Multi-objective Target Research Articles

МЕТОДЫ НЕЧЕТКОГО МНОГОКРИТЕРИАЛЬНОГО ГРУППОВОГО ПРИНЯТИЯ РЕШЕНИЙ ДЛЯ ЗАДАЧ ЭВАКУАЦИИ ПРИ ЧРЕЗВЫЧАЙНЫХ СИТУАЦИЯХ

The purpose of this article is to analyze the current state of research in the field of fuzzymulticriteria optimization methods, as well as the development of aggregation operators and algorithmsusing fuzzy multicriteria group decision-making using an intuitionistic attitude of linguisticpreferences. The most well-known fuzzy methods of multicriteria optimization are presented:ELECTRE, PROMETHEE, VIKOR, TOPSIS, AHP, ANP, MACBETH, DEMATEL, Shoke integraland DEA, their features, applications and the most cited articles are considered. Most real optimizationproblems may have conflicting goals. The method of fuzzy multi-purpose decision-makingFMODM is also presented for situations where there are inaccuracies and uncertainty in somegoals and variables on which they depend; methods of fuzzy multi-purpose linear programmingFMOLP, fuzzy multi-objective target programming FMOGP and fuzzy heuristic decision-makingmethods. The problem of fuzzy multicriteria group decision-making during evacuation with anintuitive relation of linguistic preferences is considered. It is noted that fuzzy logic methods areparticularly suitable for making evacuation decisions when there is little data, knowledge ofcause-and-effect relationships is inaccurate, and observations and criteria can be expressed inlinguistic qualitative terms. The main stages of group making the best decision among alternativesin a fuzzy environment are presented: combining expert assessments; obtaining a final assessmentfor each alternative represented by a linguistic variable; ranking alternatives; group making themost preferred decision. An approach to group decision-making with an intuitive preference relationshipbased on aggregation procedures is proposed. The group model of decision-making andthe concept of fuzzy group decision and linguistic variables used in predicting an emergency situationand planning evacuation are considered. It is noted that the well-known operators of orderedweighted averaging OWA, LOWA do not take into account the weights of experts. The Low operatoris defined, which allows taking into account the weight values of experts, as well as an approachto determining a fuzzy group solution of aFCS as a type 2 set. Algorithms for determininga fuzzy group multicriteria solution based on aFCS are presented

Antibacterial and Photocatalytic Activities of LDH-Based Sorbents of Different Compositions.

Layered double hydroxides (LDHs) play a fundamental role in the processes for the abatement of pollutants in water, with reference to heavy metal decontamination. The research on the topic is multiobjective target oriented, aiming at combining environmental remediation with the possibility of reusing a sorbent as many times as possible, turning it into a renewable resource. In this study, the antibacterial and catalytic properties of a ZnAl-SO4 LDH and its resulting product after being subjected to a Cr(VI) remediation process are compared. Both solid substrates have also been tested after undergoing a thermal annealing process. The sorbent (previously described and tested for remediation) has been investigated for its antibacterial activity in view of further surgery and drug delivery applications. Finally, its photocatalytic properties have been experimentally tested in the degradation of a model pollutant, i.e., Methyl Orange (MO), under solar-simulated light. Identifying the best recycling strategy for these materials requires an accurate knowledge of their physicochemical properties. The results show that both the antimicrobial activity and the photocatalytic performance may considerably improve after thermal annealing.

An Improved Adaptive Coordination Control of Wind Integrated Multi-Terminal HVdc System

The increasing penetration of the renewables and integration of the power electronic devices leads to lower system inertia, which is changeling the system stability of a multiterminal HVdc (MTdc) system. This article presents an improved adaptive predictive control with the multiobjective targets coordinating the key parameters that the dc voltage, ac frequency and power-sharing among the terminals in MTdc system. Specifically, we contribute two main points to the relevant literature, with the purpose of distinguishing our study from existing ones. First, the proposed method is based on minimal information exchange by only considering neighboring terminals. Second, the adaptive control is achieved by setting a weighted fitness function to adaptively tune the weights with the effective integration of the trust-region and particle swarm optimization. A four-terminal HVdc system built within the IEEE 30-bus ac system is used as the study case to validate the robustness and efficiency of the proposed method. In the case study regarding the multiobjective fitness function, the proposed approach benefits in suppressing the voltage deviation, providing frequency support and establishing an automatically updated power equilibrium leveraging by the adaptive parameters.

Target-Oriented Teaching Path Planning with Deep Reinforcement Learning for Cloud Computing-Assisted Instructions

In recent years, individual learning path planning has become prevalent in online learning systems, while few studies have focused on teaching path planning for traditional classroom teaching. This paper proposes a target-oriented teaching path optimization scheme for cloud computing-assisted instructions, in which a sequence of learning contents is arranged to ensure the maximum benefit for a given group of students. First, to evaluate the teaching performance, we investigate various student models and define some teaching objectives, including the pass rate, the excellence rate, the average score, and related constraints. Second, a new Deep Reinforcement Learning (DRL)-based teaching path planning method is proposed to tackle the learning path by maximizing a multi-objective target while satisfying all teaching constraints. It adopts a Proximal Policy Optimization (PPO) framework to find a model-free solution for achieving fast convergence and better optimality. Finally, extensive simulations with a variety of commonly used teaching methods show that our scheme provides nice performance and versatility over commonly used student models.

Evaluation and optimization of various treatment methods for enhancing the properties of brick-concrete recycled coarse aggregate

Crushed brick-concrete recycled aggregate (BCRA) represents a sustainable and environmentally friendly alternative to natural aggregates (NAs) in practical engineering. However, compared with NA, the high porosity and low strength of BCRA means it struggles to meet engineering requirements, which limits its large-scale application. This paper reports upon an experimental study to enhance the quality of BCRA via soaking in different types of solutions (pozzolanic materials, sodium silicate, polyvinyl alcohol, and silicon-based additive). The physical and mechanical properties of treated and untreated BCRA were evaluated by testing the apparent density, water absorption, and crushing value. Meanwhile, the optimization schemes for different treatment techniques were recommended using the multi-objective grey target decision method. In addition, the surface morphology of BCRA was characterized by X-ray diffraction and scanning electron microscopy with energy dispersive spectroscopy, to examine the microstructure of the BCRA surface before and after different treatment processes. The results show that 80% cement, 10% fly ash, and 10% silica fume slurries had notable effects on the apparent density and mechanical strength of BCRA, in which the apparent density increased by 0.6–1.8% and the crushing value decreased by 3.5–19.7%; however, the water absorption of BCRA increased by 1.7–8.5%. The physical and mechanical properties of BCRA increased under an increase of sodium silicate concentration and soaking time, though its strengthening effects were limited. Polyvinyl alcohol and silicon-based additives were best able to improve the waterproofing of BCRA, and the water absorption of the BCRA after treatment was reduced by 5.1–17.8% and 30.5–44.9%, respectively. Clear microstructural improvements can also be observed on the treated BCRA surface. To summarize, the BCRA treated by the four treatment methods exhibited surfaces that were denser, less porous, and more homogeneous and regular. These results will provide a useful reference for the future engineering applications of treated BCRA.

Design Optimization of a Single-Strand Tundish Based on CFD-Taguchi-Grey Relational Analysis Combined Method

A novel digital design methodology that combines computational fluid dynamics (CFD) modelling and Taguchi-Grey relational analysis method was presented for a single-strand tundish. The present study aimed at optimizing the flow control device in the tundish with an emphasis on maximizing the inclusion removal rate and minimizing the dead volume fraction. A CFD model was employed to calculate the fluid flow and the residence-time distribution of liquid steel in the tundish. The Lagrangian approach was applied to investigate the behavior of non-metallic inclusions in the system. The calculated residence-time distribution curves were used to analyze the dead volume fraction in the tundish. A Taguchi orthogonal array L9(3^4) was used to analyze the effects of design factors on both single and multiple responses. Moreover, for the purpose of meeting the multi-objective target functions, grey relational analysis and analysis of variance were used. The optimum positions of the weir and the dam were obtained based on the design targets. A special focus of this study was to demonstrate the capabilities of the Taguchi-Grey relational analysis method as a powerful means of increasing the effectiveness of CFD simulation.

Interactive multiobjective DEA target setting using lexicographic DDF

In this paper, a new interactive multiobjective target setting approach based on lexicographic directional distance function (DDF) method is proposed. Lexicographic DDF computes efficient targets along a specified directional vector. The interactive multiobjective optimization approach consists in several iteration cycles in each of which the Decision Making Unit (DMU) is presented a fixed number of efficient targets computed corresponding to different directional vectors. If the DMU finds one of them promising, the directional vectors tried in the next iteration are generated close to the promising one, thus focusing the exploration of the efficient frontier on the promising area. In any iteration the DMU may choose to finish the exploration of the current region and restart the process to probe a new region. The interactive process ends when the DMU finds its most preferred solution (MPS).

Multi-objective calibration by combination of stochastic and gradient-like parameter generation rules – the caRamel algorithm

Abstract. Environmental modelling is complex, and models often require the calibration of several parameters that are not able to be directly evaluated from a physical quantity or field measurement. Multi-objective calibration has many advantages such as adding constraints in a poorly constrained problem or finding a compromise between different objectives by defining a set of optimal parameters. The caRamel optimizer has been developed to meet the requirement for an automatic calibration procedure that delivers not just one but a family of parameter sets that are optimal with regard to a multi-objective target. The idea behind caRamel is to rely on stochastic rules while also allowing more “local” mechanisms, such as the extrapolation along vectors in the parameter space. The caRamel algorithm is a hybrid of the multi-objective evolutionary annealing simplex (MEAS) method and the non-dominated sorting genetic algorithm II (ε-NSGA-II). It was initially developed for calibrating hydrological models but can be used for any environmental model. The caRamel algorithm is well adapted to complex modelling. The comparison with other optimizers in hydrological case studies (i.e. NSGA-II and MEAS) confirms the quality of the algorithm. An R package, caRamel, has been designed to easily implement this multi-objective algorithm optimizer in the R environment.

Scalable Modeling Approach and Robust Hardware-in-the-Loop Testing of an Optimized Interleaved Bidirectional HV DC/DC Converter for Electric Vehicle Drivetrains

Automotive Original Equipment Manufacturers (OEMs) require varying levels of functionalities and model details at different phases of the electric vehicles (EV) development process, with a trade-off between accuracy and execution time. This article proposes a scalable modelling approach depending on the multi-objective targets between model functionalities, accuracy and execution time. In this article, four different fidelity levels of modelling approaches are described based on the model functionalities, accuracy and execution time. The highest error observed between the low fidelity (LoFi) map-based model and the high fidelity (HiFi) physics-based model is 5.04%; while, the simulation time of the LoFi model is ~10 4 times faster than corresponding one of the HiFi model. A detailed comparison of all characteristics between multi-fidelity models is demonstrated in this paper. Furthermore, a dSPACE SCALEXIO Hardware-in-the-Loop (HiL) testbench, equipped with a minimal latency of 18 μ sec, is used for real-time (RT) model implementation of the EV's HV DC/DC converter. The performance of the entire HiL setup is compared with the Model-in-the-Loop (MiL) setup and the highest RMSE is limited to 0.54 among the HiL and MiL results. Moreover, the accuracy (95.7%) of the passive component loss estimation is verified through the Finite Element Method (FEM) software model. Finally, the experimental results of a full-scale 30-kW SiC DC/DC converter prototype are presented to validate the accuracy and correlation between multi-fidelity models. It has been observed that the efficiency deviation between the hardware prototype and multi-fidelity models is less than 1.25% at full load. Furthermore, the SiC Interleaved Bidirectional Converter (IBC) prototype achieves a high efficiency of 98.4% at rated load condition.

A Multi-Objective Target-Oriented Cooperative MAC Protocol for Wireless Ad-hoc Networks With Energy Harvesting

In this paper, we present a cross layer cooperative medium access control (CMAC) protocol with energy harvesting (EH) capability. The small energy capacity and size of wireless nodes pose a great challenge to the longevity of wireless networks due to the cost of ensuring a reliable communication link between transmitting nodes characterized by path-loss, shadowing and fading effects. Besides, the inability of existing CMAC protocols to exhibit multi-objective target orientation limits their adaptation to the dynamic network requirements. To address this problem, we propose a protocol that harnesses the radio frequency (RF) EH in the physical layer to enhance the throughput, end to end delay, energy efficiency and network lifetime of energy constraint wireless networks. This ensures that beneficial cooperation is achieved in fairness and multi-objective target-oriented protocol. We then investigate the performance of deploying a selective time-switching relaying (TSR) and power-splitting relaying (PSR) schemes in the MAC layer stack for a decode-and-forward (DF) reactive relaying distributed network. In addition, the quality of service requirement, outage probability, and network lifetime optimization techniques, respectively were utilized for optimal power allocation. Also, we propose a distributed and adaptive relay selection algorithm to select the best helper node that improves the network performance and balance the network energy consumption. The results of simulation show that a multi-objective target orientation can be achieved by the proposed EH-CMAC protocol and outperforms EAP-CMAC protocol with respect to throughput, end to end delay, network lifetime and energy efficiency.

Electric Vehicle Inverter Electro-Thermal Models Oriented to Simulation Speed and Accuracy Multi-Objective Targets

With the increasing demand for electric vehicles, the requirements of the market are changing ever faster. Therefore, there is a need to improve the electric car’s design time, where simulations could be an appropriate tool for this task. In this paper, the modeling and simulation of an inverter for an electric vehicle are presented. Four different modeling approaches are proposed, depending on the required simulation speed and accuracy in each case. In addition, these models can provide up to 150 different electric modeling and three different thermal modeling variants. Therefore, in total, there were 450 different electrical and thermal variants. These variants are easily selectable and usable and offer different options to calculate the electrical parameters of the inverter. Finally, the speed and accuracy of the different models were compared and the obtained results presented.

Multi-Objective Online Ride-Matching

We study the following multi-period multi-objective online ride-matching problem. A ride-sourcing platform needs to match passengers and drivers in real time without observing future information, considering multiple objectives such as platform revenue, pick-up distance, and service quality. We develop an efficient online matching policy that adaptively balances the trade-offs among multiple objectives in a dynamic setting, and provide theoretical performance guarantee for the policy. We prove that the proposed adaptive matching policy can achieve a “target-based optimal solution”, i.e., a solution that minimizes the Euclidean distance to any pre-determined multi-objective target. Specifically, the outcome under our policy converges to the “compromise solution” if we set the utopia point as the target. Through numerical experiments and industrial testing using real data from a ride-sourcing platform, we demonstrate that our approach indeed obtains solutions that are closest to the pre-determined targets under various settings, in comparison to existing approaches. The policy presents solutions with delicate balance among multiple objectives and brings value to all the stakeholders in the ride-sourcing ecosystem comparing to benchmark policies: (1) drivers with higher service scores are dispatched with more orders and receive higher incomes; (2) passengers are more likely to be served by drivers with higher service scores, and passengers with higher order revenues are served with higher answer rates, at the expense of a small increase in pick-up distance; (3) the platform obtains a higher total revenue.

Evaluation and optimization of bridge deck waterproof bonding system using multi-objective grey target decision method

Surfacing of steel decks is one of the crucial issues which engineers are facing during construction of long-span steel bridges. This study aims to optimise the overall performance of the bridge deck waterproof bonding system. The waterproof bonding system used in this study consisted of two layers, a waterproof layer and a bonding layer. The role of the bonding is to ensure that the deck pavement and the steel panel can form a monolithic structure. The waterproof layer mainly prevents moisture from penetrating into the bridge deck. A series of laboratory experiments were conducted to evaluate the bonding performance of the waterproof adhesive system. Different types of waterproof adhesive systems were considered in the experimental design. Five performance indicators such as tensile strength, direct shear strength, skew shear strength, fatigue life, and project cost, were incorporated for evaluation. Then, the multi-objective grey target decision approach was employed as an optimised method to recommend a suitable waterproof adhesive system. Results showed that the temperature and performance indicators had a significant influence on the ranking of varying waterproof bonding system types. Findings of this research could provide the technical support for the future design of bridge deck waterproof adhesive systems.

A Network Lifetime Extension-Aware Cooperative MAC Protocol for MANETs With Optimized Power Control

In this paper, a cooperative medium access control (CMAC) protocol, termed network lifetime extension-aware CMAC (LEA-CMAC) for mobile ad-hoc networks (MANETs) is proposed. The main feature of the LEA-CMAC protocol is to enhance the network performance through the cooperative transmission to achieve a multi-objective target orientation. The unpredictable nature of wireless communication links results in the degradation of network performance in terms of throughput, end-to-end delay, energy efficiency, and network lifetime of MANETs. Through cooperative transmission, the network performance of MANETs can be improved, provided a beneficial cooperation is satisfied and design parameters are carefully selected at the MAC layer. To achieve a multi-objective target-oriented CMAC protocol, we formulated an optimization problem to extend the network lifetime of MANETs. The optimization solution led to the investigation of symmetric and asymmetric transmit power policies. We then proposed a distributed relay selection process to select the best retransmitting node among the qualified relays, with consideration on a transmit power, a sufficient residual energy after cooperation, and a high cooperative gain. The simulation results show that the LEA-CMAC protocol can achieve a multi-objective target orientation by exploiting an asymmetric transmit power policy to improve the network performance.

Multi-Objective Weapon Target Assignment Based on D-NSGA-III-A

The multi-objective weapon-target assignment problem, which aims to generate reasonable assignment to meet the objectives, is a typical optimization problem with complex constraints. In order to get close to the actual air combat, the game process between both sides at war is introduced to construct a three objective mathematical model, which includes the damage of the enemy, the cost of missiles, and the damage value of fighting capacity. Considering the NP-complete nature of multi-objective weapon-target assignment problem, an improved intelligent algorithm (named as D-NSGA-III-A) on the basis of non-dominated sorting genetic algorithm III (NSGA-III) is proposed. In this improved algorithm, first, the non-dominated sorting based on dominance degree matrix is proposed to reduce the unnecessary or repetitive comparisons in ranking schemes, so as to further decrease the time consumption. Second, diversity and convergence are taken into account resorting to the niching information and the dominance ratio when selecting individuals. Third, the adaptive operator selection mechanism, which selects the operators adaptively according to the information of generations from a pool where single point crossover and all bits crossover operators are included, is employed to seek a balance between intensification and diversification within the decision space and to improve the quality of Pareto solutions. From the experiments, the combination of above technologies obtains better Pareto solutions and time performance for solving the static multi-objective target assignment (SMWTA) problem than NSGA-III, MP-ACO, NSGA-II, MOPSO, MOEA/D, and DMOEA-eC.

Multi-objective target oriented robust optimization for the design of an integrated biorefinery

Integrated biorefineries offer an efficient way of producing biofuels while increasing economic potential through the production of valuable co-products. Such bioenergy systems can potentially reduce environmental footprints by reusing waste streams from other processes within the system. An integrated biorefinery utilizes multiple conversion technologies to produce an array of products, such as biofuels, biochemicals, and bioenergy, from biomass feedstocks. Methodologies such as mathematical programming models have been instrumental in the optimal synthesis and design of such complex systems. However, uncertainties in model parameters may result in economic losses or increased environmental emissions during actual operations. Uncertainties may be accounted for in the design procedure of an integrated biorefinery through target-oriented robust optimization methodology. This new approach identifies the optimal design of an integrated biorefinery by maximizing a robustness index against uncertainties. However, since practical engineering problems often involve satisfying multiple targets, a multi-objective target-oriented robust optimization approach is developed in this paper. This new approach allows the advantages of target-oriented robust optimization to be realized for multi-objective problems. A case study on the design of an integrated algal biorefinery, with both economic performance and environmental footprint considerations, is used to illustrate the methodology. Results of Monte Carlo simulation show that the robust optimal configuration of the integrated biorefinery is relatively immune to parametric uncertainties compared to the nominal optimum found using deterministic models.

On the Implementation of Advanced Hybrid Controllers of AC/DC Converters

The implementation of hybrid controllers in power electronics is specially challenging due to the fast processing and measurement requirements of the controller to achieve good performance. In this paper, an advanced hybrid control for a class of rectifiers is developed, which is capable to regulate the output voltage, to control the active/reactive power as well as to control the ripple of the input current. The proposed controller besides of achieving a multi-objective control target, it is implemented through the use of analog circuitry, which reduces the computation burden and the use of complex and expensive devices such as FPGAs and DSPs. The performance evaluation of the proposed controller gives evidence of the suitability of analog circuitry to implement such complex controllers which its digital implementation is usually characterized by multi-period processing and delays.

Method for measuring target depth by using optical field focusing image

Depth information of the scene is the key to the three-dimensional (3D) reconstruction. Traditional depth mea- surement methods are based on stereo vision or defocusing image measurement, which have bad real-time performance, high computational complexity and cost much. Optical field is a new technology that can record the 3D information without the focusing process. Using optical field refocusing can rebuild scene at any depth through a single image, as for complex scene using window scanning to get depth information of different position. When set the size of window to 1 pixel can get the depth value pixel by pixel from gradient operator. Finally gain a high-resolution and high-accuracy depth map. Optical field technol- ogy applying to the depth measurement is equivalent to an improved focusing distance measuring method. The new method is simple, has high precision and strong real-time performance to apply to multi-objective and non-cooperative targets.

A model-based adaptive controller for chatter mitigation and productivity enhancement in CNC milling machines

Seeking a higher level of automation, according to Intelligent Manufacturing paradigm, an optimal process control for milling process has been developed, aiming at optimizing a multi-objective target function defined in order to mitigate vibration level and surface quality, while preserving production times and decreasing tool wear rate. The control architecture relies on a real-time process model able to capture the most significant phenomena ongoing during the machining, such as cutting forces and tool vibration (both forced and self-excited). For a given tool path and workpiece material, an optimal sequence of feedrate and spindle speed is calculated both for the initial setup of the machining process and for the continuous, in-process adaptation of process parameters to changes the current machining behavior. For the first time in the literature, following a Model-Predictive-Control (MPC) approach, the controller is able to adapt its actions taking into account process and axes dynamics on the basis of Optimal Control theory. The developed controller has been implemented in a commercial CNC of a 3-axes milling machine manufactured by Alesamonti; the effectiveness of the approach is demonstrated on a real industrial application and the performance enhancement is evaluated and discussed. HighlightsModel-based controller for milling process based on Optimal Control Theory.Multi-objective optimization: vibrations, tool deflection, tool wear and productivity.Real-time model of cutting process based on computation of tool-workpiece engagement.Implementation and testing on a commercial milling machine by Alesamonti.

An analysis of the power market in Saudi Arabia: Retrospective cost and environmental optimization

This paper provides an overview of the power sector in the Kingdom of Saudi Arabia (KSA) with the latest data and applies a retrospective modeling approach for its power market under (i) a cost minimization and (ii) an environmental impact minimization objective for the period 2003–2013 with domestic and international fuel prices. For the analysis an enhanced version of Open Source energy MOdelling SYStem (OSeMOSYS) is utilized. Implemented model improvements include (a) temperature adjustments as the temperature difference between winter and summer of 20–30°C is common almost all over the world and has significant impact on power generation efficiency, (b) a multi-objective target function considering both costs and emissions to create a Pareto frontier, and (c) emissions are assessed with the ‘quality enhanced life days’ (QELD) indicator to consider global (e.g. carbon dioxide) and local emissions (e.g. NOx) simultaneously. Results show that the biggest reductions in emissions can be reached by considering international fuel prices. While the amount of new technology is quite similar within the minimize emission cases, the installed type of technology differs significantly within the minimize cost cases: cost minimization with international fuel prices is forcing Combined Cycle Gas Turbine (CCGT or CC) plants and Steam Turbine (ST) plants whereas when considering domestic fuel prices, the share of inefficient Open Cycle Gas Turbine (OCGT or GT) plants is 100%. Focusing on emission reduction within the international fuel price scenario a 19% cost increase results in 43% emission reductions while a 33% cost increase reduces emissions by 60%. Notably, an emission reduction of 50% can be reached independently of fuel pricing when the weight of emission criterion is 95% or higher within the enhanced OSeMOSYS model.