Multi-objective Optimization Criteria Research Articles

Optimal Sizing and Energy Management of Electric Vehicle Hybrid Energy Storage Systems With Multi-Objective Optimization Criterion

Optimal Sizing and Energy Management of Electric Vehicle Hybrid Energy Storage Systems With Multi-Objective Optimization Criterion

Multi-objective optimization of actuators and consensus ADP-based vibration control for the large flexible space structures

In this study, the multi-objective optimization and decision-making for optimal positions of actuators and consensus adaptive dynamic programming (CADP) are investigated to mitigate the vibration of large flexible space structures (LFSS). The optimization of the actuator positions maintains a balance between maximizing actuation efficiency and maximizing input voltage decoupling. Meanwhile, the CADP control method accelerates the attenuation of vibration when agents collaborate in the designed communication topology network. First, the electromechanical coupled dynamic model of the LFSS is built by the finite element method. Subsequently, the multi-objective optimization criteria are proposed, which maximize the actuation efficiency and decoupling of control inputs. Moreover, the multi-objective optimization and decision-making, which is based on the non-dominated sorting differential evolutionary algorithm (NSDE) and technique for order preference by similarity to ideal solution (TOPSIS), respectively, are performed to rapidly find the optimal position of actuators. In addition, the CADP control algorithm is designed and its stability is proven. Finally, for harmonic excitation under multi-frequency superposition, simulation comparisons based on the CADP and adaptive dynamic programming (ADP) are performed. Simulation results verify the effectiveness of the proposed optimization criterion of actuators and the CADP algorithm for vibration mitigation of LFSS.

Context-Aware Wireless Connectivity and Processing Unit Optimization for IoT Networks

A novel approach is presented in this work for context-aware connectivity and processing optimization of Internet of Things (IoT) networks. Different from the state-of-the-art approaches, the proposed approach simultaneously selects the best connectivity and processing unit (e.g., device, fog, and cloud) along with the percentage of data to be offloaded by jointly optimizing energy consumption, response time, security, and monetary cost. The proposed scheme employs a reinforcement learning algorithm and manages to achieve significant gains compared to deterministic solutions. In particular, the requirements of IoT devices in terms of response time and security are taken as inputs along with the remaining battery level of the devices, and the developed algorithm returns an optimized policy. The results obtained show that only our method is able to meet the holistic multiobjective optimization criteria, albeit, the benchmark approaches may achieve better results on a particular metric at the cost of failing to reach the other targets. Thus, the proposed approach is a device-centric and context-aware solution that accounts for the monetary and battery constraints.

A methodology for dam parameter identification combining machine learning, multi-objective optimization and multiple decision criteria

Physical and mechanical parameter identification could provide useful information for dam health monitoring. Traditional methods for parameter identification commonly use subjective weights to convert multi-objective problems into single-objective problems, which may result in unreasonable identification results. In recent years, multi-objective optimization algorithms have been applied to avoid introduction of subjective weights in parameter identification. Since multi-objective optimization algorithms produce a massive number of Pareto solutions, how to select acceptable parameters from the Pareto solutions is an intractable problem, and effective decision criteria for selecting acceptable solutions is required. In this paper, a methodology combining machine learning, multi-objective optimization, and multiple decision criteria is proposed to effectively improve efficiency and credibility of parameter identification for dams. A surrogate model constructed using the Support Vector Machine (SVM) based on grid search and cross-validation is employed to replace time-consuming finite element calculations in the process of parameter identification. Multiple decision criteria incorporating influencing factors of parameter identification (such as the membership degree of identified parameters with respect to designed ones, fitting error of monitoring data, and the degree of overfitting) are devised to compare Pareto solutions derived from multi-objective optimization and determine the optimal one. Parameter identification of an actual gravity dam is implemented to illustrate and verify the proposed methodology. It is shown that the results yielded with the proposed methodology are superior to those produced by traditional methods in terms of robustness of reasonable parameter identification, prediction accuracy of monitoring data, and reasonable physical meaning of material parameters.

Model-Based Optimization of Mannitol Production by Using a Sequence of Batch Reactors for a Coupled Bi-Enzymatic Process—A Dynamic Approach

Multi-enzymatic reactions can successfully replace complex chemical syntheses, using milder reaction conditions, and generating less waste. The present model-based analysis compares the performances of several optimally operated Batch Reactors (BR) with those of an optimally operated serial Sequence of BRs (SeqBR). In multi-enzymatic systems, SeqBR could be more advantageous and flexible, allowing the optimization of costly enzymes amounts used in each BR in the series. Exemplification was made for the bi-enzymatic reduction of D-fructose to mannitol by using MDH (mannitol dehydrogenase) and the NADH cofactor, with the in situ continuous regeneration of NADH at the expense of formate degradation in the presence of FDH (formate dehydrogenase). For such coupled enzymatic systems, the model-based engineering evaluations are difficult tasks, because they must account for the common species’ initial levels, their interaction, and their dynamics. The determination of optimal operating modes of sole BR or of a SeqBR turns into a multi-objective optimization problem with multiple constraints to be solved for every particular system. The study presents multiple elements of novelty: (i) the proof of higher performances of an optimal SeqBR (including N-BRs) compared to a sole optimal BR operated for N-number of runs and (ii) the effect of using a multi-objective optimization criteria on SeqBR adjustable dynamics.

Model-Based Simulation Approach for Pre-Front End Engineering Design Studies for Subsea Field Architecture Development

Summary Presented is a model-based methodology identifying subsea field architectures that satisfy prespecified multiphysics constraints. The proposed methodology prioritizes the identified subsea system using a multiobjective optimization approach considering two objective functions, which are minimizing pressure drop reflecting the maximization of production flow rates and minimizing capital expenditures. The architecture solutions produce manifolds positioning and optimal pipeline routing/sizing. A convex combination approach creates the multiobjective optimization criterion enabling weighting among constraints such as hydraulic, topological, structural, and flow assurance, as well as technical issues and financial limitations. The optimization problem is computationally solved using a hybrid method with a global multistart algorithm that combines a scatter search process with a gradient-based local nonlinear problem solver. A case study is provided to test the proposed methodology including the effect of varying the weights among the constraints. This deep-dive analysis demonstrates the potential offered by the proposed methodology, illustrated by the ability to perform several investigations such as wells-grouping analysis and insulation effect on the overall optimization procedure, as well as to provide a tracking tool for flow-assurance factors, namely erosion and corrosion rates along the subsea layout. Hence, we present a demonstration of the capabilities of the proposed model-based subsea field layout optimization procedure.

Modelling surface quality of abrasive water jet processing at multi-objective optimization criteria

Al–Mg alloys belong to high mechanical properties aluminum alloys. They are used for different industries such as: automotive, and ship building, etc. Most of these industries depend on large sheet processing. Productivity of the used conventional processing techniques is considered a drawback specially for large plates cutting. High processing temperature can deteriorate the cut material properties. Abrasive water jet process (AWJ) is one of the modern nontraditional machining processes. It can be used for cutting most of materials with approximately zero thermal effect and relatively high cutting speed. Despite the high productivity of this technique, however quality of the cut surface is considered a challenge. During this study AWJ technique will be investigated for understanding the cutting quality behavior at high cutting speed without forfeiting the cutting quality. A control model is developed to understand the interactive relation between the control factors and output responses of the cutting process. It can be used for: prediction the surface behavior at different control parameters combinations, and recommend the control parameters which satisfy a certain surface quality. Cutting speed, main stream pressure, and (stand-off distance) were employed as control factors. Quality of cutting process will be assessed in terms of surface roughness, striation forming, kerf width, and cut taper angel. Model is optimized and verified experimentally at different conditions to evaluate the accuracy of the model prediction. The model validation shows a very good correlation between the experimental and optimization results.

An optimization design strategy of 1–3 piezocomposite ultrasonic transducer for imaging applications

An optimization design strategy based on finite element method (FEM) and particle swarm optimization (PSO) algorithm is developed to fabricate high-performance 1–3 piezocomposite ultrasonic transducer. In this strategy, the structure and performance of ultrasonic transducer are simulated by FEM based PZFlex software to analyze the effect of design parameters (ceramic volume fraction and kerf width) on its performance. Due to the time-consuming of FEM simulation, based on the data obtained by simulations, artificial neural network models are established to describe the mapping relation of the design parameters and performance parameters (center frequency and high electromechanical coupling coefficient). The multi-objective optimization criterion is constructed for ultrasonic transducer, and then the modified PSO algorithm are adopted to optimize design parameters. According to the optimized design parameters, the FEM simulation and experiment are conducted to validate the effectiveness of the developed strategy. The simulation and experiment results are in agreement with the designed performance, which indicates that the developed strategy can be used to optimize design parameters for fabricating high-performance ultrasonic transducer. In addition, the imaging experiments of tungsten wires and RMB coin prove the high imaging performance of the ultrasonic transducer fabricated by the developed strategy.

Signal Generation for Switched Reluctance Motors using Parallel Genetic Algorithms

Abstract Switched reluctance motors (SRM) are an inherent part in robotics and automation systems where energy and cost efficiency is required. This motor type has no windings and permanent magnets on the rotor which results in a simple and robust structure. However, SRMs require a complex electronic control system to generate a specified number of voltage pulses for each motor phase. This paper presents the signal generation of multiple phases using only one current sensor in an asymmetric half bridge (AHB). In addition to maintain the predetermined phase voltages, sufficient current measurement windows and a minimal current ripple for the individual phases are further optimization criteria for signal generation. The generation of a state vector which controls the individual semiconductor for each motor phase to achieve a required phase voltage and simultaneously fulfill the multi-objective optimization criteria is challenging. Due to the vast number of possible solutions, a genetic algorithm (GA) was used to find state combinations that are suitable for the formulated optimization criteria. The results were discussed and recommendations about the genotype representation and the used genetic operators were given. Interested readers will find detailed information about the software technical implementation using the Global Optimization Toolbox from MATLAB.

Reliability nodal pricing considering CO2 emissions in a multi objective function

This paper proposes a pricing model based on reliability which considers CO2 emissions using a multi-objective optimization criterion. The model performs a reliability analysis for the order of up to N-2 contingencies, the nodal prices per contingency are grouped into two user classes to have prices that reflect reliability and CO2 emissions. The first prices for reliability are class A which considers contingencies of the order N-1 and class B which considers emissions of the order N-1 and N-2. The objective functions to be optimized in this research work are: 1) prices for reliability and 2) CO2 emissions for contingency, because they have a greater impact on the reliability of the transmission system. In this case the price for emissions is reflected to the end user according to the type of contingency you want to have. The methodology determines the ENNS and contingency emissions to obtain the expected values per class. The methodology proposed in this article was implemented to optimize reliability and CO2 emissions in the 24-node test system proposed by the IEEE achieving satisfactory results.

Optimal Design for Distributed Secondary Voltage Control in Islanded Microgrids: Communication Topology and Controller

This paper proposes an optimal design algorithm for distributed secondary voltage control in islanded microgrids (MGs), including communication topology and controller gains. First, upon the consensus-based secondary voltage control, the sufficient condition for network connectivity of communication topology is revealed by the reachability matrix. A multi-objective optimization criterion is first proposed for the network design, taking the convergence performance, network-relevant time delays, and communication costs into consideration. After obtaining the Pareto frontier of this multi-objective model, an optimal network is selected to meet the practical requirements. Based on static output feedback, a small-signal dynamic model of an MG installed with a secondary voltage controller is established, where the distributed secondary voltage controller can be converted into an equivalent decentralized controller. Thereby, a linear quadratic regulator is formulated for the near-optimal design of controller parameters. Our approach customizes the optimal design framework of the topology and controller, which have been largely ignored in the existing literatures. Therefore, it promises to improve the performance of distributed secondary control. The effectiveness of the proposed methodology is verified by a simulation study.

COMPUTATIONAL DESIGN FOR THE PRESERVATION AND ENHANCEMENT OF HISTORICAL HERITAGE. A PREFABRICATED COVERING PROTOTYPE FOR ARCHAEOLOGICAL SITES IN RURAL CONTEXT

Abstract In Italy, many archaeological sites in peri-urban and rural areas are in a deep state of neglect, with unprotected remains and no prospects of enhancement. The paper shows the results of a study aimed at developing a parametric prototype of a prefabricated coverage, to be adapted to various conditions. The Roman Domus of the Acropolis of Populonia in the archaeological park of Baratti and Populonia in Tuscany was chosen as case study. The prototype was parametrically realized using heuristic algorithms based on natural selection (Genetic Algorithms), associated with multi-objective optimization criteria. The algorithm was conceived to optimize the support pillars’ position and the visiting path, in relation to the main points of interest of the archaeological site. The final product consists of a wooden structural covering with 11 steel circular-section pillars, completed with a casing made of wood slats and opaque white PVC sheets.

A bi-objective aggregate production planning problem with learning effect and machine deterioration: Modeling and solution

The learning effects of the workers and machine deterioration in an aggregate production planning (APP) problem have not been taken into account in the literature yet. These factors affect the performance of any real-world production system and require attention. In this paper, a bi-objective optimization model is developed for an APP problem with labor learning effect and machine deterioration. The first objective of this model maximizes the profit by improving learning and reducing the failure cost of the system. The second objective function minimizes the costs associated with repairs and deterioration, which depend on the failure rate of the machines in the production periods. The aim of this article is to obtain appropriate levels of production rates in regular and overtimes, inventory and shortage levels, workers' hiring and firing levels, and the quantities of the products that are subcontracted. To demonstrate the validity of the proposed mathematical formulation, the multi-objective model is converted into a single-objective model using the fuzzy goal programming method, based on which computational experiments are performed on a set of random small-sized instances solved by the LINGO software. As the problem is shown NP-hard, a subpopulation genetic algorithm (SPGA) is proposed to solve large-size problems. In addition, two other meta-heuristics called weighted sum multi-objective genetic algorithm (WMOGA) and non-dominated sorting genetic algorithm II (NSGA-II) are utilized to solve a set of benchmark problems, in order to validate the results obtained and to assess the performance of the SPGA. For tuning the parameters, the Taguchi method is proposed in order to obtain high-quality solutions. Finally, the performances of the proposed algorithms are statistically compared together. The computational results show that SPGA compared to the other algorithms has a better performance in terms of some multi-objective optimization criteria.

Modeling and Designing of a Radial Anode Off-Gas Recirculation Fan for Solid Oxide Fuel Cell Systems

To improve the industry benchmark of solid oxide fuel cell (SOFC) systems, we consider anode off-gas recirculation (AOR) using a small-scale fan. Evolutionary algorithms compare different system design alternatives with hot or cold recirculation. The system performance is evaluated through multi-objective optimization (MOO) criteria, i.e., maximization of electrical efficiency and cogeneration efficiency. The aerodynamic efficiency and rotordynamic stability of the high-speed recirculation fan is investigated in detail. The results obtained suggest that improvements to the best SOFC systems, in terms of net electrical efficiency, are achievable, including for small power scale (10 kWe).

Editorial: Special issue on understanding complexity in multiobjective optimization

This report documents the program and outcomes of the Dagstuhl Seminar 15031 Understanding Complexity in Multiobjective Optimization. This seminar carried on the series of four previous Dagstuhl Seminars (04461, 06501, 09041 and 12041) that were focused on Multiobjective Optimization, and strengthening the links between the Evolutionary Multiobjective Optimization (EMO) and Multiple Criteria Decision Making (MCDM) communities. The purpose of the seminar was to bring together researchers from the two communities to take part in a wide-ranging discussion about the different sources and impacts of complexity in multiobjective optimization. The outcome was a clarified viewpoint of complexity in the various facets of multiobjective optimization, leading to several research initiatives with innovative approaches for coping with complexity. Seminar January 11–16, 2015 – http://www.dagstuhl.de/15031 1998 ACM Subject Classification G.1.6 Optimization, H.4.2 Types of Systems, I.2.6 Learning, I.2.8 Problem Solving, Control Methods, and Search, I.5.1 Models

Artificial Intelligence Based Selection of Optimal Cutting Tool and Process Parameters for Effective Turning and Milling Operations

With the increased trend in automation of modern manufacturing industry, the human intervention in routine, repetitive and data specific activities of manufacturing is greatly reduced. In this paper, an attempt has been made to reduce the human intervention in selection of optimal cutting tool and process parameters for metal cutting applications, using Artificial Intelligence techniques. Generally, the selection of appropriate cutting tool and parameters in metal cutting is carried out by experienced technician/cutting tool expert based on his knowledge base or extensive search from huge cutting tool database. The present proposed approach replaces the existing practice of physical search for tools from the databooks/tool catalogues with intelligent knowledge-based selection system. This system employs artificial intelligence based techniques such as artificial neural networks, fuzzy logic and genetic algorithm for decision making and optimization. This intelligence based optimal tool selection strategy is developed using Mathworks Matlab Version 7.11.0 and implemented. The cutting tool database was obtained from the tool catalogues of different tool manufacturers. This paper discusses in detail, the methodology and strategies employed for selection of appropriate cutting tool and optimization of process parameters based on multi-objective optimization criteria considering material removal rate, tool life and tool cost.

Decision Support System to Determine Intention to Use Mobile Payment Systems on Social Networks: A Methodological Analysis

The rapid growth of social networks has led many companies to use mobile payment systems as business sales tools. As these platforms have an increasing acceptance among the consumers, the main goal of this research is to analyze the individuals’ use intention of these systems in a social network environment. The data used were collected after performing an experiment using the Zong Payment tool. The paper includes a comparative analysis of several machine learning methods applied to the variable selection problem considering strategic, sociodemographic, and behavioral variables. The methods were applied isolatedly and, in a novel approach, using multiobjective optimization criteria. The results obtained by these algorithms were validated by an expert committee, who ranked them on the basis of their quality. The proposed methodology obtained the best results in terms of optimization as well as the highest scores from the experts.

Towards the Next-Generation of Solid Oxide Fuel Cell Systems

Use of intermediate temperature solid oxide fuel cells for power generation is attractive, due to the highest achievable electrical efficiencies in the low power generation range. Nonetheless, there still exists a potential to improve the industry benchmark SOFC systems, which use natural gas or biogas as fuel and consider steam methane reforming, with external steam supply, for syngas production and usage in the stack. Anode off-gas recirculation using a blower is the add-on to our next-generation SOFC system. Since the recirculating feed contains steam produced in the stack, no external steam supply is needed for reforming. This eliminates the use of expensive water de-ionisation sets. Further, it allows for high overall fuel utilization at low diffusion losses. System performance is evaluated through multi-objective optimization criteria, i.e. maximization of electrical efficiency and cogeneration efficiency. Evolutionary algorithms compare the different design alternatives, i.e. co-flow or counter-flow stack operation with hot or cold recirculation. The system flowsheet which includes models for BOP components and an inhouse experimentally validated SOFC stack model, is solved using the commercial software Belsim VALI. The design variables identified for this system are 1. oxygen to carbon ratio before the external reformer 2. external to internal methane reforming fraction 3. reducing fuel species molar fraction at anode outlet 4. air-fuel equivalent ratio in the burner and 5. blower inlet temperature. Successive generations of population are obtained by reproduction and mutation of the existing population. Following the ‘survival of the fittest’ rule, the iteration is stopped when a non-dominated solution set represented by a Pareto-optimal front is obtained. The results obtained (eg. figure 1) suggest that improvements to the best SOFC systems, in terms of net electrical efficiency, are achievable. Figure 1

Towards the Next-Generation of Solid Oxide Fuel Cell Systems

To improve the industry benchmark of solid oxide fuel cell systems (SOFC), we consider anode off-gas recirculation using a blower as an add-on to our next-generation SOFC system. Evolutionary algorithms compare the different design alternatives, i.e. co-flow or counter-flow stack operation with hot or cold recirculation. The system performance is evaluated through multi-objective optimization criteria, i.e. maximization of electrical efficiency and cogeneration efficiency. The results obtained suggest that improvements to the best SOFC systems, in terms of net electrical efficiency, are achievable.

A modified interactive procedure to solve multi-objective group decision making problem

Multi-objective optimization and multiple criteria decision making problems are the process of designing the best alternative by considering the incommensurable and conflicting objectives simultaneously. One of the first interactive procedures to solve multiple criteria decision making problems is STEM method. In this paper we propose a modified interactive procedure based on STEM method by calculating the weight vector of objectives which emphasize that more important objectives be closer to ideal one. We use the AHP and TOPSIS method to find these weights and develop a multi-objective group decision making procedure. Therefore the presented method tries to increase the rate of satisfactoriness of the obtained solution. Finally, a numerical example for illustration of the new method is given to clarify the main results developed in this paper.