scholarly journals RESEARCH OF GENETIC ALGORITHMS FOR SEARCHING OPTIMAL WAYS IN THE SYSTEM OF CONDUCTING METROMARATHONS

2019 ◽  
Vol 2 (93) ◽  
pp. 53-58
Author(s):  
О. О. Mazurova ◽  
Т. О. Gordienko
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Experimental Study ◽  
Genetic Algorithms ◽  
Graph Theory ◽  
Different Dimensions ◽  
Subway Stations ◽  
Genome Representation

The work is devoted to the study of genetic algorithms on the example of the search for the best ways to support“Transit challenge” or “Subway Challenge” system. Based on the rules of “Transit challenge” and the tasks of thesalesman, the problem of stations was formulated - the search for the best way to visit all subway stations in the shortestpossible time. Based on the graph theory, a mathematical model of the metrophone system was developed. To solve thestation problem, a genetic algorithm has been developed: the method of genome representation, population mutationand genome crossing rules have been chosen. On the basis of the experimental study of the genetic algorithm the mosteffective parameters were selected and recommendations on the solution of the station problem for “Transit challenge”of different dimensions were developed.

Research on Multi-Objective Transmission Planning in Electricity Markets

2012 ◽  
Vol 516-517 ◽  
pp. 1429-1432
Author(s):  
Yang Liu ◽  
Xu Liu ◽  
Feng Xian Cui ◽  
Liang Gao
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Genetic Algorithms ◽  
Electricity Markets ◽  
Optimization Problem ◽  
Nsga Ii ◽  
Transmission Planning ◽  
Multi Objective ◽  
Complex Optimization ◽  
The Mathematical Model

Abstract. Transmission planning is a complex optimization problem with multiple deciding variables and restrictions. The mathematical model is non-linear, discrete, multi-objective and dynamic. It becomes complicated as the system grows. So the algorithm adopted affects the results of planning directly. In this paper, a fast non-dominated sorting genetic algorithm (NSGA-II) is employed. The results indicate that NSGA-II has some advantages compared to the traditional genetic algorithms. In transmission planning, NSGA-II is feasible, flexible and effective.

Multi-Depot Vehicle Routing Problem with Hybrid Genetic Algorithm

2012 ◽  
Vol 479-481 ◽  
pp. 555-560 ◽  
Author(s):  
Li Wei Dang ◽  
Xiao Ming Sun
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Genetic Algorithms ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Hybrid Genetic Algorithm ◽  
Routing Problem ◽  
Transport Distance ◽  
Delivery Costs ◽  
Two Factors

About the multi-depot vehicle routing problem, considering the transport distance and the number of dispatching vehicles together can effectively reduce the total delivery costs. Firstly establish the corresponding mathematical model by taking the two factors into account. Secondly solve the model by using hybrid genetic algorithms. Thirdly demonstrate the effectiveness of the model and algorithm by an example

GENETINIO ALGORITMO TAIKYMAS ŽEMĖS SKLYPŲ RIBŲ TOPOLOGIJAI OPTIMIZUOTI NEKILNOJAMOJO TURTO KADASTRO ŽEMĖLAPYJE / АЛГОРИТМ ДЛЯ ОПТИМИЗАЦИИ ТОПОЛОГИИ ГРАНИЦ ЗЕМЕЛЬНЫХ УЧАСТКОВ НА КАДАСТРОВОЙ КАРТЕ НЕДВИЖИМОГО ИМУЩЕСТВА

2011 ◽  
Vol 37 (2) ◽  
pp. 84-90 ◽  
Author(s):  
Irina Jonauskienė ◽  
Algimantas Zakarevičius ◽  
Vladislovas Česlovas Aksamitauskas ◽  
Dmitrij Šešok
Keyword(s):  
Genetic Algorithm ◽  
Experimental Study ◽  
Genetic Algorithms ◽  
Real Estate ◽  
Turning Points ◽  
The Real ◽  
The Real Estate

Discrepancies between adjacent parcel boundaries happen when marking the boundaries of land parcels in the real estate cadastre map, for example, gaps, overlaps etc. In cases where the coordinates of land turning points meet statutory admissibility, the topology of land boundaries is adjusted. The paper addresses issues related to the application of genetic algorithms for optimizing the topology of land boundaries. When applying the genetic algorithm, for the purpose of optimizing the topology of the boundaries of land parcels, methodological justification and experimental study are employed. Santrauka Žymint žemės sklypų ribas nekilnojamojo turto kadastro žemėlapyje pasitaiko nesutapimų tarp gretimų sklypų ribų, t. y. atsiranda tarpai arba susidaro sanklotos. Tais atvejais, kai žemės sklypų posūkio taškų koordinatės atitinka teisės aktais nustatytus leistinumus, derinama žemės sklypų ribų topologija. Straipsnyje nagrinėjami klausimai, susiję su genetinių algoritmų taikymu žemės sklypų ribų topologijai optimizuoti. Atliktas genetinio algoritmo taikymo tam tikslui metodologinis pagrindimas ir eksperimentinis tyrimas. Резюме При обозначении границ земельных участков на кадастровой карте недвижимого имущества между границами смежных участков могут возникнуть расхождения, то есть появиться пробелы или пересечения. Когда координаты границ земельных участков не превышают предусмотренной правовыми и нормативными актами приемлемости, корректируется топология границ земельных участков. В статье рассмотрены вопросы, касающиеся применения генетических алгоритмов для оптимизации топологии границ земельных участков. Разработана методология применения генетического алгоритма для оптимизации топологии границ земельных участков и выполнено экспериментальное исследование.

scholarly journals Genetic Algorithm-Based Test Data Generation for Multiple Paths via Individual Sharing

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Xiangjuan Yao ◽  
Dunwei Gong
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Genetic Algorithms ◽  
Test Data ◽  
Experimental Results ◽  
Test Data Generation ◽  
Data Generation ◽  
Path Testing ◽  
Multiple Paths

The application of genetic algorithms in automatically generating test data has aroused broad concerns and obtained delightful achievements in recent years. However, the efficiency of genetic algorithm-based test data generation for path testing needs to be further improved. In this paper, we establish a mathematical model of generating test data for multiple paths coverage. Then, a multipopulation genetic algorithm with individual sharing is presented to solve the established model. We not only analyzed the performance of the proposed method theoretically, but also applied it to various programs under test. The experimental results show that the proposed method can improve the efficiency of generating test data for many paths’ coverage significantly.

Picking Route Optimization of Automated Warehouse Based on Improved Genetic Algorithms

2013 ◽  
Vol 411-414 ◽  
pp. 2694-2697
Author(s):  
Pei Guang Wang ◽  
Xing Min Qi ◽  
Xiao Ping Zong ◽  
Ling Ling Zhu
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Genetic Algorithms ◽  
Traveling Salesman Problem ◽  
Optimization Problem ◽  
Evolution Operator ◽  
Order Picking ◽  
Traveling Salesman ◽  
Route Optimization ◽  
Initial Population

In order to improve the efficiency of automated warehouse, the order-picking task of the fixed shelve was researched and analysed. The picking mathematical model of automated warehouse was established and attributed to the classical traveling salesman problem (TSP) model. At the same time, using an improved genetic algorithms(improved GAs) solved the optimization problem. Firstly, the initial population of the algorithm was optimized, and then a 'reverse evolution operator' was introduced in the improved genetic algorithms because of the lack of local optimization ability of genetic algorithm. Results of experiments verify that the method can acquire satisfying the demands of the route picking and optimization of speed.

The Application of Hanoi Towers Game in Logistics Management

2016 ◽  
pp. 422-433
Author(s):  
José Alberto Hernández Aguilar ◽  
Augusto Renato Pérez Mayo ◽  
Santiago Yip Ortuño ◽  
Alberto Ochoa-Zezzatti ◽  
Julio César Ponce Gallegos
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Game Theory ◽  
Genetic Algorithms ◽  
Logistics Management ◽  
C Language ◽  
Preliminary Results ◽  
Towers Of Hanoi ◽  
Depth Analysis

In this chapter we discussed the application of Towers of Hanoi in logistics management applications, for this purpose. Firstly, we discussed how pile problems applications impact in logistics, later we discussed the Hanoi Towers application in Logistics Management, its mathematical model and common solutions applying different paradigms (iterative, recursive, and heuristics), we present an in depth analysis in genetic algorithms, later we present the analysis of a genetic algorithm applied to solve basic Hanoi Tower game (three pegs three discs) implemented in C language, and later we discuss its generalization from three to four discs. Finally, we discussed preliminary results and present our conclusions. Main contribution of this chapter is demonstrate game theory, specifically Towers of Hanoi, can be applied to solve logistics problems, and an approach for the generalization of basic Hanoi Tower form, three discs to four discs, by means of genetic algorithms implemented in C language.

scholarly journals Quality Improvement of a Gear Transmission by Means of Genetic Algorithm

2019 ◽  
Vol 1 (1) ◽  
pp. 386-393
Author(s):  
Paweł Lempa ◽  
Edward Lisowski ◽  
Fumito Masui ◽  
Grzegorz Filo ◽  
Michal Ptaszynski ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Genetic Algorithms ◽  
Quality Improvement ◽  
Optimization Method ◽  
Gear Transmission ◽  
Flow Pulsation ◽  
Reduced Flow

Abstract The article deals with the issue of quality improvement of a gear transmission by optimizing its geometry with the use of genetic algorithms. The optimization method is focused on increasing productivity and efficiency of the pump and reducing its pulsation. The best results are tested on mathematical model and automatically modelled in 3D be means of PTC Creo Software. The developed solution proved to be an effective tool in the search for better results, which greatly improved parameters of pump especially reduced flow pulsation.

Mathematical Modeling and Genetic Algorithms for Product Sequencing in a Cellular System

2014 ◽  
pp. 31-47 ◽  
Author(s):  
Gürsel A. Süer ◽  
Fatih Yarimoglu
Keyword(s):  
Mathematical Modeling ◽  
Genetic Algorithm ◽  
Mathematical Model ◽  
Genetic Algorithms ◽  
The Other ◽  
Scheduling Problem ◽  
Optimal Result ◽  
Manufacturing Environment ◽  
Reasonable Time ◽  
Sequencing Problem

This chapter considers a product-sequencing problem in a synchronized manufacturing environment, which is using a uniform time bucket approach for synchronization. This problem has been observed in a jewelry manufacturing company and is valid in other labor-intensive cellular environments. The scheduling problem handled has two aspects: first, determining manpower allocation; second, sequencing the products in order to minimize the number of periods where available manpower is exceeded. The number of operators needed in a time bucket may exceed the available manpower level as different products have different manpower requirements for different processes. A mathematical model is developed for the manpower allocation part of the problem. To perform product sequencing, two methods are used, namely mathematical modeling and genetic algorithm. A new five-phase GA approach is proposed, and the results show that it outperforms the classical GA. Several experiments have been conducted to find better GA parameters as well. Finally, GA results are compared with mathematical model results. Mathematical Modeling finds optimal result in a reasonable time for small problems. On the other hand, for the bigger problems, genetic algorithm is a feasible approach to use.

Formulation of Colors Using a Genetic Algorithm

2012 ◽  
Vol 17 (4) ◽  
pp. 241-244
Author(s):  
Cezary Draus ◽  
Grzegorz Nowak ◽  
Maciej Nowak ◽  
Marcin Tokarski
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Production Process ◽  
Computer Technology ◽  
Manual Method ◽  
Apply Genetic ◽  
Computer Aided ◽  
Multiple Samples ◽  
Plastics Industry

Abstract The possibility to obtain a desired color of the product and to ensure its repeatability in the production process is highly desired in many industries such as printing, automobile, dyeing, textile, cosmetics or plastics industry. So far, most companies have traditionally used the "manual" method, relying on intuition and experience of a colorist. However, the manual preparation of multiple samples and their correction can be very time consuming and expensive. The computer technology has allowed the development of software to support the process of matching colors. Nowadays, formulation of colors is done with appropriate equipment (colorimeters, spectrophotometers, computers) and dedicated software. Computer-aided formulation is much faster and cheaper than manual formulation, because fewer corrective iterations have to be carried out, to achieve the desired result. Moreover, the colors are analyzed with regard to the metamerism, and the best recipe can be chosen, according to the specific criteria (price, quantity, availability). Optimaization problem of color formulation can be solved in many diferent ways. Authors decided to apply genetic algorithms in this domain.

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

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