THE ROUTING PROBLEM IN TRAFFIC CONTROL USING GENETIC ALGORITHMS

1994 ◽  
pp. 187-191 ◽  
Author(s):  
F.J. MARIN ◽  
F.J. GONZALEZ ◽  
F. SANDOVAL
Keyword(s):  
Genetic Algorithms ◽  
Traffic Control ◽  
Routing Problem

The Routing Problem in Traffic Control Using Genetic Algorithms

1994 ◽  
Vol 27 (3) ◽  
pp. 187-191
Author(s):  
F.J. Marin ◽  
F.J. Gonzalez ◽  
F. Sandoval
Keyword(s):  
Genetic Algorithms ◽  
Traffic Control ◽  
Routing Problem

scholarly journals SCHEDULLING MODEL OF REPLENISHMENT AT SEA FOR STRICKING FORCE UNIT IN SEA OPERATION USING GENETIC ALGORITHM

JOURNAL ASRO ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 1
Author(s):  
Aris Tri Ika R ◽  
Benny Sukandari ◽  
Okol Sri Suharyo ◽  
Ayip Rivai Prabowo
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Time Windows ◽  
Lubricating Oil ◽  
Optimal Time ◽  
Routing Problem ◽  
Scheduling Model ◽  
Force Unit ◽  
Replenishment At Sea ◽  
Crossover And Mutation

Navy as a marine core in the defense force is responsible for providing security for realizing stability and security of the country.  At any time there was an invasion of other countries past through sea,  TNI AL must be able to break the enemy resistance line through a sea operation to obtain the sea superiority. But this time the endurance of Striking force Unit at only 7-10 days and required replenishment at sea to maximize the presence in the theater of operations to meet a demand of the logistics: HSD, Freshwater, Lubricating Oil, foodstuffs and amonisi. For the optimal replenishment at sea required scheduling model supporting unit to get the minimum time striking force unit was on node rendezvous. Replenishment at sea scheduling model for striking force unit refers to the problems Vehicle routing problem with time windows using Genetic Algorithms. These wheelbase used is roulette for reproduction, crossover, and mutation of genes. Genetic algorithms have obtained optimum results in the shortest route provisioning scenario uses one supporting unit with a total time of 6.89 days. In scenario two supporting unit with minimal time is 4.97 days. In the scenario, the changing of the node replenishment Genetic Algorithm also get optimal time is 4.97 days with two supporting units. Research continued by changing the parameters of the population, the probability of crossover and mutation that can affect the performance of the genetic algorithm to obtain the solution. Keywords: Genetic Algorithm, Model Scheduling, Striking Force unit

Dynamic Genetic Algorithms with Hyper-Mutation Schemes for Dynamic Shortest Path Routing Problem in Mobile Ad Hoc Networks

2012 ◽  
Vol 3 (1) ◽  
pp. 87-98 ◽  
Author(s):  
Hui Cheng
Keyword(s):  
Genetic Algorithms ◽  
Wireless Networks ◽  
Shortest Path ◽  
Network Topology ◽  
Ad Hoc ◽  
Optimization Techniques ◽  
Mobile Wireless Networks ◽  
Mobile Wireless ◽  
Routing Problem ◽  
Mobile Ad Hoc

In recent years, the static shortest path (SP) routing problem has been well addressed using intelligent optimization techniques, e.g., artificial neural networks (ANNs), genetic algorithms (GAs), particle swarm optimization (PSO), etc. However, with the advancement in wireless communications, more and more mobile wireless networks appear, e.g., mobile ad hoc network (MANET), wireless mesh network (WMN), etc. One of the most important characteristics in mobile wireless networks is the topology dynamics, that is, the network topology changes over time due to energy conservation or node mobility. Therefore, the SP routing problem in MANETs turns out to be a dynamic optimization problem. This paper proposes to use two types of hyper-mutation GAs to solve the dynamic SP routing problem in MANETs. The authors consider MANETs as target systems because they represent new generation wireless networks. The experimental results show that the two hyper-mutation GAs can quickly adapt to the environmental changes (i.e., the network topology change) and produce good solutions after each change.

Modelling time complexity of micro-genetic algorithms for online traffic control decisions

2019 ◽  
Vol 11 (2) ◽  
pp. 141
Author(s):  
Bassant Abdelrahman ◽  
Kenan Hazirbaba ◽  
Omar Mughieda ◽  
Ghassan Abu Lebdeh
Keyword(s):  
Genetic Algorithms ◽  
Traffic Control ◽  
Time Complexity ◽  
Online Traffic

Modelling time complexity of micro-genetic algorithms for online traffic control decisions

2019 ◽  
Vol 11 (2) ◽  
pp. 141
Author(s):  
Ghassan Abu Lebdeh ◽  
Kenan Hazirbaba ◽  
Omar Mughieda ◽  
Bassant Abdelrahman
Keyword(s):  
Genetic Algorithms ◽  
Traffic Control ◽  
Time Complexity ◽  
Online Traffic

scholarly journals Focusing on the Golden Ball Metaheuristic: An Extended Study on a Wider Set of Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
E. Osaba ◽  
F. Diaz ◽  
R. Carballedo ◽  
E. Onieva ◽  
A. Perallos
Keyword(s):  
Genetic Algorithms ◽  
Combinatorial Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Optimization Problems ◽  
Traveling Salesman ◽  
Routing Problem ◽  
Combinatorial Optimization Problems ◽  
Golden Ball ◽  
The One

Nowadays, the development of new metaheuristics for solving optimization problems is a topic of interest in the scientific community. In the literature, a large number of techniques of this kind can be found. Anyway, there are many recently proposed techniques, such as the artificial bee colony and imperialist competitive algorithm. This paper is focused on one recently published technique, the one called Golden Ball (GB). The GB is a multiple-population metaheuristic based on soccer concepts. Although it was designed to solve combinatorial optimization problems, until now, it has only been tested with two simple routing problems: the traveling salesman problem and the capacitated vehicle routing problem. In this paper, the GB is applied to four different combinatorial optimization problems. Two of them are routing problems, which are more complex than the previously used ones: the asymmetric traveling salesman problem and the vehicle routing problem with backhauls. Additionally, one constraint satisfaction problem (the n-queen problem) and one combinatorial design problem (the one-dimensional bin packing problem) have also been used. The outcomes obtained by GB are compared with the ones got by two different genetic algorithms and two distributed genetic algorithms. Additionally, two statistical tests are conducted to compare these results.

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