Multi-objective optimization and evaluation of supercritical CO_(2) Brayton cycle for nuclear power generation

The supercritical CO_(2) Brayton cycle is considered a promising energy conversion system for Generation IV reactors for its simple layout,compact structure,and high cycle efficiency.Mathematical models of four Brayton cycle layouts are developed in this study for different reactors to reduce the cost and increase the thermohydraulic performance of nuclear power generation to promote the commercialization of nuclear energy.Parametric analysis,multi-objective optimizations,and four decision-making methods are applied to obtain each Brayton scheme’s optimal thermohydraulic and economic indexes.Results show that for the same design thermal power scale of reactors,the higher the core’s exit temperature,the better the Brayton cycle’s thermo-economic performance.Among the four-cycle layouts,the recompression cycle(RC)has the best overall performance,followed by the simple recuperation cycle(SR)and the intercooling cycle(IC),and the worst is the reheating cycle(RH).However,RH has the lowest total cost of investment(C_(tot))of$1619.85 million,and IC has the lowest levelized cost of energy(LCOE)of 0.012$/(kWh).The nuclear Brayton cycle system’s overall performance has been improved due to optimization.The performance of the molten salt reactor combined with the intercooling cycle(MSR-IC)scheme has the greatest improvement,with the net output power(W_(net)),thermal efficiencyη_(t),and exergy efficiency(η_(e))improved by 8.58%,8.58%,and 11.21%,respectively.The performance of the lead-cooled fast reactor combined with the simple recuperation cycle scheme was optimized to increase C_(tot) by 27.78%.In comparison,the internal rate of return(IRR)increased by only 7.8%,which is not friendly to investors with limited funds.For the nuclear Brayton cycle,the molten salt reactor combined with the recompression cycle scheme should receive priority,and the gas-cooled fast reactor combined with the reheating cycle scheme should be considered carefully.

Interactive Multi-objective Optimization Design for the Pylon Structure of an Airplane

The pylon structure of an airplane is very complex, and its high-fidelity analysis is quite time-consuming. If posterior preference optimization algorithm is used to solve this problem, the huge time consumption will be unacceptable in engineering practice due to the large amount of evaluation needed for the algorithm. So, a new interactive optimization algorithm-interactive multi-objective particle swarm optimization （IMOPSO） is presented. IMOPSO is efficient, simple and operable. The decision-maker can expediently determine the accurate preference in IMOPSO. IMOPSO is used to perform the pylon structure optimization design of an airplane, and a satisfactory design is achieved after only 12 generations of IMOPSO evolutions. Compared with original design, the maximum displacement of the satisfactory design is reduced, and the mass of the satisfactory design is decreased for 22%.

Multi-Objective Optimal Dispatch Considering Wind Power and Interactive Load for Power System

With the rapid and large-scale development of renewable energy, the lack of new energy power transportation or consumption, and the shortage of grid peak-shifting ability have become increasingly serious. Aiming to the severe wind power curtailment issue, the characteristics of interactive load are studied upon the traditional day-ahead dispatch model to mitigate the influence of wind power fluctuation. A multi-objective optimal dispatch model with the minimum operating cost and power losses is built. Optimal power flow distribution is available when both generation and demand side participate in the resource allocation. The quantum particle swarm optimization (QPSO) algorithm is applied to convert multi-objective optimization problem into single objective optimization problem. The simulation results of IEEE 30-bus system verify that the proposed method can effectively reduce the operating cost and grid loss simultaneously enhancing the consumption of wind power.

Improved Fuzzification Method for Multi-Objective Decision-Making and Its Application in Evaluation of Highway Planning

A new fuzzification method for multi-objective decision-making and selective sorting is proposed on the basis of the fuzzy consistent relation, and the specific algorithm is presented. The method is applied to the evaluation of highway planning of Zhanjiang city. To decrease the subjectivity in the process of decision-making, the LOWA operator is introduced, and a discussion on how to select appropriate weights involved in multi-objective sorting is made. It is concluded that it is feasible to apply the fuzzy consistent relation to multi-objective decision-making analysis, and the improved fuzzication method is workable.

Decision-Making in Driver-Automation Shared Control:A Review and Perspectives

Shared control schemes allow a human driver to work with an automated driving agent in driver-vehicle systems while retaining the driver’s abilities to control.The human driver,as an essential agent in the driver-vehicle shared control systems,should be precisely modeled regarding their cognitive processes,control strategies,and decision-making processes.The interactive strategy design between drivers and automated driving agents brings an excellent challenge for human-centric driver assistance systems due to the inherent characteristics of humans.Many open-ended questions arise,such as what proper role of human drivers should act in a shared control scheme?How to make an intelligent decision capable of balancing the benefits of agents in shared control systems?Due to the advent of these attentions and questions,it is desirable to present a survey on the decision making between human drivers and highly automated vehicles,to understand their architectures,human driver modeling,and interaction strategies under the driver-vehicle shared schemes.Finally,we give a further discussion on the key future challenges and opportunities.They are likely to shape new potential research directions.

Observation-Oriented Causal Discovery for Cultivation Abandonment of Rice Terraces: Focusing on an Effect of Cultural Endemism on Decision-Making in Toraja, Indonesia

Previous discussion about the factors of the expanding trend of abandoned cultivation had focused only on universal factors and lacked evaluation of the regionality of the phenomenon. This paper demonstrated the Toraja’s regional characteristics and the influence of cultural endemism on decision-making about abandoning cultivation by an observation-oriented approach. Based on a causal framework constructed by field observation and geospatial data generation, an adjustment for overt covariates using the multivariate logistic regression model to draw the causal effect from hidden covariates was examined in two rice terraces with different water systems, i.e. irrigated field and rain-fed field. The result of sub-group analysis revealed that decisions about abandoning cultivation in Toraja were greatly associated with disadvantageous factors for intensive farming, i.e. “number of adjacent fields” and “soil erosion” rather than advantageous factors, i.e. “area of field” and “distance to roads”. Moreover, the result of interaction analysis which controlled the effect of topography revealed the powerful effect of particular decision factors only in rain-fed rice terrace: the “distance to roads” factor’s fairly negative contribution on abandoning cultivation (Odds ratio = 9.94E - 01, P value = 2.03E - 11), as well as the “number of adjacent field” factor’s positive contribution on abandoning cultivation (Odds ratio = 1.13E+00, P value = 3.65E - 04). Given the evidence from the explanation of these results by customary laws and land inheritance system for each site, therefore, it could be concluded that the screening and detection of cultural endemism’s influence was achieved using the algorithm this paper proposes.

Game Interactive Learning:A New Paradigm towards Intelligent Decision-Making

Decision-making plays an essential role in various real-world systems like automatic driving,traffic dispatching,information system management,and emergency command and control.Recent breakthroughs in computer game scenarios using deep reinforcement learning for intelligent decision-making have paved decision-making intelligence as a burgeoning research direction.In complex practical systems,however,factors like coupled distracting features,long-term interact links,and adversarial environments and opponents,make decision-making in practical applications challenging in modeling,computing,and explaining.This work proposes game interactive learning,a novel paradigm as a new approach towards intelligent decision-making in complex and adversarial environments.This novel paradigm highlights the function and role of a human in the process of intelligent decision-making in complex systems.It formalizes a new learning paradigm for exchanging information and knowledge between humans and the machine system.The proposed paradigm first inherits methods in game theory to model the agents and their preferences in the complex decision-making process.It then optimizes the learning objectives from equilibrium analysis using reformed machine learning algorithms to compute and pursue promising decision results for practice.Human interactions are involved when the learning process needs guidance from additional knowledge and instructions,or the human wants to understand the learning machine better.We perform preliminary experimental verification of the proposed paradigm on two challenging decision-making tasks in tactical-level War-game scenarios.Experimental results demonstrate the effectiveness of the proposed learning paradigm.

Challenges of human-machine collaboration in risky decision-making

The purpose of this paper is to delineate the research challenges of human-machine collaboration in risky decision-making.Technological advances in machine intelligence have enabled a growing number of applications in human-machine collaborative decisionmaking.Therefore,it is desirable to achieve superior performance by folly leveraging human and machine capabilities.In risky decision-making,a human decisionmaker is vulnerable to cognitive biases when judging the possible outcomes of a risky event,whereas a machine decision-maker cannot handle new and dynamic contexts with incomplete information well.We first summarize features of risky decision-making and possible biases of human decision-makers therein.Then,we argue the necessity and urgency of advancing human-machine collaboration in risky decision-making.Afterward,we review the literature on human-machine collaboration in a general decision context,from the perspectives of human-machine organization,relationship,and collaboration.Lastly,we propose challenges of enhancing human-machine communication and teamwork in risky decisionmaking,followed by future research avenues.

用于发酵过程多目标优化的几何支持向量回归Pareto前沿的连续近似方法（英文）

The approaches to discrete approximation of Pareto front using multi-objective evolutionary algorithms have the problems of heavy computation burden, long running time and missing Pareto optimal points. In order to overcome these problems, an approach to continuous approximation of Pareto front using geometric support vector regression is presented. The regression model of the small size approximate discrete Pareto front is constructed by geometric support vector regression modeling and is described as the approximate continuous Pareto front. In the process of geometric support vector regression modeling, considering the distribution characteristic of Pareto optimal points, the separable augmented training sample sets are constructed by shifting original training sample points along multiple coordinated axes. Besides, an interactive decision-making(DM)procedure, in which the continuous approximation of Pareto front and decision-making is performed interactively, is designed for improving the accuracy of the preferred Pareto optimal point. The correctness of the continuous approximation of Pareto front is demonstrated with a typical multi-objective optimization problem. In addition,combined with the interactive decision-making procedure, the continuous approximation of Pareto front is applied in the multi-objective optimization for an industrial fed-batch yeast fermentation process. The experimental results show that the generated approximate continuous Pareto front has good accuracy and completeness. Compared with the multi-objective evolutionary algorithm with large size population, a more accurate preferred Pareto optimal point can be obtained from the approximate continuous Pareto front with less computation and shorter running time. The operation strategy corresponding to the final preferred Pareto optimal point generated by the interactive DM procedure can improve the production indexes of the fermentation process effectively.

Research on the Infuence of Multiple Parameters on the Responses of a B-type Subway Train

To obtain improved comprehensive crashworthiness criteria for a B-type subway train,the infuence laws of the vehicle design collision weight M and empty stroke D on the train’s collision responses were investigated,and multiobjective optimization and decision-making were performed to minimize TS(total compression displacement along the moving train)and TAMA(the overall mean acceleration along the moving train).Firstly,a one-dimensional train collision dynamics model was established and verifed by comparing with the results of the fnite element model.Secondly,based on the dynamics model,the infuence laws of M and D on the collision responses,such as the energy-absorbing devices’displacements and absorbed energy,vehicles’velocity and acceleration,TS,TAMA and the coupling correlation efect were investigated.Then,surrogate models for TS and TAMA were developed using the optimal Latin hypercube method(OLHD)and response surface method(RSM),and multi-objective optimization was conducted using the particle swarm optimization algorithm method(MPOSO).Finally,the entropy method was used to obtain the weight coefcients for TS and TAMA,and multi-objective decision-making was performed.The results indicate that D and M signifcantly afect the compression displacements and energy absorption of the frst three collision interfaces,but have limited impact on the last three collision interfaces.The velocity versus time curves of vehicle M1 and M2 are shifted and parallel with diferent D.However,the velocity versus time curves of all the vehicles are shifted but gradually divergent with diferent M.The maximum collision instantaneous accelerations of the vehicles are directly determined by M,but are only slightly afected by D.Under the coupling efect,all concerned collision responses are strongly correlated with M;however,the responses are weakly correlated with D except for the compression displacement at the M2-M3 collision interface and the maximum collision instantaneous acceleration of vehicle M2.The comprehensive crashworthiness criteria of the B-type subway train were signifcantly improved after multi-objective optimization and decision-making.The research provides more theoretical and engineering application references for the subway train crashworthiness design.

Path Planning of Quadrotors in a Dynamic Environment Using a Multicriteria Multi-Verse Optimizer

Paths planning of Unmanned Aerial Vehicles(UAVs)in a dynamic environment is considered a challenging task in autonomous flight control design.In this work,an efficient method based on a Multi-Objective MultiVerse Optimization(MOMVO)algorithm is proposed and successfully applied to solve the path planning problem of quadrotors with moving obstacles.Such a path planning task is formulated as a multicriteria optimization problem under operational constraints.The proposed MOMVO-based planning approach aims to lead the drone to traverse the shortest path from the starting point and the target without collision with moving obstacles.The vehicle moves to the next position from its current one such that the line joining minimizes the total path length and allows aligning its direction towards the goal.To choose the best compromise solution among all the non-dominated Pareto ones obtained for compromise objectives,the modified Technique for Order Preference by Similarity to Ideal Solution(TOPSIS)is investigated.A set of homologous metaheuristics such as Multiobjective Salp Swarm Algorithm(MSSA),Multi-Objective Grey Wolf Optimizer(MOGWO),Multi-Objective Particle Swarm Optimization(MOPSO),and Non-Dominated Genetic Algorithm II(NSGAII)is used as a basis for the performance comparison.Demonstrative results and statistical analyses show the superiority and effectiveness of the proposed MOMVO-based planning method.The obtained results are satisfactory and encouraging for future practical implementation of the path planning strategy.

Closed-loop supply chain network equilibrium strategy model with environmental protection objectives

With the increasing popularity of ecological civilization and sustainable development,enterprises should consider environmental protection measures in their operations in addition to pursue their economic interests.This paper establsihes a closed-loop supply chain network model composed of multiple suppliers,manufacturers,retailers,recyclers,and demand markets—regarding their dual goals of the profit maximization and the minimization of carbon emissions.The conditions necessary for establishing overall equilibrium and an equilibrium model of the entire closed-loop supply chain network are determined by applying variational inequality and dual theory.A modified projection contraction algorithm is used to design a model-solving program.Finally,using numerical examples,the paper conducts a comparative static analysis on important parameters such as the weight coefficients of environmental protection objectives and consumers'awareness of low-carbon environmental protection and attains some beneficial enlightenment on management.The results indicate that when the environmental protection objectives of a certain type of enterprise increases,both the economic benefits and environmental protection performance will improve;when the environmental protection objectives of all enterprises increases simultaneously,environmental protection performance improves significantly,but the changes in economic benefits of different enterprises are inconsistent and profit coordination is more complex.Although consumers’awareness of low-carbon preference could improve environmental performance,it reduces the overall profits of network members and the entire closed-loop supply chain network as a whole.The above conclusions can be used as a reference for the government in designing low-carbon environmental protection policy and in closed-loop supply chain research.

Conjunctive Use of Engineering and Optimization in Water Distribution System Design

Water Distribution Systems (WDSs) design and operation are usually done on a case-by-case basis. Numerous models have been proposed in the literature to solve specific problems in this field. The implementation of these models to any real-world WDS optimization problem is left to the discretion of designers who lack the necessary tools that will guide them in the decision-making process for a given WDS design project. Practitioners are not always very familiar with optimization applied to water network design. This results in a quasi-exclusive use of engineering judgment when dealing with this issue. In order to support a decision process in this field, the present article suggests a step-by-step approach to solve the multi-objective design problem by using both engineering and optimization. A genetic algorithm is proposed as the optimization tool and the targeted objectives are: 1) to minimize the total cost (capital and operation), 2) to minimize the residence time of the water within the system and 3) to maximize a network reliability metric. The results of the case study show that preliminary analysis can significantly reduce decision variables and computational burden. Therefore, the approach will help network design practitioners to reduce optimization problems to a more manageable size.

DIP-MOEA:a double-grid interactive preference based multi-ob jective evolutionary algorithm for formalizing preferences of decision makers

The final solution set given by almost all existing preference-based multi-objective evolutionary algorithms(MOEAs)lies a certain distance away from the decision makers’preference information region.Therefore,we propose a multi-ob jective optimization algorithm,referred to as the double-grid interactive preference based MOEA(DIPMOEA),which explicitly takes the preferences of decision makers(DMs)into account.First,according to the optimization ob jective of the practical multi-ob jective optimization problems and the preferences of DMs,the membership functions are mapped to generate a decision preference grid and a preference error grid.Then,we put forward two dominant modes of population,preference degree dominance and preference error dominance,and use this advantageous scheme to update the population in these two grids.Finally,the populations in these two grids are combined with the DMs’preference interaction information,and the preference multi-ob jective optimization interaction is performed.To verify the performance of DIP-MOEA,we test it on two kinds of problems,i.e.,the basic DTLZ series functions and the multi-ob jective knapsack problems,and compare it with several different popular preference-based MOEAs.Experimental results show that DIP-MOEA expresses the preference information of DMs well and provides a solution set that meets the preferences of DMs,quickly provides the test results,and has better performance in the distribution of the Pareto front solution set.

Mixed Least Square Method for Priority of Complementary Judgement Matrix and Its Algorithm

Based on the concept of multiplicative fuzzy consistent complementary judgement matrix, the mixed least square method （MLSM） for priority of complementary judgement matrix is proposed and proved. Then, the corresponding convergent iterative algorithm is given and its convergence is proved. Finally, some main properties of the developed priority method, such as rank preservation under strong condition, etc., ate introduced. The theoretical analyses show that the MLSM can sufficiently reflect the preference information of the decision maker, and is easy to realize on a computer.

Optimization of QoS Parameters in Cognitive Radio Using Combination of Two Crossover Methods in Genetic Algorithm

Radio Cognitive (RC) is the new concept introduced to improve spectrum utilization in wireless communication and present important research field to resolve the spectrum scarcity problem. The powerful ability of CR to change and adapt its transmit parameters according to environmental sensed parameters, makes CR as the leading technology to manage spectrum allocation and respond to QoS provisioning. In this paper, we assume that the radio environment has been sensed and that the SU specifies QoS requirements of the wireless application. We use genetic algorithm (GA) and propose crossover method called Combined Single-Heuristic Crossover. The weighted sum multi-objective approach is used to combine performance objectives functions discussed in this paper and BER approximate formula is considered.

Predicting Hurricane Evacuation Decisions with Interpretable Machine Learning Methods

Facing the escalating effects of climate change,it is critical to improve the prediction and understanding of the hurricane evacuation decisions made by households in order to enhance emergency management.Current studies in this area often have relied on psychology-driven linear models,which frequently exhibited limitations in practice.The present study proposed a novel interpretable machine learning approach to predict household-level evacuation decisions by leveraging easily accessible demographic and resource-related predictors,compared to existing models that mainly rely on psychological factors.An enhanced logistic regression model(that is,an interpretable machine learning approach) was developed for accurate predictions by automatically accounting for nonlinearities and interactions(that is,univariate and bivariate threshold effects).Specifically,nonlinearity and interaction detection were enabled by low-depth decision trees,which offer transparent model structure and robustness.A survey dataset collected in the aftermath of Hurricanes Katrina and Rita,two of the most intense tropical storms of the last two decades,was employed to test the new methodology.The findings show that,when predicting the households’ evacuation decisions,the enhanced logistic regression model outperformed previous linear models in terms of both model fit and predictive capability.This outcome suggests that our proposed methodology could provide a new tool and framework for emergency management authorities to improve the prediction of evacuation traffic demands in a timely and accurate manner.

Significance of Live Streaming in Shaping Business: A Critical Review and Analytical Study

With the rise of live streaming on social media, platforms like Facebook, Instagram, and YouTube have become powerful business tools. They enable users to share live videos, fostering direct connections between businesses and their customers. This critical literature review paper explores the impact of live streaming on businesses, focusing on its role in attracting and satisfying consumers by promoting products tailored to their needs and wants. It emphasizes live streaming’s crucial role in engaging customers, a key to business growth. The study also provides viable strategies for businesses to leverage live streaming for growth and customer engagement, underscoring its importance in the business landscape.

Optimal microgrid planning for enhancing ancillary service provision

Microgrids have presented themselves as an effective concept to guarantee a reliable,efficient and sustainable electricity delivery during the current transition era from passive to active distribution networks.Moreover,microgrids could offer effective ancillary services(AS)to the power utility,although this will not be possible before the traditional planning and operation methodologies are updated.Hence,a probabilistic multi-objective microgrid planning(POMMP)methodology is proposed in this paper to contemplate the large number of variables,multiple objectives,and different constraints and uncertainties involved in the microgrid planning.The planning methodology is based on the optimal size and location of energy distributed resources with the goal of minimizing the mismatch power in islanded mode,while the residual power for AS provision and the investment and operation costs of the microgrid in grid-connected mode are maximized and minimized,respectively.For that purpose,probabilistic models and a true multi-objective optimization problem are implemented in the methodology.The methodology is tested in an adapted PG&E 69-bus distribution system and the non-dominated sorting genetic algorithmⅡ(NSGA-Ⅱ)optimization method and an analytic hierarchy process for decision-making are used to solve the optimization problem.

Multi-verse optimizer based parameters decision with considering tool life in dry hobbing process

Dry hobbing has received extensive attention for its environmentally friendly processing pattern.Due to the absence of lubricants,hobbing process is highly dependent on process parameters combination since using unreasonable parameters tends to affect the machining performance.Besides,the consideration of tool life is frequently ignored in gear hobbing.Thus,to settle the above issues,a multiobjective parameters decision approach considering tool life is developed.Firstly,detailed quantitative analysis between process parameters and hobbing performance,i.e.,machining time,production cost and tool life is introduced.Secondly,a multi-objective parameters decision-making model is constructed in search for optimum cutting parameters(cutting velocity v,axial feed rate f_(a))and hob parameters(hob diameter d_(0),threads z_(0)).Thirdly,a novel algorithm named multi-objective multi-verse optimizer(MOMVO)is utilized to solve the presented model.A case study is exhibited to show the feasibility and reliability of the proposed approach.The results reveal that(i)a balance can be achieved among machining time,production cost and tool life via appropriate process parameters determination;(ii)optimizing cutting parameters and hob parameters simultaneously contributes to optimal objectives;(iii)considering tool life provides usage precautions support and process parameters guidance for practical machining.