scholarly journals Phase-controlled metasurface design via optimized genetic algorithm

Nanophotonics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 3931-3939 ◽  
Author(s):  
Yulong Fan ◽  
Yunkun Xu ◽  
Meng Qiu ◽  
Wei Jin ◽  
Lei Zhang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
High Speed ◽  
Fitness Function ◽  
Low Noise ◽  
Local Optimum ◽  
Large Area ◽  
Long Distance ◽  
Random Mutation ◽  
Light Sheet Microscopy ◽  
Optical Modes

AbstractIn an optical Pancharatnam-Berry (PB) phase metasurface, each sub-wavelength dielectric structure of varied spatial orientation can be treated as a point source with the same amplitude yet varied relative phase. In this work, we introduce an optimized genetic algorithm (GA) method for the synthesis of one-dimensional (1D) PB phase-controlled dielectric metasurfaces by seeking for optimized phase profile solutions, which differs from previously reported amplitude-controlled GA method only applicable to generate transverse optical modes with plasmonic metasurfaces. The GA–optimized phase profiles can be readily used to construct dielectric metasurfaces with improved functionalities. The loop of phase-controlled GA consists of initialization, random mutation, screened evolution, and duplication. Here random mutation is realized by changing the phase of each unit cell, and this process should be efficient to obtain enough mutations to drive the whole GA process under supervision of appropriate mutation boundary. A well-chosen fitness function ensures the right direction of screened evolution, and the duplication process guarantees an equilibrated number of generated light patterns. Importantly, we optimize the GA loop by introducing a multi-step hierarchical mutation process to break local optimum limits. We demonstrate the validity of our optimized GA method by generating longitudinal optical modes (i. e., non-diffractive light sheets) with 1D PB phase dielectric metasurfaces having non-analytical counter-intuitive phase profiles. The produced large-area, long-distance light sheets could be used for realizing high-speed, low-noise light-sheet microscopy. Additionally, a simplified 3D light pattern generated by a 2D PB phase metasurface further reveals the potential of our optimized GA method for manipulating truly 3D light fields.

scholarly journals Optimization of Train Trip Package Operation Scheme

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Lu Tong ◽  
Lei Nie ◽  
Zhenhuan He ◽  
Huiling Fu
Keyword(s):  
Genetic Algorithm ◽  
Large Volume ◽  
High Speed ◽  
Freight Transportation ◽  
Fixed Time ◽  
Long Distance ◽  
Related Factors ◽  
Railway Freight ◽  
Proposed Model ◽  
Fixed Path

Train trip package transportation is an advanced form of railway freight transportation, realized by a specialized train which has fixed stations, fixed time, and fixed path. Train trip package transportation has lots of advantages, such as large volume, long distance, high speed, simple forms of organization, and high margin, so it has become the main way of railway freight transportation. This paper firstly analyzes the related factors of train trip package transportation from its organizational forms and characteristics. Then an optimization model for train trip package transportation is established to provide optimum operation schemes. The proposed model is solved by the genetic algorithm. At last, the paper tests the model on the basis of the data of 8 regions. The results show that the proposed method is feasible for solving operation scheme issues of train trip package.

scholarly journals A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2790
Author(s):  
Qi Xiong ◽  
Xinman Zhang ◽  
Shaobo He ◽  
Jun Shen
Keyword(s):  
Fractional Order ◽  
Iris Recognition ◽  
Search Algorithm ◽  
Fitness Function ◽  
Histogram Equalization ◽  
Enhancement Effect ◽  
Local Optimum ◽  
Long Distance ◽  
Iris Image ◽  
Bee Colony

At present, iris recognition has been widely used as a biometrics-based security enhancement technology. However, in some application scenarios where a long-distance camera is used, due to the limitations of equipment and environment, the collected iris images cannot achieve the ideal image quality for recognition. To solve this problem, we proposed a modified sparrow search algorithm (SSA) called chaotic pareto sparrow search algorithm (CPSSA) in this paper. First, fractional-order chaos is introduced to enhance the diversity of the population of sparrows. Second, we introduce the Pareto distribution to modify the positions of finders and scroungers in the SSA. These can not only ensure global convergence, but also effectively avoid the local optimum issue. Third, based on the traditional contrast limited adaptive histogram equalization (CLAHE) method, CPSSA is used to find the best clipping limit value to limit the contrast. The standard deviation, edge content, and entropy are introduced into the fitness function to evaluate the enhancement effect of the iris image. The clipping values vary with the pictures, which can produce a better enhancement effect. The simulation results based on the 12 benchmark functions show that the proposed CPSSA is superior to the traditional SSA, particle swarm optimization algorithm (PSO), and artificial bee colony algorithm (ABC). Finally, CPSSA is applied to enhance the long-distance iris images to demonstrate its robustness. Experiment results show that CPSSA is more efficient for practical engineering applications. It can significantly improve the image contrast, enrich the image details, and improve the accuracy of iris recognition.

Research on Co-Evolutionary Genetic Algorithm of Long-Distance Gas Pipelines Optimal Model Economy

2011 ◽  
Vol 383-390 ◽  
pp. 7246-7250
Author(s):  
Li Gang Li ◽  
Yong Shou Dai ◽  
Ji Guang Wang
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Fitness Function ◽  
Transmission Network ◽  
Optimal Model ◽  
Long Distance ◽  
Pipeline Network ◽  
Natural Gas Pipeline ◽  
Evolutionary Genetic ◽  
Different Parts

Based on the analysis of the current long-distance pipeline network running conditions, an economic optimal mathematical model of the gas transmission network including compressor station is used. The natural gas pipeline network is divided into different parts, and adopting the cooperation co-evolutionary genetic algorithm, the subpopulations are created. The fitness function is established by taking advantage of the punish function. The results of the simulation show that this approach has better convergence. It is an effective method to solve the optimization problem.

A New Flatness Evaluation-Rotation Method Based on GA

2010 ◽  
Vol 139-141 ◽  
pp. 2033-2037 ◽  
Author(s):  
Yan Ming Jiang ◽  
Gui Xiong Liu
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Three Dimensions ◽  
Local Optimum ◽  
Mutation Probability ◽  
Projection Length ◽  
Rotation Method ◽  
Minimum Zone ◽  
Crossover And Mutation

Flatness is one fundamental element of geometric forms, and the flatness evaluation is particularly important for ensuring the quality of industrial products. This paper presents a new flatness evaluation in the view of the minimum zone evaluation - rotation method based on genetic algorithm. This method determines the minimum zone through rotating measurement points in three dimensions coordinate. The points are firstly rotated about coordinate axes. Then they are projected in one axis, and the smallest projection length is the flatness value. The rotation angles are optimized by genetic algorithm to improve search efficiency. An exponential fitness function and the rotation angles range is designed on the basis of flatness characteristics. An adaptive mode of crossover and mutation probability is used to avoid local optimum. The results show this method can search the minimum zone and converge rapidly.

Optimal design of flywheels using an injection island genetic algorithm

1999 ◽  
Vol 13 (5) ◽  
pp. 327-340 ◽  
Author(s):  
DAVID EBY ◽  
R.C. AVERILL ◽  
WILLIAM F. PUNCH ◽  
ERIK D. GOODMAN
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Optimal Design ◽  
High Speed ◽  
Design Space ◽  
Fitness Function ◽  
Unit Weight ◽  
Computational Time ◽  
Finite Element Code ◽  
Symmetric Plane

This paper presents an approach to optimal design of elastic flywheels using an Injection Island Genetic Algorithm (iiGA), summarizing a sequence of results reported in earlier publications. An iiGA in combination with a structural finite element code is used to search for shape variations and material placement to optimize the Specific Energy Density (SED, rotational energy per unit weight) of elastic flywheels while controlling the failure angular velocity. iiGAs seek solutions simultaneously at different levels of refinement of the problem representation (and correspondingly different definitions of the fitness function) in separate subpopulations (islands). Solutions are sought first at low levels of refinement with an axi-symmetric plane stress finite element code for high-speed exploration of the coarse design space. Next, individuals are injected into populations with a higher level of resolution that use an axi-symmetric three-dimensional finite element code to “fine-tune” the structures. A greatly simplified design space (containing two million possible solutions) was enumerated for comparison with various approaches that include: simple GAs, threshold accepting (TA), iiGAs and hybrid iiGAs. For all approaches compared for this simplified problem, all variations of the iiGA were found to be the most efficient. This paper will summarize results obtained studying a constrained optimization problem with a huge design space approached with parallel GAs that had various topological structures and several different types of iiGA, to compare efficiency. For this problem, all variations of the iiGA were found to be extremely efficient in terms of computational time required to final solution of similar fitness when compared to the parallel GAs.

scholarly journals Optimization of High-Speed Train Control Strategy for Traction Energy Saving Using an Improved Genetic Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Ruidan Su ◽  
Qianrong Gu ◽  
Tao Wen
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Control Strategy ◽  
High Speed ◽  
Energy Savings ◽  
Fitness Function ◽  
High Speed Train ◽  
Improved Genetic Algorithm ◽  
Running Time ◽  
Train Control

A parallel multipopulation genetic algorithm (PMPGA) is proposed to optimize the train control strategy, which reduces the energy consumption at a specified running time. The paper considered not only energy consumption, but also running time, security, and riding comfort. Also an actual railway line (Beijing-Shanghai High-Speed Railway) parameter including the slop, tunnel, and curve was applied for simulation. Train traction property and braking property was explored detailed to ensure the accuracy of running. The PMPGA was also compared with the standard genetic algorithm (SGA); the influence of the fitness function representation on the search results was also explored. By running a series of simulations, energy savings were found, both qualitatively and quantitatively, which were affected by applying cursing and coasting running status. The paper compared the PMPGA with the multiobjective fuzzy optimization algorithm and differential evolution based algorithm and showed that PMPGA has achieved better result. The method can be widely applied to related high-speed train.

scholarly journals High-speed detection of emergent market clustering via an unsupervised parallel genetic algorithm

2016 ◽  
Vol Volume 112 (Number 1/2) ◽  
Author(s):  
Dieter Hendricks ◽  
Tim Gebbie ◽  
Diane Wilcox ◽  
◽  
◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Graphics Processing Units ◽  
High Speed ◽  
Spanning Trees ◽  
Fitness Function ◽  
Low Cost ◽  
Cost Effective ◽  
Fourth Generation ◽  
Parallel Genetic Algorithm ◽  
Matrix Estimation

Abstract We implement a master-slave parallel genetic algorithm with a bespoke log-likelihood fitness function to identify emergent clusters within price evolutions. We use graphics processing units (GPUs) to implement a parallel genetic algorithm and visualise the results using disjoint minimal spanning trees. We demonstrate that our GPU parallel genetic algorithm, implemented on a commercially available general purpose GPU, is able to recover stock clusters in sub-second speed, based on a subset of stocks in the South African market. This approach represents a pragmatic choice for low-cost, scalable parallel computing and is significantly faster than a prototype serial implementation in an optimised C-based fourth-generation programming language, although the results are not directly comparable because of compiler differences. Combined with fast online intraday correlation matrix estimation from high frequency data for cluster identification, the proposed implementation offers cost-effective, near-real-time risk assessment for financial practitioners.

scholarly journals Multi-weapon multi-target assignment based on hybrid genetic algorithm in uncertain environment

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142090592
Author(s):  
Yang Zhao ◽  
Yifei Chen ◽  
Ziyang Zhen ◽  
Ju Jiang
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Hybrid Genetic Algorithm ◽  
Threat Assessment ◽  
Local Optimum ◽  
Grey Theory ◽  
Interval Numbers ◽  
Uncertain Environment ◽  
Mutation Operators ◽  
Target Assignment

The multi-weapon multi-target assignment is always an unavoidable problem in military field. It does make sense to find a proper assignment of weapons to targets which may help maximize the attack effect. In this article, as the information achieved from the battlefield is becoming more and more uncertain, a novel threat assessment method and target assignment algorithm are proposed against the background of unmanned aerial vehicles intelligent air combat. Specifically, with regard to the threat assessment issue, a possibility degree function based on grey theory is structured to further improve the grey analytic hierarchy process. It can transform the interval weight of threat factors into scalar-valued weight, with which the accuracy of threat assessment can be improved. Regarding the target assignment problem, combining with interval grey number, an improved hybrid genetic algorithm is developed. The improvements are mainly consisting of adaptive crossover and mutation operators which can help to find an approximate solution within certain time constraints. Meanwhile, the simulated annealing operation is incorporated to avoid local optimum and premature phenomenon. In addition, the selection operation and fitness function are also redesigned to handle the interval numbers. Simulation results demonstrate the effectiveness of our algorithm in completing the multi-objective weapon-target assignment under uncertain environment.

High-Speed, Long-Distance Electric Traction

1905 ◽  
Vol 59 (1537supp) ◽  
pp. 24627-24628
Author(s):  
Charles A. Mudge
Keyword(s):  
High Speed ◽  
Long Distance ◽  
Electric Traction
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