scholarly journals Deepzzle: Solving Visual Jigsaw Puzzles With Deep Learning and Shortest Path Optimization

2020 ◽  
Vol 29 ◽  
pp. 3569-3581
Author(s):  
Marie-Morgane Paumard ◽  
David Picard ◽  
Hedi Tabia
Keyword(s):  
Deep Learning ◽  
Shortest Path ◽  
Path Optimization ◽  
Jigsaw Puzzles

scholarly journals A Fissile Ripple Spreading Algorithm to Solve Time-Dependent Vehicle Routing Problem via Coevolutionary Path Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Wen Xu ◽  
JiaJun Li
Keyword(s):  
Vehicle Routing ◽  
Shortest Path ◽  
Traffic Congestion ◽  
Path Optimization ◽  
Time Dependent ◽  
Optimization Approach ◽  
Routing Problem ◽  
Shortest Path Problems ◽  
Travelling Time ◽  
Logistics Delivery

The time-dependent vehicle routing problems have lately received great attention for logistics companies due to their crucial roles in reducing the time and economic costs, as well as fuel consumption and carbon emissions. However, the dynamic routing environment and traffic congestions have made it challenging to make the actual travelling trajectory optimal during the delivery process. To overcome this challenge, this study proposed an unconventional path optimization approach, fissile ripple spreading algorithm (FRSA), which is based on the advanced structure of coevolutionary path optimization (CEPO). The objective of the proposed model is to minimize the travelling time and path length of the vehicle, which are the popular indicators in path optimization. Some significant factors usually ignored in other research are considered in this study, such as congestion evolution, routing environment dynamics, signal control, and the complicated correlation between delivery sequence and the shortest path. The effectiveness of the proposed approach was demonstrated well in two sets of simulated experiments. The results prove that the proposed FRSA can scientifically find out the optimal delivery trajectory in a single run via global research, effectively avoid traffic congestion, and decrease the total delivery costs. This finding paves a new way to explore a promising methodology for addressing the delivery sequence and the shortest path problems at the same time. This study can provide theoretical support for the practical application in logistics delivery.

The Shortest Path Optimization Based on Mutation Particle Swarm Optimization Algorithm

2014 ◽  
Vol 1049-1050 ◽  
pp. 1690-1693 ◽  
Author(s):  
Juan Li
Keyword(s):  
Particle Swarm Optimization ◽  
Shortest Path ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Path Optimization ◽  
Adaptive Mutation ◽  
Swarm Optimization ◽  
Mutation Strategy

The traditional evolutionary algorithm is cannot converge faster to solve the path optimization problems, and the path that is computed is not the shortest path, in allusion to the disadvantage of this algorithm, a mutation particle swarm optimization algorithm is proposed. The algorithm introduces the adaptive mutation strategy, and accelerated the speed to search for the global optimal solution. For seven examples experiment in standard database, the result shows that the algorithm is more 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.

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.

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