scholarly journals Implementation of Genetic Algorithms for Optimization of Transportation Problem

2019 ◽  
Vol 8 (II) ◽  
Author(s):  
Soobia Saeed
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Transportation Problem ◽  
Optimal Solution ◽  
Computational Method ◽  
Different Types ◽  
Index Terms ◽  
The Impact ◽  
Single Processor ◽  
Human Problem

Transportation problem is a model which is commonly used in data structure solving a problem (human problem solving due to the computational method) because all the humans are related to transportation in any type of manner. Normally, traditional mathematical procedures used for solving the problem which is quite lengthy, after the computational solving procedures it comes to the bit easier to solve it except traditional lengthy methods. The Genetic Algorithm (GA) is most powerful tool for solving transportation problem. It refines the better optimal solution, for enhancing the optimization of transportation problem, using genetic algorithms lots of the work already has been done. This paper discusses the impact of genetic algorithms on two different types of systems environments i.e., Single-Processor Environment Systems and Multi-Processor Environment Systems, for solving the transportation problem and found the best optimal solution time of both systems.   Index Terms— Transportation Problem, Genetics Algorithm (GA), Single-Processor Systems, Multi-Processor Systems, Optimization.

Minimization of total trim loss occurring in pipe cutting in shipbuilding with genetic algorithm

2021 ◽  
pp. 147509022199169
Author(s):  
Abdullah Türk ◽  
Dursun Saral ◽  
Murat Özkök ◽  
Ercan Köse
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Search Space ◽  
Cutting Process ◽  
Genetic Operators ◽  
Critical Stage ◽  
Different Types ◽  
Trim Loss ◽  
Pipe Systems

Outfitting is a critical stage in the shipbuilding process. Within the outfitting, the construction of pipe systems is a phase that has a significant effect on time and cost. While cutting the pipes required for the pipe systems in shipyards, the cutting process is usually performed randomly. This can result in large amounts of trim losses. In this paper, we present an approach to minimize these losses. With the proposed method it is aimed to base the pipe cutting process on a specific systematic. To solve this problem, Genetic Algorithms (GA), which gives successful results in solving many problems in the literature, have been used. Different types of genetic operators have been used to investigate the search space of the problem well. The results obtained have proven the effectiveness of the proposed approach.

Optimization of Radial Cams with Translating Roller Follower through Genetic Algorithms

2015 ◽  
Vol 783 ◽  
pp. 83-94
Author(s):  
Alberto Borboni
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Contact Pressure ◽  
Optimization Problem ◽  
Optimization Method ◽  
Optimization Process ◽  
Radius Of Curvature ◽  
Design Problems ◽  
Computational Optimization ◽  
Different Types

In this work, the optimization problem is studied for a planar cam which rotates around its axis and moves a centered translating roller follower. The proposed optimization method is a genetic algorithm. The paper deals with different design problems: the minimization of the pressure angle, the maximization of the radius of curvature and the minimization of the contact pressure. Different types of motion laws are tested to found the most suitable for the computational optimization process.

scholarly journals A novel pseudoderivative-based mutation operator for real-coded adaptive genetic algorithms

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 139
Author(s):  
Maxinder S Kanwal ◽  
Avinash S Ramesh ◽  
Lauren A Huang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Mutation Rate ◽  
Optimal Solution ◽  
Global Optimum ◽  
Optimization Techniques ◽  
Adaptive Mutation ◽  
Local Optima ◽  
3 Dimensional ◽  
Successive Generations

Recent development of large databases, especially those in genetics and proteomics, is pushing the development of novel computational algorithms that implement rapid and accurate search strategies. One successful approach has been to use artificial intelligence and methods, including pattern recognition (e.g. neural networks) and optimization techniques (e.g. genetic algorithms). The focus of this paper is on optimizing the design of genetic algorithms by using an adaptive mutation rate that is derived from comparing the fitness values of successive generations. We propose a novel pseudoderivative-based mutation rate operator designed to allow a genetic algorithm to escape local optima and successfully continue to the global optimum. Once proven successful, this algorithm can be implemented to solve real problems in neurology and bioinformatics. As a first step towards this goal, we tested our algorithm on two 3-dimensional surfaces with multiple local optima, but only one global optimum, as well as on the N-queens problem, an applied problem in which the function that maps the curve is implicit. For all tests, the adaptive mutation rate allowed the genetic algorithm to find the global optimal solution, performing significantly better than other search methods, including genetic algorithms that implement fixed mutation rates.

A New Testability Optimization Allocation Approach

2013 ◽  
Vol 328 ◽  
pp. 444-449 ◽  
Author(s):  
Gang Liu ◽  
Fang Li
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Iterative Process ◽  
Resource Constraints ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Numerical Example ◽  
Set Up ◽  
Optimization Allocation ◽  
Allocation Approach

This paper describes a methodology based on improved genetic algorithms (GA) and experiments plan to optimize the testability allocation. Test resources were reasonably configured for testability optimization allocation, in order to meet the testability allocation requirements and resource constraints. The optimal solution was not easy to solve of general genetic algorithm, and the initial parameter value was not easy to set up and other defects. So in order to more efficiently test and optimize the allocation, migration technology was introduced in the traditional genetic algorithm to optimize the iterative process, and initial parameters of algorithm could be adjusted by using AHP approach, consequently testability optimization allocation approach based on improved genetic algorithm was proposed. A numerical example is used to assess the method. and the examples show that this approach can quickly and efficiently to seek the optimal solution of testability optimization allocation problem.

scholarly journals Using computation to enhance diagnosis and therapy: a novel mutation operator for real-coded adaptive genetic algorithms

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 139
Author(s):  
Maxinder S Kanwal ◽  
Avinash S Ramesh ◽  
Lauren A Huang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Mutation Rate ◽  
Novel Mutation ◽  
Fitness Function ◽  
Genetic Disorders ◽  
Optimal Solution ◽  
Global Optimum ◽  
Adaptive Mutation ◽  
Local Optima

The fields of molecular biology and neurobiology have advanced rapidly over the last two decades. These advances have resulted in the development of large proteomic and genetic databases that need to be searched for the prediction, early detection and treatment of neuropathologies and other genetic disorders. This need, in turn, has pushed the development of novel computational algorithms that are critical for searching genetic databases. One successful approach has been to use artificial intelligence and pattern recognition algorithms, such as neural networks and optimization algorithms (e.g. genetic algorithms). The focus of this paper is on optimizing the design of genetic algorithms by using an adaptive mutation rate based on the fitness function of passing generations. We propose a novel pseudo-derivative based mutation rate operator designed to allow a genetic algorithm to escape local optima and successfully continue to the global optimum. Once proven successful, this algorithm can be implemented to solve real problems in neurology and bioinformatics. As a first step towards this goal, we tested our algorithm on two 3-dimensional surfaces with multiple local optima, but only one global optimum, as well as on the N-queens problem, an applied problem in which the function that maps the curve is implicit. For all tests, the adaptive mutation rate allowed the genetic algorithm to find the global optimal solution, performing significantly better than other search methods, including genetic algorithms that implement fixed mutation rates.

scholarly journals INCREASING THE EFFICIENCY OF MULTIMODAL TRANSPORTATION WITH ALTERNATIVE USE OF DIFFERENT TYPES OF TRANSPORT

2021 ◽  
Vol 23 (2(79)) ◽  
pp. 96-104
Author(s):  
S.V. KOTENKO ◽  
V.A. KASIANOVA ◽  
M.V. SHAMIN
Keyword(s):  
Optimal Solution ◽  
Freight Transportation ◽  
Point Of View ◽  
Objective Functions ◽  
Measurement Scales ◽  
Multimodal Transportation ◽  
Different Types ◽  
Cargo Transportation ◽  
The Impact ◽  
Efficiency And Reliability

Topicality. The urgency of the study is due to the fact that in Ukraine the volume of cargo transportation using multimodal technologies is growing. Therefore, increasing the efficiency of multimodal transportation using the latest methods of their optimization is timely. This is especially true for those modes of transport whose share in total freight turnover is relatively small. Aim and tasks.The main purpose of the study is to develop theoretical and applied provisions and algorithms for finding the optimal solution for several target functions with different measurement scales to increase the efficiency of multimodal transportation, in particular, with the alternative use of different modes of transport. To achieve this goal, the following tasks arose: to study the impact of the monopolization of the transportation market by certain modes of transport on the reliability and efficiency of transportation; solving the problem of optimization of freight transportation from the national or general industry point of view; study of the importance of the development of inland water transport to increase the efficiency and reliability of multimodal transport; creating an algorithm for finding the optimal solution for several target functions. Research results.Theoretical and applied positions of search of the optimum decision on several target functions with different scale of measurement for increase of efficiency of multimodal transportations, including, at alternative use of different types of transport are investigated. The study proved that the choice of only one of the traditional target functions of transport companies - the cost or time of transportation does not guarantee its effectiveness without assessing the risks of transportation of goods. An algorithm for selecting the optimal solution for several objective functions is proposed. To do this, we used the method of finding the extremum of each of them by its own nontrivial subset. It is proved that these sets, in the general case, are not identical, so to find a solution for several objective functions complicated by their different dimensions, the principle of compromise must be used.The study established a significant degree of monopolization of the transportation market by certain modes of transport and indicated that to solve the problem of optimization of freight transportation from the national or general industry point of view requires equalization of disparities in freight turnover by mode of transport.Conclusion.Analysis of the results of the study allows us to draw the following conclusions: to use the resource to increase the efficiency and reliability of cargo transportation, especially for multimodal transportation technologies, it is necessary to equalize the disproportions of cargo turnover by types of cargo transportation to avoid monopolization of the transport market. To increase the efficiency of transportation, the latest algorithm for selecting the optimal solution for several target functions is proposed. The introduction of this algorithm to optimize multimodal transportation in scientific and practical problems will allow to take into account the risks and find a compromise solution in complex problems of finding solutions for the transportation of goods.

Research on Multimodal Transport Shortest Path Based on the Genetic Algorithm

2015 ◽  
Vol 744-746 ◽  
pp. 1813-1816
Author(s):  
Shou Wen Ji ◽  
Shi Jin ◽  
Kai Lv
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Shortest Path ◽  
Optimal Solution ◽  
Path Model ◽  
Time Analysis ◽  
Multimodal Transportation ◽  
Network Time ◽  
Multimodal Transport ◽  
Crossover And Mutation

This paper focuses on the research of multimodal transportation optimization model and algorithm, designs an intermodal shortest time path model and gives a solution to algorithm, constructs a multimodal transport network time analysis chart. By using genetic algorithms, the transportation scheme will be optimized. And based on each path’s code, the population will be evolved to obtain the optimal solution by using crossover and mutation rules.

User’s Preference Aggregation Based on Parallel Interactive Genetic Algorithms

2010 ◽  
Vol 34-35 ◽  
pp. 1159-1164 ◽  
Author(s):  
Yi Nan Guo ◽  
Yong Lin ◽  
Mei Yang ◽  
Shu Guo Zhang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimal Solution ◽  
Small Population ◽  
Population Diversity ◽  
Design System ◽  
Preference Aggregation ◽  
Ip Multicast ◽  
Interactive Genetic Algorithm ◽  
Interactive Genetic Algorithms

In traditional interactive genetic algorithms, high-quality optimal solution is hard to be obtained due to small population size and limited evolutional generations. Aming at above problems, a parallel interactive genetic algorithm based on knowledge migration is proposed. During the evolution, the number of the populations is more than one. Evolution information can be exchanged between every two populations so as to guide themselves evolution. In order to realize the freedom communication, IP multicast is adopted as the transfer protocol to find out the similar users instead of traditional TCP/IP communication mode. Taken the fashion evolutionary design system as test platform, the results indicate that the IP multicast-based parallel interactive genetic algorithm has better population diversity. It also can alleviate user fatigue and speed up the convergence.

Vehicle Scheduling Model for Fresh Agriculture Products Pickup with Uncertain Demands

2014 ◽  
Vol 974 ◽  
pp. 282-287
Author(s):  
Li Xia Rong ◽  
Huan Bin Sha
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Vehicle Scheduling ◽  
Uncertain Measure ◽  
Uncertainty Distribution ◽  
Uncertain Variables ◽  
Scheduling Model ◽  
Agriculture Products ◽  
The Impact

A chance-constrained vehicle scheduling model for fresh agriculture products pickup with uncertain demands is proposed in this paper. The uncertain measure that vehicle loading will not exceed capacity constraint is presented in the model because of the uncertainty of demands. Based on uncertainty theory, when the demands are some special uncertain variables with uncertainty distribution such as linear, zigzag and normal uncertain distribution etc., the model can be transformed to a deterministic form and solved by genetic algorithm. When the demands are general uncertain variables, a hybrid genetic algorithm with uncertain simulation is presented to obtain the optimal solution. At last, to illustrate the effective of the model and algorithm, and to analyze the impact of parameters on model solution, an experiment is provided.

Lamination Optimization of Composite Curved Wing for Maximum Flutter Stability Using Micro Genetic Algorithm

2006 ◽  
Vol 324-325 ◽  
pp. 743-746
Author(s):  
Dong Hyun Kim ◽  
Il Kwon Oh
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Composite Materials ◽  
Computational Method ◽  
Computational System ◽  
Wing Model ◽  
Weight Condition ◽  
Micro Genetic Algorithm

Flutter characteristics of composite curved wing are investigated in this study. The efficient and robust computational system for the flutter optimization has been developed using the coupled computational method based on the micro genetic algorithms. The present results show that the micro genetic algorithm is very efficient in order to find optimized lay-ups for a composite curved wing model. It is found that the flutter stability of curved wing model can be significantly increased using composite materials with proper optimum lamination design when compared to the case of isotropic wing model under the same weight condition.

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