Layout optimization of a military operations center using a genetic algorithm

2020 ◽  
pp. 154851292093707
Author(s):  
Wenbi Wang
Keyword(s):  
Genetic Algorithm ◽  
Human Factors ◽  
Fitness Function ◽  
Military Operations ◽  
Abstract Representation ◽  
Genetic Encoding ◽  
Encoding Method ◽  
Elitist Strategy ◽  
Operations Center

A genetic algorithm was developed to support the spatial layout design of military operations centers. Based on an abstract representation of the workplace, the algorithm uses a textual string as the genetic encoding method, two genetic operations (i.e., selection and swap) for simulating an evolution process, a fitness function that reflects a human factors characterization of workplace layout requirements, and an elitist strategy for improving its search efficiency. The effectiveness of the algorithm was demonstrated in the design of a mid-sized operations center that involved a team of 68 operators. This algorithm expands the human factors practitioners’ toolkit and enhances their ability to examine layout options of complex workplaces using modeling and simulation.

An Algorithmic Solution to Improve Command Centre Layout

2017 ◽  
Vol 61 (1) ◽  
pp. 445-449 ◽  
Author(s):  
Wenbi Wang
Keyword(s):  
Fitness Function ◽  
Search Efficiency ◽  
Layout Problem ◽  
One Dimensional ◽  
Genetic Encoding ◽  
Algorithmic Solution ◽  
Military Command ◽  
Encoding Method ◽  
Elitist Strategy

A genetic algorithm was developed in this study to optimize the spatial layout of military command centres. The algorithm uses a textual string as the genetic encoding method, two genetic operations (i.e., selection and swap) for simulating an evolution process, a fitness function that reflects a human factors characterization of workplace layout requirements, and an elitist strategy for improving its search efficiency. To examine the effectiveness of the proposed algorithm, a simulation experiment was conducted using a hypothetical one dimensional layout problem. The results revealed that the algorithm identified the complete list of solutions that are theoretically optimal for the test problem. Compared to exhaustive search, the proposed algorithm increased search efficiency by more than 99%.

Fundamental Frequency Optimization of Laminated Cylindrical Panels by Elitist-Genetic Algorithm

2011 ◽  
Vol 471-472 ◽  
pp. 337-342 ◽  
Author(s):  
Mohammad Homayoun Sadr-Lahidjani ◽  
Hadi Ghashochi Bargh
Keyword(s):  
Genetic Algorithm ◽  
Fundamental Frequency ◽  
Fitness Function ◽  
Finite Strip Method ◽  
Design Variables ◽  
Cylindrical Panels ◽  
Edge Conditions ◽  
Length Width ◽  
Frequency Optimization ◽  
Elitist Strategy

In this paper, fundamental frequency optimization of symmetrically laminated cylindrical panels is investigated by the Elitist-Genetic algorithm (E-GA). The number of layers, the fiber orientation angles, edge conditions, length/width (a/b) and length/R (a/R) ratios are considered as design variables. The classical shallow shell theory (Donnell’s formulation) is applied to calculate the natural frequencies of laminated cylindrical curved panels. To improve the speed of the optimization process, the elitist strategy is used in the Genetic algorithm and the fitness function is computed with a semi-analytical finite strip method (FSM). A program based on Maple is used for this purpose. To check the validity, the obtained results are also compared with some other stacking sequences.

Arrangement Optimization of Instruments Based on Genetic Algorithm

2010 ◽  
Vol 97-101 ◽  
pp. 3622-3626
Author(s):  
Sheng Yuan Yan ◽  
Kun Yu ◽  
Zhi Jian Zhang ◽  
Min Jun Peng
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Solution Space ◽  
Optimization Method ◽  
Human Machine Interaction ◽  
System A ◽  
Frequency Of Use ◽  
Search Speed ◽  
Machine Interface ◽  
Encoding Method

The instruments arrangement of human-machine interface can directly influence the operation and efficiency of human-machine interaction in system. A novel instruments arrangement optimization method based on genetic algorithm was proposed. The biology heredity and evolution mode of genetic algorithm was used to search for the optimal or satisfying arrangement solution. Fitness function was constructed based on the principles of importance, frequency of use, relevance and operational sequence. Literal permutation encoding method was applied to represent the instruments arrangement. The optimization preserving strategy was used to enhance the search speed and to enlarge the width and depth of solution space. Finally, a case of instruments arrangement optimization proves that the arrangement optimization method is effective.

Fundamental Frequency Optimization of Angle-Ply Laminated Plates Using Elitist-Genetic Algorithm and Finite Strip Method

2010 ◽  
Author(s):  
Mohammad Homayoun Sadr ◽  
Hadi Ghashochi Bargh
Keyword(s):  
Genetic Algorithm ◽  
Fundamental Frequency ◽  
Fitness Function ◽  
Composite Plates ◽  
Laminated Plates ◽  
Finite Strip ◽  
Strip Method ◽  
Finite Strip Method ◽  
Frequency Optimization ◽  
Elitist Strategy

In the present paper, fundamental frequency optimization of symmetrically angle-ply laminated composite plates is studied using the combination of Elitist-Genetic algorithm (EGA) and finite strip method (FSM). The design variables are the number of layers, the fiber orientation angles, edge conditions and plate length/width ratios. The classical laminated plate theory (CLPT) is used to calculate the natural frequencies of laminated rectangular plates. To improve the speed of the optimization process, the elitist strategy is used in the Genetic algorithm. In the E-GA, the fittest individuals in the generation survive and are automatically reinserted in the next generation, right before the next selection process takes place and the fitness function is computed with a semi-analytical finite strip model developed originally on the basis of full energy method. In addition, from the computational economy point of view, combination of E-GA and FSM provides a much higher convergence and reduced the CPU time. To check the validity, the obtained results are also compared with some other stacking sequences.

scholarly journals QoS-Aware Mobile Service Selection Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Chengwen Zhang ◽  
Lei Zhang ◽  
Guanhua Zhang
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Service Selection ◽  
Mobile Service ◽  
Context Aware ◽  
Selection Algorithm ◽  
Sequence Composition ◽  
Population Evolution ◽  
Encoding Method ◽  
Selection Of

For the problem of mobile service selection, this paper gives a context-aware service selection algorithm based on Genetic Algorithm. In this algorithm, a tree encoding method, a fitness function, and a fitness-better strategy were proposed. The tree encoding mode made Genetic Algorithm support selection of various types of service combinations, for example, sequence composition, concurrence composition, probability composition, and loop composition. According to the encoding method, a fitness function was designed specially. The fitness-better strategy gives the direction of population evolution and avoids the degradation of population fitness. Some experiments analyses show that the provided service selection algorithm can get better service composition.

scholarly journals Application of Genetic Algorithm Elements to Modelling of Rotation Processes in Motion Transmission Including a Long Shaft

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 115
Author(s):  
Andriy Chaban ◽  
Marek Lis ◽  
Andrzej Szafraniec ◽  
Radoslaw Jedynak
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fitness Function ◽  
Least Square ◽  
Distributed Parameter ◽  
Parameter Method ◽  
Analytical Mechanics ◽  
Parameter System ◽  
Rotational Systems ◽  
Elastic Elements

Genetic algorithms are used to parameter identification of the model of oscillatory processes in complicated motion transmission of electric drives containing long elastic shafts as systems of distributed mechanical parameters. Shaft equations are generated on the basis of a modified Hamilton–Ostrogradski principle, which serves as the foundation to analyse the lumped parameter system and distributed parameter system. They serve to compute basic functions of analytical mechanics of velocity continuum and rotational angles of shaft elements. It is demonstrated that the application of the distributed parameter method to multi-mass rotational systems, that contain long elastic elements and complicated control systems, is not always possible. The genetic algorithm is applied to determine the coefficients of approximation the system of Rotational Transmission with Elastic Shaft by equivalent differential equations. The fitness function is determined as least-square error. The obtained results confirm that application of the genetic algorithms allow one to replace the use of a complicated distributed parameter model of mechanical system by a considerably simpler model, and to eliminate sophisticated calculation procedures and identification of boundary conditions for wave motion equations of long elastic elements.

scholarly journals Hybrid Optimization Based Mathematical Procedure for Dimensional Synthesis of Slider-Crank Linkage

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1581
Author(s):  
Alfonso Hernández ◽  
Aitor Muñoyerro ◽  
Mónica Urízar ◽  
Enrique Amezua
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Linear Equations ◽  
Optimization Procedure ◽  
Optimization Techniques ◽  
Dimensional Synthesis ◽  
Hybrid Strategy ◽  
Dimensional Parameters ◽  
Optimization Methodology ◽  
Crank Mechanism

In this paper, an optimization procedure for path generation synthesis of the slider-crank mechanism will be presented. The proposed approach is based on a hybrid strategy, mixing local and global optimization techniques. Regarding the local optimization scheme, based on the null gradient condition, a novel methodology to solve the resulting non-linear equations is developed. The solving procedure consists of decoupling two subsystems of equations which can be solved separately and following an iterative process. In relation to the global technique, a multi-start method based on a genetic algorithm is implemented. The fitness function incorporated in the genetic algorithm will take as arguments the set of dimensional parameters of the slider-crank mechanism. Several illustrative examples will prove the validity of the proposed optimization methodology, in some cases achieving an even better result compared to mechanisms with a higher number of dimensional parameters, such as the four-bar mechanism or the Watt’s mechanism.

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