms (GA) for solving OSSP. The proposed algorithm, which is called EGA_OS, is evaluated and compared with other existing algorithms. Computational results show that selection of genetic operation type has a great influence on the quality of solutions, and the proposed algorithm could generate better solutions compared to other developed algorithms in terms of computational times and objective values.

A formal functional representation methodology for conceptual design of material-flow processing devices

AbstractAlthough there has been considerable computer-aided conceptual design research, most of the proposed approaches are domain specific and can merely achieve conceptual design of energy flows-processing systems. Therefore, this research is devoted to the development of a general (i.e., domain-independent) and knowledge-based methodology that can search in a wide multidisciplinary solution space for suitable solution principles for desired material-flow processing functions without designers' biases toward familiar solution principles. It first proposes an ontology-based approach for representing desired material-flow processing functions in a formal and unambiguous manner. Then a rule-based approach is proposed to represent the functional knowledge of a known solution principle in a general and flexible manner. Thereafter, a simulation-based retrieval approach is developed, which can search for suitable solution principles for desired material-flow processing functions. The proposed approaches have been implemented as a computer-aided conceptual design system for test. The conceptual design of a coin-sorting device demonstrates that our functional representation methodology can make the proposed computer-aided conceptual design system to effectively and precisely retrieve suitable solution principles for a desired material-flow processing function.

Resonant ultrasound spectroscopy for materials with high damping and samples of arbitrary geometry

AbstractResonant ultrasound spectroscopy (RUS) is a powerful and established technique for measuring elastic constants of a material with general anisotropy. The first step of this technique consists of extracting resonance frequencies and damping from the vibrational frequency spectrum measured on a sample with free boundary conditions. An inversion technique is then used to retrieve the elastic tensor from the measured resonance frequencies. As originally developed, RUS has been mostly applicable to (i) materials with small damping such that the resonances of the sample are well separated and (ii) samples with simple geometries for which analytical solutions exist. In this paper, these limitations are addressed with a new RUS approach adapted to materials with high damping and samples of arbitrary geometry. Resonances are extracted by fitting a sum of exponentially damped sinusoids to the measured frequency spectrum. The inversion of the elastic tensor is achieved with a genetic algorithm, which allows searching for a global minimum within a discrete and relatively wide solution space. First, the accuracy of the proposed approach is evaluated against numerical data simulated for samples with isotropic symmetry and transversely isotropic symmetry. Subsequently, the applicability of the approach is demonstrated using experimental data collected on a composite structure consisting of a cylindrical sample of Berea sandstone glued to a large piezoelectric disk. In the proposed experiments, RUS is further enhanced by the use of a 3‐D laser vibrometer allowing the visualization of most of the modes in the frequency band studied.

Resonant ultrasound spectroscopy with large attenuation and arbitrary geometry

Resonant ultrasound spectroscopy (RUS) is a powerful and established technique for measuring material properties. The first step of this technique consists of extracting resonance frequencies and attenuations from the vibrational frequency spectrum measured on a sample with free boundary conditions. An inversion technique is then used to retrieve the elastic tensor from the measured resonance frequencies. As originally developed, RUS has been mostly applicable to (1) weakly attenuating media where each resonance frequency can be clearly identified and (2) relatively simple geometries where analytical solutions exist. In this presentation, the possibility of using RUS in a highly attenuating medium and on a sample of arbitrary geometry is explored. The Kumaresan-Tufts algorithm is used to fit a sum of exponentially damped sinusoids with closely spaced frequencies to the measured frequency spectrum. The inversion of the elastic tensor is achieved with a genetic algorithm, which allows searching for a global minimum within a discrete and relatively wide solution space. The accuracy of the proposed approach is evaluated against numerical data for which the solutions are known a priori. [This work was supported by the U.S. Dept. of Energy, Fuel Cycle R&D, Used Fuel Disposition (Storage) campaign.]

Switching neuronal state: optimal stimuli revealed using a stochastically-seeded gradient algorithm.

Inducing a switch in neuronal state using energy optimal stimuli is relevant to a variety of problems in neuroscience. Analytical techniques from optimal control theory can identify such stimuli; however, solutions to the optimization problem using indirect variational approaches can be elusive in models that describe neuronal behavior. Here we develop and apply a direct gradient-based optimization algorithm to find stimulus waveforms that elicit a change in neuronal state while minimizing energy usage. We analyze standard models of neuronal behavior, the Hodgkin-Huxley and FitzHugh-Nagumo models, to show that the gradient-based algorithm: (1) enables automated exploration of a wide solution space, using stochastically generated initial waveforms that converge to multiple locally optimal solutions; and (2) finds optimal stimulus waveforms that achieve a physiological outcome condition, without a priori knowledge of the optimal terminal condition of all state variables. Analysis of biological systems using stochastically-seeded gradient methods can reveal salient dynamical mechanisms underlying the optimal control of system behavior. The gradient algorithm may also have practical applications in future work, for example, finding energy optimal waveforms for therapeutic neural stimulation that minimizes power usage and diminishes off-target effects and damage to neighboring tissue.

A Cooperative and Competitive Workshop in Mechatronics Engineering

This paper presents a new mechatronics laboratory for students in the 5th semester of the mechatronics degree course at the Karlsruhe Institute of Technology. It is the aim of this teaching event to sharpen the appreciation of synergy effects in the development of mechatronic systems among the students. Despite of the great freedom in the development process, a concept has been evolved, which causes low running costs due to the combination of a model kit with rapid prototyping methods. A first pilot study of the laboratory starting in the winter term 2014 has shown that the students approach the task despite of the high level of difficulty with fun and dedication, especially because of the wide solution space which was unknown for them from previous lectures.

A non dominated ranking Multi Objective Genetic Algorithm and electre method for unequal area facility layout problems

The unequal area facility layout problem (UA-FLP) comprises a class of extremely difficult and widely applicable optimization problems arising in diverse areas and meeting the requirements for real-world applications. Genetic Algorithms (GAs) have recently proven their effectiveness in finding (sub) optimal solutions to many NP-hard problems such as UA-FLP. A main issue in such approach is related to the genetic encoding and to the evolutionary mechanism implemented, which must allow the efficient exploration of a wide solution space, preserving the feasibility of the solutions and ensuring the convergence towards the optimum. In addition, in realistic situations where several design issues must be taken into account, the layout problem falls in the broader framework of multi-objective optimization problems. To date, there are only a few multi-objective FLP approaches, and most of them employ over-simplified optimization techniques which eventually influence the quality of the solutions obtained and the performance of the optimization procedure. In this paper, this difficulty is overcome by approaching the problem in two subsequent steps: in the first step, the Pareto-optimal solutions are determined by employing Multi Objective Genetic Algorithm (MOGA) implementing four separate fitness functions within a Pareto evolutionary procedure, following the general structure of Non-dominated Ranking Genetic Algorithm (NRGA) and the subsequent selection of the optimal solution is carried out by means of the multi-criteria decision-making procedure Electre. This procedure allows the decision maker to express his preferences on the basis of the knowledge of candidate solution set. Quantitative and qualitative objectives are considered referring to the slicing-tree layout representation scheme. The numerical results obtained outperform previous referenced approaches, thus confirming the effectiveness of the procedure proposed.

Multi-Robot Remote Interaction with FS-MAS

The need to reduce bandwidth, improve productivity, autonomy and the scalability in multi-robot teleoperation has been recognized for a long time. In this article we propose a novel finite state machine mobile agent based on the network interaction service model, namely FS-MAS. This model consists of three finite state machines, namely the Finite State Mobile Agent (FS-Agent), which is the basic service module. The Service Content Finite State Machine (Content-FS), using the XML language to define workflow, to describe service content and service computation process. The Mobile Agent computation model Finite State Machine (MACM-FS), used to describe the service implementation. Finally, we apply this service model to the multi-robot system, the initial realization completing complex tasks in the form of multi-robot scheduling. This demonstrates that the robot has greatly improved intelligence, and provides a wide solution space for critical issues such as task division, rational and efficient use of resour...