A Dynamic Re-Configuration and Order Optimization Model and Optimization Algorithm in Complex System

2010 ◽  
Vol 26-28 ◽  
pp. 1159-1162
Author(s):  
Ya Hong Yang ◽  
Ying Wang
Keyword(s):  
Complex System ◽  
Optimization Algorithm ◽  
Assignment Problem ◽  
Graph Model ◽  
Assessment System ◽  
Application System ◽  
Information Platform ◽  
Simulation Results ◽  
Order Assignment ◽  
Part Feature

In order to make the complex assessment system become more intelligent and efficient for the application and realization technology in real system, the enterprise alliance and its business reconfiguration model for complex application system are processed. The enterprise alliance with dynamic reconfiguration and part feature is established by constructing the information platform The system can realize the informationize in enterprise and between enterprise. The cooperation between enterprises can also be supported. The order assignment problem in the enterprise with directed graph model is presented. Simulation results show that the model and the algorithm are effective to the problem.

A Determinant Elimination Method for Bottleneck Assignment and Generalized Assignment Problems

2012 ◽  
Vol 239-240 ◽  
pp. 1522-1527
Author(s):  
Wen Bo Wu ◽  
Yu Fu Jia ◽  
Hong Xing Sun
Keyword(s):  
Computational Complexity ◽  
Optimization Algorithm ◽  
Assignment Problem ◽  
Numerical Experiments ◽  
High Efficiency ◽  
Synthesis Method ◽  
Assignment Problems ◽  
Generalized Assignment ◽  
The Cost ◽  
Time And Space Complexity

The bottleneck assignment (BA) and the generalized assignment (GA) problems and their exact solutions are explored in this paper. Firstly, a determinant elimination (DE) method is proposed based on the discussion of the time and space complexity of the enumeration method for both BA and GA problems. The optimization algorithm to the pre-assignment problem is then discussed and the adjusting and transformation to the cost matrix is adopted to reduce the computational complexity of the DE method. Finally, a synthesis method for both BA and GA problems is presented. The numerical experiments are carried out and the results indicate that the proposed method is feasible and of high efficiency.

Ontology alignment evaluation for online assessment of e-learners: a new e-learning management system

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rajakumar B.R. ◽  
Gokul Yenduri ◽  
Sumit Vyas ◽  
Binu D.
Keyword(s):  
Hierarchical Clustering ◽  
Optimization Algorithm ◽  
Semantic Processing ◽  
Assessment System ◽  
Online Assessment ◽  
Ontology Alignment ◽  
Content Type ◽  
System Module ◽  
The Mean ◽  
Better Than

Purpose This paper aims to propose a new assessment system module for handling the comprehensive answers written through the answer interface. Design/methodology/approach The working principle is under three major phases: Preliminary semantic processing: In the pre-processing work, the keywords are extracted for each answer given by the course instructor. In fact, this answer is actually considered as the key to evaluating the answers written by the e-learners. Keyword and semantic processing of e-learners for hierarchical clustering-based ontology construction: For each answer given by each student, the keywords and the semantic information are extracted and clustered (hierarchical clustering) using a new improved rider optimization algorithm known as Rider with Randomized Overtaker Update (RR-OU). Ontology matching evaluation: Once the ontology structures are completed, a new alignment procedure is used to find out the similarity between two different documents. Moreover, the objects defined in this work focuses on “how exactly the matching process is done for evaluating the document.” Finally, the e-learners are classified based on their grades. Findings On observing the outcomes, the proposed model shows less relative mean squared error measure when weights were (0.5, 0, 0.5), and it was 71.78% and 16.92% better than the error values attained for (0, 0.5, 0.5) and (0.5, 0.5, 0). On examining the outcomes, the values of error attained for (1, 0, 0) were found to be lower than the values when weights were (0, 0, 1) and (0, 1, 0). Here, the mean absolute error (MAE) measure for weight (1, 0, 0) was 33.99% and 51.52% better than the MAE value for weights (0, 0, 1) and (0, 1, 0). On analyzing the overall error analysis, the mean absolute percentage error of the implemented RR-OU model was 3.74% and 56.53% better than k-means and collaborative filtering + Onto + sequential pattern mining models, respectively. Originality/value This paper adopts the latest optimization algorithm called RR-OU for proposing a new assessment system module for handling the comprehensive answers written through the answer interface. To the best of the authors’ knowledge, this is the first work that uses RR-OU-based optimization for developing a new ontology alignment-based online assessment of e-learners.

AN IMPROVED HYBRID OPTIMIZATION ALGORITHM FOR THE QUADRATIC ASSIGNMENT PROBLEM

2005 ◽  
Vol 9 (2) ◽  
pp. 149-168 ◽  
Author(s):  
A. Misevičius
Keyword(s):  
Simulated Annealing ◽  
Tabu Search ◽  
Optimization Algorithm ◽  
Assignment Problem ◽  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Quadratic Assignment Problem ◽  
Hybrid Optimization ◽  
Quadratic Assignment ◽  
Hybrid Optimization Algorithm

In this paper, we present an improved hybrid optimization algorithm, which was applied to the hard combinatorial optimization problem, the quadratic assignment problem (QAP). This is an extended version of the earlier hybrid heuristic approach proposed by the author. The new algorithm is distinguished for the further exploitation of the idea of hybridization of the well‐known efficient heuristic algorithms, namely, simulated annealing (SA) and tabu search (TS). The important feature of our algorithm is the so‐called “cold restart mechanism”, which is used in order to avoid a possible “stagnation” of the search. This strategy resulted in very good solutions obtained during simulations with a number of the QAP instances (test data). These solutions show that the proposed algorithm outperforms both the “pure” SA/TS algorithms and the earlier author's combined SA and TS algorithm. Key words: hybrid optimization, simulated annealing, tabu search, quadratic assignment problem, simulation.

scholarly journals Risk Assessment System Based on Fuzzy Composite Evaluation and a Backpropagation Neural Network for a Shield Tunnel Crossing under a River

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xiao Liang ◽  
Taiyue Qi ◽  
Zhiyi Jin ◽  
Shaojie Qin ◽  
Pengtao Chen
Keyword(s):  
Neural Network ◽  
Numerical Simulation ◽  
Risk Assessment ◽  
Assessment System ◽  
Chamber Pressure ◽  
Shield Tunnel ◽  
Risk Level ◽  
Backpropagation Neural Network ◽  
Grouting Pressure ◽  
Simulation Results

Constructing a shield tunnel that crosses under a river poses considerable safety risks, and risk assessment is essential for guaranteeing the safety of tunnel construction. This paper studies a risk assessment system for a shield tunnel crossing under a river. Risk identification is performed for the shield tunnel, and the risk factors and indicators are determined. The relationship between the two is determined preliminarily by numerical simulation, the numerical simulation results are verified by field measurements, and a sample set is established based on the numerical simulation results. Fuzzy comprehensive evaluation and a backpropagation neural network are then used to evaluate and analyze the risk level. Finally, the risk assessment system is used to evaluate the risk for Line 5 of the Hangzhou Metro in China. Based on the evaluation results, adjustments to the slurry strength, grouting pressure, and soil chamber pressure are proposed, and the risk is mitigated effectively.

Research on a Dynamic Binary Stack Anti-Collision Algorithm

2012 ◽  
Vol 268-270 ◽  
pp. 1426-1431
Author(s):  
Jian Jun Yi ◽  
Fei Luo ◽  
Shao Li Chen ◽  
Bai Yang Ji ◽  
Hai Xu Yan
Keyword(s):  
The Other ◽  
Application System ◽  
Key Technology ◽  
Rfid Systems ◽  
Simulation Results ◽  
Better Than ◽  
Improved Algorithm

RFID anti-collision technology is one of a key technology in RFID application system. Anti-collision algorithms for RFID systems include tag anti-collision algorithms and reader anti-collision algorithms. This paper focused on the impoved binary algorithm and dynamic binary algorithm. An improved algorithm has been proposed, in which the collision bits was put into the stack and they were used as the reader’s request. Based on this mechanism, a novel binary stack algorithm has been proposed. Its simulation was given to analyze the performance of this algorithm. The simulation results showed that the amount of transmitted data in proposed algorithm was obviously less than those in the other two traditional algorithms with the number of tags and their bytes increasing. Consequently, the performance of the proposed algorithm is much better than that of the traditional anti-collision binary algorithm.

scholarly journals Multi-UAV Reconnaissance Task Assignment for Heterogeneous Targets Based on Modified Symbiotic Organisms Search Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 734 ◽  
Author(s):  
Hao-Xiang Chen ◽  
Ying Nan ◽  
Yi Yang
Keyword(s):  
Assignment Problem ◽  
Time Window ◽  
Search Algorithm ◽  
Task Assignment ◽  
Multiple Time ◽  
Nsga Ii ◽  
Symbiotic Organisms Search ◽  
Simulation Results ◽  
Symbiotic Organisms ◽  
Task Assignment Problem

This paper considers a reconnaissance task assignment problem for multiple unmanned aerial vehicles (UAVs) with different sensor capacities. A modified Multi-Objective Symbiotic Organisms Search algorithm (MOSOS) is adopted to optimize UAVs’ task sequence. A time-window based task model is built for heterogeneous targets. Then, the basic task assignment problem is formulated as a Multiple Time-Window based Dubins Travelling Salesmen Problem (MTWDTSP). Double-chain encoding rules and several criteria are established for the task assignment problem under logical and physical constraints. Pareto dominance determination and global adaptive scaling factors is introduced to improve the performance of original MOSOS. Numerical simulation and Monte-Carlo simulation results for the task assignment problem are also presented in this paper, whereas comparisons with non-dominated sorting genetic algorithm (NSGA-II) and original MOSOS are made to verify the superiority of the proposed method. The simulation results demonstrate that modified SOS outperforms the original MOSOS and NSGA-II in terms of optimality and efficiency of the assignment results in MTWDTSP.

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