Fast Genetic Algorithm for Pick-up Path Optimization in the Large Warehouse System

2015 ◽  
pp. 38-44 ◽  
Author(s):  
Yongjie Ma ◽  
Zhi Li ◽  
Wenxia Yun
Keyword(s):  
Genetic Algorithm ◽  
Path Optimization

scholarly journals Memory-efficient Genetic Algorithm for Path Optimization in Embedded Systems

2013 ◽  
Vol 6 (0) ◽  
pp. 28-36 ◽  
Author(s):  
Umair F. Siddiqi ◽  
Yoichi Shiraishi ◽  
Sadiq M. Sait
Keyword(s):  
Genetic Algorithm ◽  
Embedded Systems ◽  
Path Optimization ◽  
Memory Efficient

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.

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.

GA Implementation Path Optimization of Pneumatic Marking System

2013 ◽  
Vol 391 ◽  
pp. 390-393
Author(s):  
Lei Shao ◽  
Hai Bin Zuo ◽  
Nan Liu
Keyword(s):  
Genetic Algorithm ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Path Optimization ◽  
Matlab Simulation

According to the characteristics of pneumatic marking system, and the typing path was seen as a TSP problem. After comparing the Dijkstra optimization algorithm of marking path results, and applying the genetic algorithm (GA) to analysis, research, and solve the optimization problem, reasonable to get print needle typing path. In this case, printing mark time was shorten as much as possible. It was proved by MATLAB simulation that the study can solve the problem of path optimization and improve the efficiency of marking greatly.

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