scholarly journals An Adaptive Test Sheet Generation Mechanism Using Genetic Algorithm

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Huan-Yu Lin ◽  
Jun-Ming Su ◽  
Shian-Shyong Tseng
Keyword(s):  
Genetic Algorithm ◽  
Computation Time ◽  
Mixed Integer ◽  
Selection Strategy ◽  
Integer Programming Problem ◽  
Test Sheet ◽  
Test Items ◽  
Adaptive Test ◽  
Time Series Approach ◽  
Assessment Requirements

For test-sheet composition systems, it is important to adaptively compose test sheets with diverse conceptual scopes, discrimination and difficulty degrees to meet various assessment requirements during real learning situations. Computation time and item exposure rate also influence performance and item bank security. Therefore, this study proposes an Adaptive Test Sheet Generation (ATSG) mechanism, where a Candidate Item Selection Strategy adaptively determines candidate test items and conceptual granularities according to desired conceptual scopes, and an Aggregate Objective Function applies Genetic Algorithm (GA) to figure out the approximate solution of mixed integer programming problem for the test-sheet composition. Experimental results show that the ATSG mechanism can efficiently, precisely generate test sheets to meet the various assessment requirements than existing ones. Furthermore, according to experimental finding, Fractal Time Series approach can be applied to analyze the self-similarity characteristics of GA’s fitness scores for improving the quality of the test-sheet composition in the near future.

Scheduling of Yard Truck Considering Loading and Unloading Simultaneously in an Underground Container Logistics System

2021 ◽  
pp. 036119812110478
Author(s):  
Yinping Gao ◽  
Daofang Chang ◽  
Jun Yuan ◽  
Chengji Liang
Keyword(s):  
Genetic Algorithm ◽  
Container Terminals ◽  
Mixed Integer ◽  
Operating Efficiency ◽  
Adaptive Genetic Algorithm ◽  
Integer Programming Problem ◽  
Logistics System ◽  
Container Logistics ◽  
Loading And Unloading ◽  
Mixed Integer Programming Problem

With the rapid growth of containers and scarce of land, the underground container logistics system (UCLS) presents a logical alternative for container terminals to better protect the environment and relieve traffic pressure. The operating efficiency of container terminals is one of the competitive edges over other terminals, which requires UCLS to be well integrated with the handling process of the storage yard. In UCLS, yard trucks (YTs) serve different handling points dynamically instead of one fixed handling point, and yard cranes (YCs) perform loading and unloading simultaneously. To minimize the total time of handling all containers in UCLS, the mixed integer programming problem is described and solved using an adaptive genetic algorithm (AGA). The convergence speed and accuracy of AGA are demonstrated by comparison with conventional genetic algorithm (GA). Additionally, AGA and CPLEX are compared with different scale cases. Experimental results show that the proposed algorithm is superior to CPLEX in resulted solutions and calculation time. A sensitivity analysis is provided to obtain reasonable numbers of YTs for scheduling between handling points and the storage yard in UCLS.

Coalition Formation Among Agents in Complex Problems Based on a Combinational Auction Perspective

2011 ◽  
pp. 48-60 ◽  
Author(s):  
Hiromitsu Hattori
Keyword(s):  
Coalition Formation ◽  
Computation Time ◽  
Combinatorial Auction ◽  
Mixed Integer ◽  
Integer Programming Problem ◽  
Scheduling Problem ◽  
Trade Off ◽  
Complex Problems ◽  
Real World Problem ◽  
Mixed Integer Programming Problem

This chapter focuses on a scheduling problem that considers various constraints as a complex real world problem. Constraints on scheduling can be expressed as combinations of items (time slots) in a combinatorial auction. Agents bid for necessary combinations of time slots to satisfy users’ preferences. We formalize a combinatorial auction for scheduling as an MIP (mixed integer programming) problem, which integrates the constraints on items and bids to express complex problems. This integration solves the trade-off between the computation time to find the solution and the expressiveness to represent a scheduling problem. This chapter presents a new formalization of a combinatorial auction with constraints. We have experimentally confirmed that our method can obtain a socially preferable schedule in practical time.

scholarly journals A Genetic Algorithm for a Two-Machine Flowshop with a Limited Waiting Time Constraint and Sequence-Dependent Setup Times

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Ju-Yong Lee
Keyword(s):  
Genetic Algorithm ◽  
Waiting Time ◽  
Computation Time ◽  
Time Constraint ◽  
Setup Times ◽  
Mixed Integer ◽  
Sequence Dependent ◽  
Sequence Dependent Setup ◽  
Dependent Setup Times ◽  
Sequence Dependent Setup Times

This study considers a two-machine flowshop with a limited waiting time constraint between the two machines and sequence-dependent setup times on the second machine. These characteristics are motivated from semiconductor manufacturing systems. The objective of this scheduling problem is to minimize the total tardiness. In this study, a mixed-integer linear programming formulation was provided to define the problem mathematically and used to find optimal solutions using a mathematical programming solver, CPLEX. As CPLEX required a significantly long computation time because this problem is known to be NP-complete, a genetic algorithm was proposed to solve the problem within a short computation time. Computational experiments were performed to evaluate the performance of the proposed algorithm and the suggested GA outperformed the other heuristics considered in the study.

scholarly journals Per-Phase and 3-Phase Optimal Coordination of Directional Overcurrent Relays Using Genetic Algorithm

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1699
Author(s):  
Ronald C. Matthews ◽  
Trupal R. Patel ◽  
Adam K. Summers ◽  
Matthew J. Reno ◽  
Shamina Hossain-McKenzie
Keyword(s):  
Genetic Algorithm ◽  
Optimization Algorithm ◽  
Distributed Storage ◽  
Renewable Energy Sources ◽  
Computation Time ◽  
Discrete Control ◽  
Mixed Integer ◽  
First Case ◽  
Three Phase ◽  
Optimal Coordination

Penetration of the power grid by renewable energy sources, distributed storage, and distributed generators is becoming increasingly common. Increased utilization of these distributed energy resources (DERs) has given rise to additional protection coordination concerns, particularly where they are utilized in an unbalanced manner or where loading among phases is unbalanced. Digital relays such as the SEL-751 (produced by Schweitzer Engineering Laboratories, Pullman, WA, USA) series have the capability of being set on a per-phase basis. This capability is underutilized in common practice. Additionally, in optimization algorithms for determining relay settings, the time-overcurrent characteristics (TOCs) of relays are generally not treated as variables and are assigned before running the optimization algorithm. In this paper, TOC options themselves are treated as discrete variables to be considered in the optimization algorithm. A mixed integer nonlinear programming problem (MINLP) is set up where the goal is to minimize relay operating times. A genetic algorithm (GA) approach is implemented in MATLAB where two cases are considered. In the first case, the TOC and Time dial setting (TDS) of each relay is set on a three-phase basis. In the second case, per-phase settings are considered. Relay TDSs and TOCs are both considered as simultaneous discrete control variables. Despite the stochastic nature of using per-phase settings for unbalanced systems is found to generally allow for shorter operating times. However, for relatively balanced systems, it is best to use three-phase settings if computation time is of importance.

scholarly journals Joint Relay Selection and Power Allocation through a Genetic Algorithm for Secure Cooperative Cognitive Radio Networks

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3934 ◽  
Author(s):  
Md Rahman ◽  
YoungDoo Lee ◽  
Insoo Koo
Keyword(s):  
Genetic Algorithm ◽  
Cognitive Radio ◽  
Power Allocation ◽  
Cognitive Radio Networks ◽  
Relay Selection ◽  
Radio Networks ◽  
Mixed Integer ◽  
Integer Programming Problem ◽  
Transmission Power ◽  
Cooperative Cognitive Radio Networks

In cooperative cognitive radio networks (CCRNs), there has been growing demand of transmitting secondary user (SU) source information secretly to the corresponding SU destination with the aid of cooperative SU relays. Efficient power allocation (PA) among SU relays and multi-relay selection (MRS) are a critical problem for operating such networks whereas the interference to the primary user receiver is being kept below a tolerable level and the transmission power requirements of the secondary users are being satisfied. Subsequently, in the paper, we develop the problem to solve the optimal solution for PA and MRS in a collaborative amplify-and-forward-based CCRNs, in terms of maximizing the secrecy rate (SR) of the networks. It is found that the problem is a mixed integer programming problem and difficult to be solved. To cope with this difficulty, we propose a meta-heuristic genetic algorithm-based MRS and PA scheme to maximize the SR of the networks while satisfying transmission power and the interference requirements of the networks. Our simulation results reveal that the proposed scheme achieves near-optimal SR performance, compared to the exhaustive search scheme, and provides a significant SR improvement when compared with some conventional relay selection schemes with equal power allocation.

Chaotic Genetic Algorithm for Mixed Integer Programming Problem

2014 ◽  
Vol 651-653 ◽  
pp. 2273-2277
Author(s):  
Ya Ming Wang ◽  
Z. Zhang ◽  
Jun Bao Zheng ◽  
L.L. Tong
Keyword(s):  
Genetic Algorithm ◽  
Integer Programming ◽  
Programming Problem ◽  
Mixed Integer Programming ◽  
Stability And Convergence ◽  
Mixed Integer ◽  
Search Process ◽  
Integer Programming Problem ◽  
Genetic Search ◽  
Mixed Integer Programming Problem

This paper proposed a chaotic genetic algorithm (CGA) to solve the mixed integer programming problem (MIPP). The basic idea of this algorithm is to overcome the deficiency of genetic algorithm (GA) by introducing chaotic disturbances into the genetic search process. Two typical MIPP problems are used to evaluate the performances of the proposed CGA. Experimental results show that performances of the algorithm have been improved by the chaotic disturbances, such as, search ability, precision, stability and convergence speed or calculation efficiency. The proposed CGA algorithm is suitable for solving complicated practical MIPP problem.

Two-Layer Optimize Algorithm for Microgrid Economic Dispatch

2014 ◽  
Vol 672-674 ◽  
pp. 1336-1341
Author(s):  
Yan Ding ◽  
Rong Jia ◽  
Kai Song Dong ◽  
Zhen Li ◽  
Wei Cheng Shen ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Integer Programming ◽  
Mixed Integer Programming ◽  
Optimization Algorithm ◽  
Power Flow ◽  
Lower Layer ◽  
Economic Dispatch ◽  
Mixed Integer ◽  
Integer Programming Problem ◽  
Short Time

To deal with the super short-time energy management of microgrid, a mixed-integer programming optimization algorithm combined with genetic algorithm is proposed. Firstly, this paper introduced the short-time economic dispatch mathematical model of microgrid. Secondly, a two-layer optimize algorithm is been developed. The lower layer takes no account of power flow constrains, convert the model into a mixed-integer programming problem through linearization techniques. The lower layer gets the generation schedule, and then passes the data to the upper layer. The upper layer takes the power flow constrains into account, optimize the unit output based on genetic algorithm. The simulation result shows that the proposed algorithm achieves a better complementary of the two kinds of optimization algorithm. At the same time, the optimization result also shows the effectiveness of the algorithm.

scholarly journals Optimal Placement and Sizing of D-STATCOM in Radial and Meshed Distribution Networks Using a Discrete-Continuous Version of the Genetic Algorithm

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1452
Author(s):  
Cristian Mateo Castiblanco-Pérez ◽  
David Esteban Toro-Rodríguez ◽  
Oscar Danilo Montoya ◽  
Diego Armando Giral-Ramírez
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Programming Model ◽  
Distribution Networks ◽  
Optimization Method ◽  
Optimal Placement ◽  
Mixed Integer ◽  
Power Balance ◽  
Continuous Version ◽  
Discrete Part

In this paper, we propose a new discrete-continuous codification of the Chu–Beasley genetic algorithm to address the optimal placement and sizing problem of the distribution static compensators (D-STATCOM) in electrical distribution grids. The discrete part of the codification determines the nodes where D-STATCOM will be installed. The continuous part of the codification regulates their sizes. The objective function considered in this study is the minimization of the annual operative costs regarding energy losses and installation investments in D-STATCOM. This objective function is subject to the classical power balance constraints and devices’ capabilities. The proposed discrete-continuous version of the genetic algorithm solves the mixed-integer non-linear programming model that the classical power balance generates. Numerical validations in the 33 test feeder with radial and meshed configurations show that the proposed approach effectively minimizes the annual operating costs of the grid. In addition, the GAMS software compares the results of the proposed optimization method, which allows demonstrating its efficiency and robustness.

scholarly journals Identifying the Optimal Subsets of Test Items through Adaptive Test for Cost Reduction of ICs

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 680
Author(s):  
Huaguo Liang ◽  
Jinlei Wan ◽  
Tai Song ◽  
Wangchao Hou
Keyword(s):  
Integrated Circuits ◽  
Cost Reduction ◽  
Bayesian Model ◽  
Test Methods ◽  
Test Time ◽  
Test Quality ◽  
Test Cost ◽  
Test Items ◽  
Adaptive Test ◽  
Test Escapes

With the growing complexity of integrated circuits (ICs), more and more test items are required in testing. However, the large number of invalid items (which narrowly pass the test) continues to increase the test time and, consequently, test costs. Aiming to address the problems of long test time and reduced test item efficiency, this paper presents a method which combines a fast correlation-based filter (FCBF) and a weighted naive Bayesian model which can identify the most effective items and make accurate quality predictions. Experimental results demonstrate that the proposed method reduces test time by around 2.59% and leads to fewer test escapes compared with the recently adopted test methods. The study shows that the proposed method can effectively reduce the test cost without jeopardizing test quality excessively.

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