scholarly journals Comparison of Genetic Algorithm and Hill Climbing for Shortest Path Optimization Mapping

2018 ◽  
Vol 31 ◽  
pp. 11017
Author(s):  
Mona Fronita ◽  
Rahmat Gernowo ◽  
Vincencius Gunawan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Shortest Path ◽  
Path Optimization ◽  
Hill Climbing ◽  
Small Cities ◽  
Mutation Probability ◽  
Optimal Distance ◽  
Early Departure ◽  
Google Map Api

Traveling Salesman Problem (TSP) is an optimization to find the shortest path to reach several destinations in one trip without passing through the same city and back again to the early departure city, the process is applied to the delivery systems. This comparison is done using two methods, namely optimization genetic algorithm and hill climbing. Hill Climbing works by directly selecting a new path that is exchanged with the neighbour’s to get the track distance smaller than the previous track, without testing. Genetic algorithms depend on the input parameters, they are the number of population, the probability of crossover, mutation probability and the number of generations. To simplify the process of determining the shortest path supported by the development of software that uses the google map API. Tests carried out as much as 20 times with the number of city 8, 16, 24 and 32 to see which method is optimal in terms of distance and time computation. Based on experiments conducted with a number of cities 3, 4, 5 and 6 producing the same value and optimal distance for the genetic algorithm and hill climbing, the value of this distance begins to differ with the number of city 7. The overall results shows that these tests, hill climbing are more optimal to number of small cities and the number of cities over 30 optimized using genetic algorithms.

scholarly journals Optimization of modes of the electric power systems by genetic algorithms

2020 ◽  
Vol 216 ◽  
pp. 01099
Author(s):  
Behzod Pulatov ◽  
Shanazarov Alisher
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Power Systems ◽  
Electric Power ◽  
Shortest Path ◽  
Optimization Problems ◽  
Electric Power Systems ◽  
Data Filtering ◽  
Path Search ◽  
Shortest Path Search

In article discusses issues for solving optimization problems based on the use of genetic algorithms. Nowadays, the genetic algorithms for solving various problems. This includes the shortest path search, approximation, data filtering and others. In particular, data is being examined regarding the use of a genetic algorithm to solve problems of optimizing the modes of electric power systems. Imagine an algorithm for developing the development of mathematical models, which includes developing the structure of the chromosome, creating a started population, creating a directing force for the population, etc.

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.

RDF Query Path Optimization Using Hybrid Genetic Algorithms

2022 ◽  
Vol 12 (1) ◽  
pp. 1-16
Author(s):  
Qazi Mudassar Ilyas ◽  
Muneer Ahmad ◽  
Sonia Rauf ◽  
Danish Irfan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Execution Time ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Solution Space ◽  
Path Optimization ◽  
Initial Population ◽  
Description Framework ◽  
The Cost

Resource Description Framework (RDF) inherently supports data mergers from various resources into a single federated graph that can become very large even for an application of modest size. This results in severe performance degradation in the execution of RDF queries. As every RDF query essentially traverses a graph to find the output of the Query, an efficient path traversal reduces the execution time of RDF queries. Hence, query path optimization is required to reduce the execution time as well as the cost of a query. Query path optimization is an NP-hard problem that cannot be solved in polynomial time. Genetic algorithms have proven to be very useful in optimization problems. We propose a hybrid genetic algorithm for query path optimization. The proposed algorithm selects an initial population using iterative improvement thus reducing the initial solution space for the genetic algorithm. The proposed algorithm makes significant improvements in the overall performance. We show that the overall number of joins for complex queries is reduced considerably, resulting in reduced cost.

Based on Genetic Algorithm Research of Stocker Picking Shortest Path

2012 ◽  
Vol 155-156 ◽  
pp. 186-190
Author(s):  
Fu Cai Wan ◽  
Duo Chen ◽  
Yong Qiang Wu
Keyword(s):  
Genetic Algorithm ◽  
Shortest Path ◽  
Optimization Problem ◽  
Traveling Salesman ◽  
Path Optimization ◽  
Improved Genetic Algorithm ◽  
Operating Process ◽  
Path Distance ◽  
Simulation Results ◽  
The Mathematical Model

This paper analyzes characteristics of automated warehouse stocker picking operating process. Path optimization problem is considered as traveling salesman problem. The coordinates of picking points by calculating determine a stocker running route. The mathematical model of a path distance is built. And using the improved genetic algorithm solves the above problem. Finally, M-file program of stocker running path optimization is written and run in MATLAB. The simulation results that, in solving stocker path optimization problem, it can search for a shortest path by genetic algorithm. Thereby enhance the efficiency of automated warehouse system, increase greater benefits of the enterprise.

Application of a Hybrid Algorithm Based on Genetic Algorithm and Hill-Climbing Algorithm to Tool Path Optimization in CNC Machining

2010 ◽  
Vol 102-104 ◽  
pp. 681-685 ◽  
Author(s):  
Hai Qing Du ◽  
Ji Bao Qi
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Tool Path ◽  
Cnc Machining ◽  
Path Optimization ◽  
Hill Climbing ◽  
Tool Path Optimization ◽  
Tool Paths ◽  
Hill Climbing Algorithm ◽  
Accelerate Convergence

The efficiency of CNC machining is greatly influenced by the tool path. A new hybrid algorithm for tool path optimization in CNC varied-shape grinding is presented in this paper. The algorithm was constructed by adding hill-climbing algorithm to nature genetic algorithm. In the new algorithm, the crossover operator and mutation operator were redesigned to enhance the local search capability and to accelerate convergence. Verification experiment demonstrated that the algorithm can reduce non-cutting movement of tool paths and improve machining efficiency significantly.

Research on Route Optimization of Dynamic Random Network Based on Genetic Algorithm

2010 ◽  
Vol 121-122 ◽  
pp. 792-796 ◽  
Author(s):  
Chun Lei Zhang
Keyword(s):  
Genetic Algorithm ◽  
Shortest Path ◽  
Optimization Problems ◽  
Random Network ◽  
Transport Network ◽  
Route Optimization ◽  
Path Optimization ◽  
Route Selection ◽  
Shortest Route ◽  
Mutation And Selection

The traditional algorithms of shortest path, different path and etc can only solve the path optimization problems of static network. Based on the fact of actual transport network as dynamic random network, the paper used method of shortest route to optimize the path. The solution of shortest route was on the basis of genetic algorithm. The paper designed operators of crossover, mutation and selection. In addition, the specific example of dynamic random network route selection based on the proposed algorithm also verified the feasibility of the algorithm.

A STUDY OF HEURISTIC APPROACHES FOR BREAKING SHORT CRYPTOGRAMS

2004 ◽  
Vol 13 (01) ◽  
pp. 45-64 ◽  
Author(s):  
RALPH MORELLI ◽  
RALPH WALDE ◽  
WILLIAM SERVOS
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Simulated Annealing ◽  
Heuristic Search ◽  
Optimization Algorithms ◽  
Hill Climbing ◽  
Frequency Data ◽  
Heuristic Approaches ◽  
Search Heuristics

In this study, we compare the use of genetic algorithms (GAs) and other forms of heuristic search in the cryptanalysis of short cryptograms. This paper expands on the work presented at FLAIRS-2003, which established the feasibility of a word-based genetic algorithm (GA) for analyzing short cryptograms.1 In this study the following search heuristics are compared both theoretically and experimentally: hill-climbing, simulated annealing, word-based and frequency-based genetic algorithms. Although the results reported apply to substitution ciphers in general, we focus in particular on short substitution cryptograms, such as the kind found in newspapers and puzzle books. Short cryptograms present a more challenging form of the problem. The word-based approach uses a relatively small dictionary of frequent words. The frequency-based approaches use frequency data for 2-, 3- and 4-letter sequences. The study shows that all of the optimization algorithms are successful at breaking short cryptograms, but perhaps more significantly, the most important factor in their success appears to be the choice of fitness measure employed.

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.

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