Multi-objective Optimization Theory Research Articles

Research on Multi-Objective Real-Time Optimization of Automatic Train Operation (ATO) in Urban Rail Transit

The determination and optimization of Automatic Train Operation (ATO) control strategy is one of the most critical technologies for urban rail train operation. The practical ATO optimal control strategy must consider many goals of the train operation, such as safety, accuracy, comfort, energy saving and so on. This paper designs a set of efficient and universal multi-objective control strategy. Firstly, based on the analysis of urban rail transit and its operating environment, the multi-objective optimization model considering all the indexes of train operation is established by using multi-objective optimization theory. Secondly, Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used to solve the model, and the optimal speed curve of train running is generated. Finally, the intelligent controller is designed by the combination of fuzzy controller algorithm and the predictive control algorithm, which can control and optimize the train operation in real time. Then the robustness of the control system can ensure and the requirements for multi-objective in train operation can be satisfied.

Multi-Objective Resource Allocation in Density-Aware Design of C-RAN in 5G

In this paper, a multi-objective resource allocation algorithm in a novel density-aware design of virtualized software-defined cloud radio access network (C-RAN) is proposed. We consider two design modes based on the average density of users: 1) high-density mode when a large number of low-cost remote radio heads (RRHs) without baseband processing capability are controlled by one single base station and 2) low-density mode when a small number of RRHs with baseband processing capability are deployed. In high-density mode, the challenge of front-haul capacity limitation is tackled via separating control plane and data plane in a heterogeneous structure. Besides, the fully centralized processing and management, and energy-efficient use of infrastructure in low traffic time by turning off RRHs are achieved. In the low-density mode, the transmission delay due to the large distance between the sparse RRHs and cloud unit, is more critical. This practical issue is handled by sharing the baseband processing and resource management among these units in a hierarchical structure. This resulting heterogeneous /hierarchical virtualized software-defined cloud-RAN (HVSD-CRAN) offers various tradeoffs in resource management objectives such as throughput and delay versus power and cost. Consequently, we resort to multi-objective optimization theory to propose a resource allocation framework in HVSD-CRAN.

Online Optimal Switching Frequency Selection for Grid-Connected Voltage Source Inverters

Enhancing the performance of the voltage source inverters (VSIs) without changing the hardware structure has recently acquired an increased amount of interest. In this study, an optimization algorithm, enhancing the quality of the output power and the efficiency of three-phase grid connected VSIs is proposed. Towards that end, the proposed algorithm varies the switching frequency (fsw) to maintain the best balance between switching losses of the insulated-gate-bipolar-transistor (IGBT) power module as well as the output power quality under all loading conditions, including the ambient temperature effect. Since there is a contradiction with these two measures in relation to the switching frequency, the theory of multi-objective optimization is employed. The proposed algorithm is executed on the platform of Altera® DE2-115 field-programmable-gate-array (FPGA) in which the optimal value of the switching frequency is determined online without the need for heavy offline calculations and/or lookup tables. With adopting the proposed algorithm, there is an improvement in the VSI efficiency without degrading the output power quality. Therefore, the proposed algorithm enhances the lifetime of the IGBT power module because of reduced variations in the module’s junction temperature. An experimental prototype is built, and experimental tests are conducted for the verification of the viability of the proposed algorithm.

A novel first–order reliability method based on performance measure approach for highly nonlinear problems

The first–order reliability method (FORM) is widely used in structural reliability analysis for its simplicity and efficiency. It can be solved by gradient–based algorithms, on which the nonlinear degree of performance functions may have a great influence. On the other hand, evolutionary algorithms could achieve convergence solutions even for highly nonlinear performance function, usually with expensive computational cost. To overcome their drawbacks, we propose a new reliability analysis method called PMA–IACC to search the most probable failure point (MPP). As the inverse reliability analysis and reliability analysis are reversible each other, the performance measure approach (PMA) of the inverse reliability analysis could be used for the reliability analysis based on the multi–objective optimization theory. To enhance the efficiency and robustness of the inverse reliability, the improved adaptive chaos control (IACC) method is proposed and then it is integrated into the PMA reliability analysis strategy. Five illustrative examples, including three two–dimensional problems and two multi–dimensional problems, demonstrate the outstanding efficiency and robustness of the PMA–IACC over other prevalent approaches.

The Unreasonable Destructiveness of Political Correctness in Philosophy

I submit that epistemic progress in key areas of contemporary academic philosophy has been compromised by politically correct (“PC”) ideology. First, guided by an evolutionary account of ideology, results from social and cognitive psychology and formal philosophical methods, I expose evidence for political bias in contemporary Western academia and sketch a formalization for the contents of beliefs from the PC worldview taken to be of core importance, the theory of social oppression and the thesis of anthropological mental egalitarianism. Then, aided by discussions from contemporary epistemology on epistemic values, I model the problem of epistemic appraisal using the frameworks of multi-objective optimization theory and multi-criteria decision analysis and apply it to politically correct philosophy. I conclude that philosophy guided by politically correct values is bound to produce constructs that are less truth-conducive and that spurious values which are ideologically motivated should be abandoned. Objections to my framework stemming from contextual empiricism, the feminine voice in ethics and political philosophy are considered. I conclude by prescribing the epistemic value of epistemic adequacy, the contextual value of political diversity and the moral virtue of moral courage to reverse unwarranted trends in academic philosophy due to PC ideology.

Multi-Objective Optimization for Distributed MIMO Networks

In this paper, we investigate the linear transceiver optimization for multiple-inputmultiple-output (MIMO) interference networks, where multiple pairs of multi-antenna source and destination nodes communicate simultaneously. Different from most of existing works, we jointly consider three critical issues of the linear transceiver optimization for MIMO interference networks based on multi-objective optimization theory, i.e., signal transmission, energy and security. Specifically, using the modified weighted Tchebycheff method, we investigate three kinds of multi-objective optimization problems (MOOPs): 1) sum mean square error minimization and harvested energy maximization; 2) transmit power minimization and energy harvesting efficiency maximization; 3) transmit power minimization, energy harvesting efficiency maximization, and physical layer security. Based on the Charnes–Cooper transformation and penalty function method, the formulated MOOPs are transformed into convex optimization problems and thus can be effectively solved. The resulting Pareto optimal solutions set reveals the complicated but important relationships among these involved single objective optimization problems, which are usually individually investigated in the literature. Finally, numerical simulation results demonstrate the performance advantages of the proposed algorithm and corroborate the theoretical analysis.

A Multi-objective Optimization Behavior Fusion Avoidance Method for Snake-like Rescue Robot

The obstacle avoidance path planning of rescue robot has been the key problem of autonomous navigation for rescue robot. Most of the existing path planning algorithms is aimed at the optimization of a particular target, often resulting in deadlock or oscillation in order to obtain the optimal solution or the optimal path. Therefore, using the multi-objective optimization theory, this paper presents a multi-objective optimization dynamic obstacle avoidance algorithm based on interval weights, the fusion process of obstacle avoidance process is divided into three seed action, by giving the behavior function of different weights, the output rate of real-time dynamic change of different action. This is not to obtain the optimal solution or the optimal path at the current moment, but to obtain only the most efficient solution or the most satisfactory path at the current moment. Experiments show that the algorithm can effectively improve the flexibility and security of the obstacle avoidance process under the premise of ensuring the real-time and robustness of the search and rescue robot obstacle avoidance process.

Process modeling and parameter optimization based on assumed inherent sensor inversion for composite automated placement

Composite automated placement shows great potential for efficient manufacturing of large composite structures. In order to realize online layup quality detection and parameter optimization with high speed and desired layup quality, a methodology is developed based on assumed inherent sensor inversion. First, it is necessary to conduct sensitive analysis in order to analyze the importance of process parameters and their changes. Then the relationship between these process parameters and the layup quality could be established by assumed inherent sensor inversion, which is considered as the basis of parameter optimization. Simultaneously, genetic algorithm combined with the multi-objective optimization theory is applied to determine the optimum set for obtaining desired composite components with high speed and best layup quality. A series of experiments had been conducted to verify the feasibility of the developed approach. Results demonstrate that the model has high precision, and significant improvement could be achieved through parameter optimization.

A Reliable Methodology for Multi-objective Voltage Regulation in the Presence of Renewable Power Generation

This article proposes a reliable computational paradigm for solving the optimal voltage regulation problem in power distribution systems in the presence of distributed generators. The main idea is to integrate elements of multi-objective optimization theory and decision analysis for computing a reliable problem solution, considering all the system uncertainties, and describing the multi-criteria aspects of the regulation problem. Detailed numerical simulations obtained from both balanced and unbalanced power systems are presented and discussed to demonstrate the effectiveness of the proposed methodology in the task of solving the optimal voltage regulation problem under different operating scenarios.

基于RORAC和MSD的多目标最优比例再保险

本文引入金融行业在风险管理和绩效评估等方面所常用的指标——风险调整资本收益率(RORAC)，以及单位标准差收益(MSD)，在资本约束范围内与条件风险价值(CVaR)建立多目标模型。在期望保费原理下，考虑扩散逼近风险模型中基于RORAC和MSD，以及CVaR的最优比例再保险问题。利用多目标优化理论，我们得到了比例再保险的Pareto最优解。最后，通过数值举例，探讨了初始资本水平，保险年度以及风险喜恶对最优再保险策略的影响。 In this paper, based on some indexes which are always used in risk management and performance appraisals for financial industry, such as return on risk adjusted capital (RORAC) and mean-standard deviation (MSD), we construct two multi-objective optimization models together with conditional value at risk (CVaR) as well as the capital regulation. Under the expected value premium principle, by the multi-objective optimization theory, we obtain the Pareto optimal proportional reinsurance strategy for the diffusion approximation risk model. Some numerical examples are given to show the impact of some important parameters, like initial capital and insurance year as well as risk aversion, on the optimal reinsurance strategy.

Promoting development of new traditional Chinese medicine by combination disease-syndrome and multi-objective optimization research in prevention and treatment of cardiovascular disease

Differences in theories, application forms and evaluation standards about curative effect between traditional Chinese medicine and modern medicine lead to not only question safty and effectiveness but also hinder development and internationalization of traditional Chinese medicine. Combination of common problems in traditional Chinese new drug registration with experiences in research on traditional Chinese new drugs of prevention and treatment of coronary heart disease elucidate application value about theory of disease-syndrome combination and multi-objective optimization in several ways such as the indications positioning, preparation process optimization, preclinical efficacy evaluating and clinical assessmenting of efficacy and analysis development prospect.

A remark on multiobjective stochastic optimization via strongly convex functions

Many economic and financial applications lead (from the mathematical point of view) to deterministic optimization problems depending on a probability measure. These problems can be static (one stage), dynamic with finite (multistage) or infinite horizon, single objective or multiobjective. We focus on one-stage case in multiobjective setting. Evidently, well known results from the deterministic optimization theory can be employed in the case when the “underlying” probability measure is completely known. The assumption of a complete knowledge of the probability measure is fulfilled very seldom. Consequently, we have mostly to analyze the mathematical models on the data base to obtain a stochastic estimate of the corresponding “theoretical” characteristics. However, the investigation of these estimates has been done mostly in one-objective case. In this paper we focus on the investigation of the relationship between “characteristics” obtained on the base of complete knowledge of the probability measure and estimates obtained on the (above mentioned) data base, mostly in the multiobjective case. Consequently we obtain also the relationship between analysis (based on the data) of the economic process characteristics and “real” economic process. To this end the results of the deterministic multiobjective optimization theory and the results obtained for stochastic one objective problems will be employed.

Study on Multi-objective Optimization Problem of Multi-source Image Fusion

Multi-source image fusion will integrate multiple images derived from the same scene or target collected into a new image to obtain more accurate and more complete description about the scene or target. The multi-objective optimi- zation problem of multi-source image fusion is researched in transform domain. Based on the analysis of multi-objective optimization theory and algorithms, an adaptive differential evolution algorithm is proposed. With adaptive variance fac- tor, dynamical crossover probability function and optimal elite ordering strategy, the algorithm reflects not only good search capability but also good convergence. When applied to multi-objective optimization of multi-source image fusion of transform domain, it will be an effective solution to the comprehensive evaluation in the image fusion process.

Generating optical freeform surfaces considering both coordinates and normals of discrete data points.

Through direct design methods, both coordinates and normals of discrete data points on a freeform surface are usually obtained, but traditionally the freeform surface is generated by only fitting the coordinates. In this paper, a novel generating method that fits both the coordinates and the normals is proposed based on the mathematical multiobjective optimization theory. This new method is suited for generating some freeform polynomial surfaces. Two design examples are introduced, and their optical performance is significantly improved when applying the new method compared with the traditional method. This new method is validated to be effective, and it is widely useful as long as the coordinates and the normals are provided in advance.

Comprehensive Evaluation Method of ORC System Performance Based on the Multi-Objective Optimization

The choice of evaluating indicator has a most important influence on the performance analysis and optimization of Organic Rankine-cycle (ORC) system. In this paper, the net output power of unit mass of exhaust gas (), the cycle exergy efficiency (), the total waste heat emissions () and the product of exchanger's area and total heat transfer coefficient () are selected as the four key indicators which can reflect the characteristics of ORC system. And then, a comprehensive evaluating indicator of ORC system performance () is obtained according to the theory of multi-objective optimization. Further, a comprehensive evaluation method of ORC system performance based on the multi-objective optimization is put forward in this paper. After that, this method is used to optimize the evaporation temperature which is considered as a key parameter of ORC system performance. According to researches, the results gained by using a single-objective optimization method with only one performance parameter as the indicator, cannot often take a global view of the ORC system performance and sometimes may lead to a deviation. Researches also show that increases firstly and then decreases with the increment of the evaporation temperature, and there exists a value of evaporation temperature to maximize . Through analysis and researches, it can be found that the comprehensive evaluation method of ORC system performance based on the multi-objective optimization provided in this paper gives a synthetic consideration to the requirements from high-efficiency, economy, thermodynamic perfection and environment protection. Moreover, by using this method, the ORC power generation system may be more likely to achieve the best comprehensive performance.

Study on multi-objective flexible job-shop scheduling problem considering energy consumption

Purpose: Build a multi-objective Flexible Job-shop Scheduling Problem(FJSP) optimization model, in which the makespan, processing cost, energy consumption and cost-weighted processing quality are considered, then Design a Modified Non-dominated Sorting Genetic Algorithm (NSGA-II) based on blood variation for above scheduling model. Design/methodology/approach: A multi-objective optimization theory based on Pareto optimal method is used in carrying out the optimization model. NSGA-II is used to solve the model. Findings: By analyzing the research status and insufficiency of multi-objective FJSP, Find that the difference in scheduling will also have an effect on energy consumption in machining process and environmental emissions. Therefore, job-shop scheduling requires not only guaranteeing the processing quality, time and cost, but also optimizing operation plan of machines and minimizing energy consumption. Originality/value: A multi-objective FJSP optimization model is put forward, in which the makespan, processing cost, energy consumption and cost-weighted processing quality are considered. According to above model, Blood-Variation-based NSGA-II (BVNSGA-II) is designed. In which, the chromosome mutation rate is determined after calculating the blood relationship between two cross chromosomes, crossover and mutation strategy of NSGA-II is optimized and the prematurity of population is overcome. Finally, the performance of the proposed model and algorithm is evaluated through a case study, and the results proved the efficiency and feasibility of the proposed model and algorithm.

Multi-objective optimumA design of double-layer perforated-wall breakwaters: Application of NSGA-II and bargaining models

Double-layered perforatedA wall (DLPW) breakwaters have mostly been studied by limited experimental methods due to complexities in their mathematical modeling. In this paper, a methodology based on data-driven simulation modeling, multi-objective optimization and game theory is proposed for optimum design of DLPW breakwaters. This methodology converts data to model and model to optimum decision in terms of design variables. Experimental results are converted to data-driven simulation models based onA ANFIS (adaptive neuro-fuzzy inference system), which can model the relationship between wave parameters, DLPW design variables and performance criteria considering inherent uncertainties in the experiment and results. The ANFIS-based simulation models are trained and validated based on experimental results of a physical DLPW breakwater. The simulation models are included in a powerful multi-objective optimization algorithm, Non-dominated sorting genetic algorithm-II (NSGA-II), to determine the trade-off between wave transmission and reflection as two important criteria in efficiency assessment of the DLPW breakwaters. Finally, two efficient multi-person decision-making models, namely unanimity fallback bargaining (UFB) and condorcet social choice method, are utilized to find the best agreed-upon design point on the trade-off curve from wave transmission and reflection point of view. The results indicate that the proposed methodology can be effectively used for determining the optimal characteristics of DLPW breakwaters.

Frequency Control of the Building Automation System in Rail Transit Stations Based on Multi-Objective Optimal Algorithm

The Building automation system (BAS) in rail transit stations with frequency optimization of unit control system is an important part for the equipment operation; it is essentially a distributed multi agent system (MAS). Based on the analysis of building automation system operation mode and the multi-objective optimal control theory, the control parameter set of frequency conversion unit is installed. After rolling optimization, the optimal solutions parameter set is got. Eventually the purpose that multiple unit run as the way of energy saving is achieved.

Research on Comprehensive Optimizing for Special Vehicle Handling and Riding Based on Multi-Objective Optimization Theory

According to the comprehensive optimizing problem, a vehicle dynamic model based on whole vehicle parameters was established in this paper. Seven optimization targets which are typical means for the special vehicles handling and riding and the twelve optimization parameters of the suspension system were put forward through analysis. And the multi-objective optimization solving was completed by using the multi-objective optimization tool. The pareto solutions for comprehensive optimization were obtained. Through setting standard of the maximum scores of understeer degree and the minimum value of driver seat departments RMS of full domain weighted acceleration, the Pareto solution was obtained. It can provide a solution method for balance and coordination chassis performance of the special vehicle.

Application of Multi-Objective Optimization Model in Design of Photovoltaic Architecture

With the increasingly serious crisis of the energy problem, all around the world, the application of solar energy has become an approach of solving the problem undoubtedly. In recent years, due to the rapid development of Chinese photovoltaic (PV) industry, the emergence of PV buildings is more and more widely in the domestic. The current trend is to apply Computer-Aided Design (CAD) software to realize the design of Building Integrated Photovoltaic (BIPV). The core problem of CAD is to obtain the best solution to a laying scheme of PV cell. In this paper, the design problem of PV building is concerned about. Through the research on the multi-objective optimization theory, it is to establish the model of multi-objective optimization that can meet these two objectives of the largest total annual solar photovoltaic power generation, yet the smallest possible the unit cost of power generation. The model can be solved by MATLAB using ideal point method. In order to detail explain the application of model and verify it, the design of PV cabin, in Datong area of Shanxi Province, is as an example. The study has laid the foundation for the development of CAD in the future.