scholarly journals Method to Improve Airborne Pollution Forecasting by Using Ant Colony Optimization and Neuro-Fuzzy Algorithms

2014 ◽  
Vol 04 (04) ◽  
pp. 81-90 ◽  
Author(s):  
Elizabeth Martinez-Zeron ◽  
Marco A. Aceves-Fernandez ◽  
Efren Gorrostieta-Hurtado ◽  
Artemio Sotomayor-Olmedo ◽  
Juan Manuel Ramos-Arreguín
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Airborne Pollution ◽  
Fuzzy Algorithms ◽  
Neuro Fuzzy

scholarly journals Optimal Design of Adaptive Neuro-Fuzzy Inference System Using PSO And Ant Colony Optimization For Estimation of Uncertain Observed Values

2021 ◽  
Author(s):  
Mahdi Danesh ◽  
Sedighe Danesh
Keyword(s):  
Ant Colony Optimization ◽  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
Ant Colony ◽  
Fuzzy Regression ◽  
Machining Process ◽  
Estimation Methods ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Fuzzy Parameter

Abstract This study employs a new method for regression model prediction in an uncertain environment and presents fuzzy parameter estimation of fuzzy regression models using triangular fuzzy numbers. These estimation methods are obtained by new learning algorithms in which linear programming is used. In this study, the new algorithm is a combination of a fuzzy rule-based system, on the basis of particle swarm optimization (PSO) and ant Colony Optimization AC\({O}_{R}\). In addition, a simulation and a practical example in the field of machining process are applied to indicate the performance of the proposed methods in dealing with problems where the observed variables have the nature of uncertainty and randomness. Finally, the results of the proposed algorithms are evaluated.

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.

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