Register Allocation with Graph Coloring by Ant Colony Optimization

2011 ◽  
Author(s):  
C. N. Lintzmayer ◽  
M. H. Mulati ◽  
A. F. da Silva
Keyword(s):  
Ant Colony Optimization ◽  
Graph Coloring ◽  
Register Allocation ◽  
Ant Colony

scholarly journals The Hybrid ColorAnt-RT Algorithms and an Application to Register Allocation

2015 ◽  
Vol 18 (55) ◽  
pp. 81
Author(s):  
Mauro Mulati, ◽  
Carla Lintzmayer ◽  
Anderson Da Silva
Keyword(s):  
Ant Colony Optimization ◽  
Graph Coloring ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Register Allocation ◽  
Ant Colony ◽  
Frequency Assignment ◽  
It Use ◽  
Combinatorial Optimization Problems ◽  
Ant Colony Optimization Algorithms

Ant Colony Optimization is a metaheuristic used to create heuristic algorithms to find good solutions for combinatorial optimization problems. This metaheuristic is inspired on the effective behavior present in some species of ants of exploring the environment to find and transport food to the nest. Several works have proposed using Ant Colony Optimization algorithms to solve problems such as vehicle routing, frequency assignment, scheduling and graph coloring. The graph coloring problem essentially consists in finding a number k of colors to assign to the vertices of a graph, so that there are no two adjacent vertices with the same color. This paper presents the hybrid ColorAnt-RT algorithms, a class of algorithms for graph coloring problems which is based on the Ant Colony Optimization metaheuristic and uses Tabu Search as local search. The experiments with ColorAnt-RT algorithms indicate that changing the way to reinforce the pheromone trail results in better results. In fact, the results with ColorAnt-RT show that it is a promising option in finding good approximations of k. The good results obtained by ColorAnt-RT motivated it use on a register allocation based on Ant Colony Optimization, called CARTRA. As a result, this paper also presents CARTRA, an algorithm that extends a classic graph coloring register allocator to use the graph coloring algorithm ColorAnt-RT. CARTRA minimizes the amount of spills, thereby improving the quality of the generated code.

scholarly journals Graph Coloring by Ant Colony Optimization with Queen Ant Strategy

2005 ◽  
Vol 125 (10) ◽  
pp. 1629-1630 ◽  
Author(s):  
Taisaku Shirahama ◽  
Satoshi Ono ◽  
Shigeru Nakayama
Keyword(s):  
Ant Colony Optimization ◽  
Graph Coloring ◽  
Ant Colony

scholarly journals Graph Coloring and Ant Colony Optimization based Sub-channel Allocation Algorithms for LTE-A HetNets

2021 ◽  
Author(s):  
Richa Siddavaatam
Keyword(s):  
Ant Colony Optimization ◽  
Graph Coloring ◽  
Channel Allocation ◽  
Ant Colony ◽  
Allocation Algorithms

Graph Coloring and Ant Colony Optimization based Sub-channel Allocation Algorithms for LTE-A HetNets

scholarly journals Graph Coloring and Ant Colony Optimization based Sub-channel Allocation Algorithms for LTE-A HetNets

2021 ◽  
Author(s):  
Richa Siddavaatam
Keyword(s):  
Ant Colony Optimization ◽  
Graph Coloring ◽  
Channel Allocation ◽  
Ant Colony ◽  
Allocation Algorithms

Graph Coloring and Ant Colony Optimization based Sub-channel Allocation Algorithms for LTE-A HetNets

Parallel Ant Colony Optimization on the University Course-Faculty Timetabling Problem in MSU-IIT

2012 ◽  
Author(s):  
Earth B. Ugat ◽  
Jennifer Joyce M. Montemayor ◽  
Mark Anthony N. Manlimos ◽  
Dante D. Dinawanao
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Timetabling Problem ◽  
University Course ◽  
The University

Achievement of Cost-Effective Datasets Transference in The Cloud Through Ant Colony Optimization

2012 ◽  
Vol 3 (3) ◽  
pp. 122-125
Author(s):  
THAHASSIN C THAHASSIN C ◽  
◽  
A. GEETHA A. GEETHA ◽  
RASEEK C RASEEK C
Keyword(s):  
Ant Colony Optimization ◽  
Cost Effective ◽  
Ant Colony

The Map-Based Shortest Route Selection by Using Ant Colony Optimization Algorithm

2018 ◽  
Vol 1 (1) ◽  
pp. 15
Author(s):  
Achmad Fanany Onnilita Gaffar ◽  
Agusma Wajiansyah ◽  
Supriadi Supriadi
Keyword(s):  
Ant Colony Optimization ◽  
Shortest Path ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Google Earth ◽  
Ant Colony ◽  
Food Sources ◽  
Shortest Route ◽  
Study Results ◽  
Destination Point

The shortest path problem is one of the optimization problems where the optimization value is a distance. In general, solving the problem of the shortest route search can be done using two methods, namely conventional methods and heuristic methods. The Ant Colony Optimization (ACO) is the one of the optimization algorithm based on heuristic method. ACO is adopted from the behavior of ant colonies which naturally able to find the shortest route on the way from the nest to the food sources. In this study, ACO is used to determine the shortest route from Bumi Senyiur Hotel (origin point) to East Kalimantan Governor's Office (destination point). The selection of the origin and destination points is based on a large number of possible major roads connecting the two points. The data source used is the base map of Samarinda City which is cropped on certain coordinates by using Google Earth app which covers the origin and destination points selected. The data pre-processing is performed on the base map image of the acquisition results to obtain its numerical data. ACO is implemented on the data to obtain the shortest path from the origin and destination point that has been determined. From the study results obtained that the number of ants that have been used has an effect on the increase of possible solutions to optimal. The number of tours effect on the number of pheromones that are left on each edge passed ant. With the global pheromone update on each tour then there is a possibility that the path that has passed the ant will run out of pheromone at the end of the tour. This causes the possibility of inconsistent results when using the number of ants smaller than the number of tours.

Modeling and optimizing logistics, information, economic and financial intercluster cooperation using the ant colony optimization algorithm

2020 ◽  
Vol 26 (11) ◽  
pp. 2427-2447
Author(s):  
S.N. Yashin ◽  
E.V. Koshelev ◽  
S.A. Borisov
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Population Decline ◽  
Innovation System ◽  
Federal District ◽  
Original Model ◽  
Ant Colony ◽  
Industrial Agglomeration ◽  
Ant Colony Optimization Algorithm

Subject. This article discusses the issues related to the creation of a technology of modeling and optimization of economic, financial, information, and logistics cluster-cluster cooperation within a federal district. Objectives. The article aims to propose a model for determining the optimal center of industrial agglomeration for innovation and industry clusters located in a federal district. Methods. For the study, we used the ant colony optimization algorithm. Results. The article proposes an original model of cluster-cluster cooperation, showing the best version of industrial agglomeration, the cities of Samara, Ulyanovsk, and Dimitrovgrad, for the Volga Federal District as a case study. Conclusions. If the industrial agglomeration center is located in these three cities, the cutting of the overall transportation costs and natural population decline in the Volga Federal District will make it possible to qualitatively improve the foresight of evolution of the large innovation system of the district under study.

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