scholarly journals Educational Information System Optimization for Artificial Intelligence Teaching Strategies

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Taotang Liu ◽  
Zhongxin Gao ◽  
Honghai Guan
Keyword(s):  
Artificial Intelligence ◽  
Information System ◽  
Network Architecture ◽  
Optimization Problems ◽  
Optimization Methods ◽  
System Optimization ◽  
Engineering Problem ◽  
Engineering Practice ◽  
Local Optimum ◽  
Clustering Method

Under the background of the information age, scientific research and engineering practice have developed vigorously, resulting in many complex optimization problems that are difficult to solve. How to design more effective optimization methods has become the focus of urgent solutions in many academic fields. Under the guidance of such demand, intelligent optimization algorithms have emerged. This article analyzes and optimizes the modern artificial intelligence teaching information system in detail. On the basis of determining the network architecture, a detailed demand analysis was carried out, and the overall structure optimization of the network was given; the business process and data flow of the main modules of the website (resource center module and collaborative learning module) were optimized. In order to further enhance the local search ability of the algorithm, a multiclass interactive optimization algorithm is proposed in combination with the Euclidean distance-based clustering method, which changes the teaching mode from “one-person teaching” to “multiperson teaching.” This clustering method has lower complexity and is beneficial to enhance the utilization of neighborhood information. At the same time, in order to enhance the diversity of the population and strengthen the connection between the subgroups, after the teaching phase, the worst students in each subgroup are allowed to learn from the best teachers of the population, and after the learning phase, individuals in a random subgroup are allowed to learn from other subgroups. The algorithm was tested in the experimental environment of unconstrained, constrained, and an engineering problem. From the test results, it can be seen that the algorithm is not easy to fall into the local optimum. Compared with other algorithms, the solution accuracy is higher and the stability is better. And it performed well in engineering optimization problems, thus verifying the effectiveness of the strategy.

scholarly journals Optimization Methods for Electric Power Systems: An Overview

2005 ◽  
Vol 2 (1) ◽  
Author(s):  
R. C. Bansal
Keyword(s):  
Artificial Intelligence ◽  
Power Systems ◽  
Electric Power ◽  
Power Optimization ◽  
Optimization Problems ◽  
Mathematical Optimization ◽  
Optimization Methods ◽  
Electric Power Systems ◽  
Unexpected Events ◽  
Large Complex

Power systems optimization problems are very difficult to solve because power systems are very large, complex, geographically widely distributed and are influenced by many unexpected events. It is therefore necessary to employ most efficient optimization methods to take full advantages in simplifying the formulation and implementation of the problem. This article presents an overview of important mathematical optimization and artificial intelligence (AI) techniques used in power optimization problems. Applications of hybrid AI techniques have also been discussed in this article.

Optimization of Machine System Designs Using Hierarchical Decomposition Based on Criteria Influence

2002 ◽  
Author(s):  
Masataka Yoshimura ◽  
Kazuhiro Izui ◽  
Shigeaki Komori
Keyword(s):  
Design Optimization ◽  
Structural Model ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Hierarchical Level ◽  
Hierarchical Optimization ◽  
Pareto Optimum ◽  
Local Optimum ◽  
Optimization Strategy ◽  
Basic Characteristics

Machine product designs routinely have so many mutually related characteristics that common design optimization methods often result in an unsatisfactory local optimum solution. In order to overcome this problem, this paper proposes a design optimization method based on the clarification of the conflicting and cooperative relationships among the characteristics. First of all, each performance characteristic is divided into simpler basic characteristics according to its structure. Next, the relationships among the basic characteristics are systematically identified and clarified. Then, based on this clarification, the optimization problem is expressed using hierarchical constructions of these basic characteristics and design variables related to the most basic characteristics. Finally, an optimization strategy and detailed hierarchical optimization procedures are constructed, after clarifying the influence levels of each basic characteristic upon the objective functions and setting a core characteristic for the product under consideration. Here, optimizations are sequentially repeated starting with the basic optimal unit group at the bottom hierarchical level and proceeding to higher levels by the hierarchical genetic algorithms. Then, the Pareto optimum solutions at the top hierarchical level are obtained. With the proposed optimization methods, optimization can be more easily applied after the optimization problems have been simplified by decomposition. In doing so, the volume of design spaces for each optimization is reduced, while useful and unique rules and laws may be uncovered. The optimization strategy expressed by the hierarchical structures can be used for the optimization of similar product designs, which realize these breakthroughs, yielding improved product performances. The proposed method is applied to a machine-tool structural model.

scholarly journals Optimal Operational Scheduling of Reconfigurable Microgrids in Presence of Renewable Energy Sources

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1858 ◽  
Author(s):  
Fatma Yaprakdal ◽  
Mustafa Baysal ◽  
Amjad Anvari-Moghaddam
Keyword(s):  
Distribution System ◽  
Distribution Systems ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Renewable Energy Sources ◽  
Hybrid Approach ◽  
Optimization Methods ◽  
System Optimization ◽  
Distribution Networks ◽  
Optimal Dispatch

Passive distribution networks are being converted into active ones by incorporating distributed means of energy generation, consumption, and storage, and the formation of so-called microgrids (MGs). As the next generation of MGs, reconfigurable microgrids (RMGs) are still in early phase studies, and require further research. RMGs facilitate the integration of distributed generators (DGs) into distribution systems and enable a reconfigurable network topology by the help of remote-controlled switches (RCSs). This paper proposes a day-ahead operational scheduling framework for RMGs by simultaneously making an optimal reconfiguration plan and dispatching controllable distributed generation units (DGUs) considering power loss minimization as an objective. A hybrid approach combining conventional particle swarm optimization (PSO) and selective PSO (SPSO) methods (PSO&SPSO) is suggested for solving this combinatorial, non-linear, and NP-hard complex optimization problem. PSO-based methods are primarily considered here for our optimization problem, since they are efficient for power system optimization problems, easy to code, have a faster convergence rate, and have a substructure that is suitable for parallel calculation rather than other optimization methods. In order to evaluate the suggested method’s performance, it is applied to an IEEE 33-bus radial distribution system that is considered as an RMG. One-hour resolution of the simultaneous network reconfiguration (NR) and the optimal dispatch (OD) of distributed DGs are carried out prior to this main study in order to validate the effectiveness and superiority of the proposed approach by comparing relevant recent studies in the literature.

Optimization of Adaptive Co-Ordination Based on Interaction Prediction Approach

2011 ◽  
Vol 135-136 ◽  
pp. 660-666
Author(s):  
Biao Wang ◽  
Chang Qing Wang ◽  
Feng Ru
Keyword(s):  
Optimization Problems ◽  
Object Oriented ◽  
Optimization Methods ◽  
System Optimization ◽  
Optimization Criterion ◽  
Decoupling Method ◽  
Interaction Prediction ◽  
Optimization Control ◽  
Output System ◽  
Prediction Approach

For parameters don't fully know big system optimization problems, establish adaptive co-ordination optimization control system, applying interaction prediction approach (IPA), will be predicted interaction output as coordination variables, with given objective optimal criteria in comparison, for this variable iteration calculations, make its eventually become the optimization of satisfying the objective optimization criterion, and can obtain corresponding to this optimization output system optimal control. This method compared with correlation decoupling method, only to meet the control number greater than or equal to the output number conditions, can be directly object-oriented feasibility coordination optimization methods.

scholarly journals Optimization of engineering systems using genetic algorithm enhanced computational techniques

2021 ◽  
Author(s):  
Nicholas Farouk Ali
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Mechanical Engineering ◽  
Optimization Methods ◽  
System Optimization ◽  
Optimization Techniques ◽  
Engineering Applications ◽  
Engineering Problems ◽  
New Ideas

The field of optimization has been and continues to be an area of significant importance in the industry. From financial, industrial, social and any other sector conceivable, people are interested in improving the scheme of existing methodologies and products and/or in creating new ideas. Due to the growing need for humans to improve their lives and add efficiency to a system, optimization has been and still is an area of active research. Typically optimization methods seek to improve rather than create new ideas. However, the ability of optimization methods to mold new ideas should not be ruled out, since optimized solutions usually lead to new designs, which are in most cases unique. Combinatorial optimization is the term used to define the method of finding the best sequence or combination of variables or elements in a large complex system in order to attain a particular objective. This thesis promises to provide a panoramic view of optimization in general before zooming into a specific artificial intelligence technique in optimization. Detailed information on optimization techniques commonly used in mechanical engineering is first provided to ensure a clear understanding of the thesis. Moreover, the thesis highlights the differences and similarities, advantages and disadvantages of these techniques. After a brief study of the techniques entailed in optimization, an artificial intelligence algorithm, namely genetic algorithm, was selected, developed, improved and later applied to a wide variety of mechanical engineering problems. Ample examples from various fields of engineering are provided to illustrate the versatility of genetic algorithms. The major focus of this thesis is therefore the application of genetic algorithms to solve a broad range of engineering problems. The viability of the genetic algorithm (GA) as an optimization tool for mechanical engineering applications is assessed and discussed. Comparison between GA generated results and results found in the literature are presented when possible to underscore the power of GA to solve problems. Moreover, the disadvantages and advantages of the genetic algorithms are discussed based on the results obtained. The mechanical engineering applications studied include conceptual aircraft design, design of truss structures under various constraints and loading conditions, and armour design using established penetration analytical models. Results show that the genetic algorithm developed is capable of handling a wide range of problems, is an efficient cost effective tool, and often provides superior results when compared to other optimization methods found in the literature.

Simulation Research on Intelligent Investment Agent Monitoring and Management System Optimization Based on Artificial Intelligence

2021 ◽  
Vol 7 (5) ◽  
pp. 2035-2044
Author(s):  
Man Lu
Keyword(s):  
Artificial Intelligence ◽  
Factor Analysis ◽  
Financial Risk ◽  
Optimization Problems ◽  
Principal Component ◽  
System Optimization ◽  
Investment Model ◽  
Simulation Research ◽  
Investment Models ◽  
Step Factor

Objectives: How to construct an effective investment early warning model, scientifically and accurately predict China’s extreme financial risks, and thus formulate effective measures to deal with and prevent risks, has become an important issue urgently needed to be solved by financial risk management departments and investors. Based on multi-step factor analysis and artificial intelligence classification, two main intelligent investment models based on artificial intelligence are designed in this paper. Firstly, the principal component analysis method and multi-step factor extraction method are used to select the variables of 28 Financial Indicators of listed companies, and a multi-factor analysis investment model is constructed. Secondly, a smart single classifier based on factor analysis is designed. The experimental results show that combined with multi-step factor analysis has a better warning effect. The final research results show that the algorithm can guarantee the convergence performance and dispersion performance for different optimization problems, and has the advantages of stability and robustness, which embodies the application value of the algorithm.

Optimum Design Using Search Agents

2001 ◽  
Author(s):  
Tomoyuki Miyashita ◽  
Hiroshi Yamakawa
Keyword(s):  
Information Exchange ◽  
Design Space ◽  
Nearest Neighbor ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Stochastic Search ◽  
Local Optimum ◽  
Objective Functions ◽  
Local Optima ◽  
Optimization Methodology

Abstract Many optimization methods and practical softwares have been developing for many years and most of them are very effective, especially to solve practical problems. But, non-linearity of objective functions and constraint functions, which have frequently seen in practical problems, has caused a difficulty in optimization. This difficulty mainly lies in the existence of several local optimum solutions. In this study, we have proposed a new global optimization methodology that provides an information exchange mechanism in the nearest neighbor method. We have developed a simple software system, which treated each design point in optimization as an agent. Many agents can search the optima simultaneously exchanging the their information. We have defined two roles of the agents. Local search agents have roles on searching local optima by such an existing method as the steepest decent method and so on. Stochastic search agents investigate the design space by making use of the information from other agents. Through simple and several structural optimization problems, we have confirmed the advantages of the method.

scholarly journals Optimization of engineering systems using genetic algorithm enhanced computational techniques

2021 ◽  
Author(s):  
Nicholas Farouk Ali
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Mechanical Engineering ◽  
Optimization Methods ◽  
System Optimization ◽  
Optimization Techniques ◽  
Engineering Applications ◽  
Engineering Problems ◽  
New Ideas

The field of optimization has been and continues to be an area of significant importance in the industry. From financial, industrial, social and any other sector conceivable, people are interested in improving the scheme of existing methodologies and products and/or in creating new ideas. Due to the growing need for humans to improve their lives and add efficiency to a system, optimization has been and still is an area of active research. Typically optimization methods seek to improve rather than create new ideas. However, the ability of optimization methods to mold new ideas should not be ruled out, since optimized solutions usually lead to new designs, which are in most cases unique. Combinatorial optimization is the term used to define the method of finding the best sequence or combination of variables or elements in a large complex system in order to attain a particular objective. This thesis promises to provide a panoramic view of optimization in general before zooming into a specific artificial intelligence technique in optimization. Detailed information on optimization techniques commonly used in mechanical engineering is first provided to ensure a clear understanding of the thesis. Moreover, the thesis highlights the differences and similarities, advantages and disadvantages of these techniques. After a brief study of the techniques entailed in optimization, an artificial intelligence algorithm, namely genetic algorithm, was selected, developed, improved and later applied to a wide variety of mechanical engineering problems. Ample examples from various fields of engineering are provided to illustrate the versatility of genetic algorithms. The major focus of this thesis is therefore the application of genetic algorithms to solve a broad range of engineering problems. The viability of the genetic algorithm (GA) as an optimization tool for mechanical engineering applications is assessed and discussed. Comparison between GA generated results and results found in the literature are presented when possible to underscore the power of GA to solve problems. Moreover, the disadvantages and advantages of the genetic algorithms are discussed based on the results obtained. The mechanical engineering applications studied include conceptual aircraft design, design of truss structures under various constraints and loading conditions, and armour design using established penetration analytical models. Results show that the genetic algorithm developed is capable of handling a wide range of problems, is an efficient cost effective tool, and often provides superior results when compared to other optimization methods found in the literature.

Improving the effectiveness of traditional education based on computer artificial intelligence and neural network system

2020 ◽  
pp. 1-11
Author(s):  
Wenjuan Ma ◽  
Xuesi Zhao ◽  
Yuxiu Guo
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Education Reform ◽  
Traditional Education ◽  
Machine Learning Algorithms ◽  
Local Optimum ◽  
Optimization Strategy ◽  
Fish School ◽  
Tlbo Algorithm ◽  
Neural Network System

The application of artificial intelligence and machine learning algorithms in education reform is an inevitable trend of teaching development. In order to improve the teaching intelligence, this paper builds an auxiliary teaching system based on computer artificial intelligence and neural network based on the traditional teaching model. Moreover, in this paper, the optimization strategy is adopted in the TLBO algorithm to reduce the running time of the algorithm, and the extracurricular learning mechanism is introduced to increase the adjustable parameters, which is conducive to the algorithm jumping out of the local optimum. In addition, in this paper, the crowding factor in the fish school algorithm is used to define the degree or restraint of teachers’ control over students. At the same time, students in the crowded range gather near the teacher, and some students who are difficult to restrain perform the following behavior to follow the top students. Finally, this study builds a model based on actual needs, and designs a control experiment to verify the system performance. The results show that the system constructed in this paper has good performance and can provide a theoretical reference for related research.

Software suite for determining the tilts of tower-type structures according to terrestrial laser scanning

2020 ◽  
Vol 961 (7) ◽  
pp. 2-7
Author(s):  
A.V. Zubov ◽  
N.N. Eliseeva
Keyword(s):  
Laser Scanning ◽  
Optimization Problems ◽  
Optimization Methods ◽  
User Participation ◽  
Software Suite ◽  
Current State ◽  
Visual Basic For Applications ◽  
Operational Data ◽  
Theoretical Developments

The authors describe a software suite for determining tilt degrees of tower-type structures according to ground laser scanning indication. Defining the tilt of the pipe is carried out with a set of measured data through approximating the sections by circumferences. They are constructed using one of the simplest search engine optimization methods (evolutionary algorithm). Automatic filtering the scan of the current section from distorting data is performed by the method of assessing the quality of models constructed with that of least squares. The software was designed using Visual Basic for Applications. It contains several blocks (subprograms), with each of them performing a specific task. The developed complex enables obtaining operational data on the current state of the object with minimal user participation in the calculation process. The software suite is the result of practical implementing theoretical developments on the possibilities of using search methods at solving optimization problems in geodetic practice.

Sign in / Sign up

Export Citation Format

Share Document