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.

Stability Enhancement of Transfer Function by Ant Colony Optimization and Particle Swarm Optimization Algorithm

2018 ◽  
Vol 8 (3) ◽  
Author(s):  
Chaman Yadav ◽  
Prabha Singh ◽  
Jaya Mishra ◽  
Kushal Tiwari ◽  
Shashank Singh
Keyword(s):  
Particle Swarm Optimization ◽  
Evolutionary Algorithms ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Ant Colony ◽  
Swarm Optimization ◽  
The Way

This paper presents the concepts of three evolutionary algorithms i.e, ant colony optimization and particle swarm optimization algorithm. An evolutionary algorithm copies the way how evolution occurs in the nature. There are various types of evolutionary algorithms. This paper focuses on ACO and PSO algorithms. ACO provides solution to various optimization problems. It follows the principle of survival of the fittest. Various problems such as knapsack problem, TSP(travelling salesman problem) can be solved using genetic algorithm. Ant colony optimization is a heuristic algorithm which follows the behaviour of ants i.e., the way ants seek food in their environment by starting from their nest. Particle swarm optimization algorithm (PSO) is also an optimization algorithm which also uses a method of searching using some heuristics.

ANT COLONY OPTIMIZATION ALGORITHM FOR HETEROGENEOUS REDUNDANCY ALLOCATION IN MULTI-STATE SERIES-PARALLEL SYSTEMS

2009 ◽  
Vol 16 (02) ◽  
pp. 163-186 ◽  
Author(s):  
MANJU AGARWAL ◽  
VIKAS K. SHARMA
Keyword(s):  
System Design ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
System Reliability ◽  
Optimization Problems ◽  
Ant Colony ◽  
System Structure ◽  
Ant Colony Optimization Algorithm ◽  
Redundancy Allocation ◽  
Demand Distribution

This paper addresses the redundancy allocation problem of multi-state series-parallel reliability structures where each subsystem can consist of maximum two types of redundant components. The objective is to minimize the total investment cost of system design satisfying system reliability constraint and the consumer load demand. The demand distribution is presented as a piecewise cumulative load curve. The configuration uses the binary components from a list of available products to provide redundancy so as to increase system reliability. The components are characterized by their feeding capacity, reliability and cost. A system that consists of elements with different reliability and productivity parameters has the capacity strongly dependent upon the selection of components constituting its structure. An ant colony optimization algorithm has been presented to analyze the problem and suggest an optimal system structure. The solution approach consists of a series of simple steps as used in early ant colony optimization algorithms dealing with other optimization problems and still proves efficient over the prevalent methods with regard to solutions obtained/computation time. Three multi-state system design problems have been solved for illustration.

scholarly journals Controlling the Balance of Exploration and Exploitation in ACO Algorithm

2018 ◽  
Vol 26 (4) ◽  
pp. 1-9 ◽  
Author(s):  
Ayad ‎ Mohammed Jabbar
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Problems ◽  
Dimensional Space ◽  
Probabilistic Method ◽  
Population Based ◽  
Ant Colony ◽  
Exploration And Exploitation ◽  
Classification Image ◽  
Shortest Route ◽  
Space Experiments

Ant colony optimization is a meta-heuristic algorithm inspired by the foraging behavior of real ant colony. The algorithm is a population-based solution employed in different optimization problems such as classification, image processing, clustering, and so on. This paper sheds the light on the side of improving the results of traveling salesman problem produced by the algorithm. The key success that produces the valuable results is due to the two important components of exploration and exploitation. Balancing both components is the foundation of controlling search within the ACO. This paper proposes to modify the main probabilistic method to overcome the drawbacks of the exploration problem and produces global optimal results in high dimensional space. Experiments on six variant of ant colony optimization indicate that the proposed work produces high-quality results in terms of shortest route.

scholarly journals Ant Colony Optimization for Traveling Tourism Problem on Timor Island East Nusa Tenggara

2020 ◽  
Vol 3 (1) ◽  
pp. 28
Author(s):  
Yampi R Kaesmetan ◽  
Marlinda Vasty Overbeek
Keyword(s):  
Ant Colony Optimization ◽  
Search Algorithm ◽  
Ant Colony ◽  
Food Sources ◽  
Test Results ◽  
Tourist Destinations ◽  
South Central ◽  
Shortest Route ◽  
Starting Location ◽  
Simulation Time

Timor island consists of five districts and one city, namely Kupang District, South Central Timor District, North Central Timor, Belu District, Malaka District, and Kupang City. On the Timor island, it has natural tourist destinations, culinary tours, cultural and historical attractions most on the island of Timor. The Ant Colony Optimization (ACO) Algorithm is very unique compared to the other nearby search algorithm, this algorithm adopted because of Ant Colony who were looking for food from the nest to food sources by leaving a footprint called Pheromone. Mapping system algorithm using ant, tourist sites can show the shortest route between two points is desired. Ants algorithm proved to be applied in determining the optimum route, but still has the disadvantage of dependence on the parameter value is not maximized. From the test results based on parameters of the cycle and the number of ants affects the simulation time, for ant algorithm parameters. From the test results based on the parameters, α and β affects, number of node, the simulation time and the shortest distance varying toward the destination even if the starting location and ending on the same location.

An Improved Ant Colony Optimization Algorithm Based on Immunization Strategy

2012 ◽  
Vol 490-495 ◽  
pp. 66-70
Author(s):  
Yang Nan
Keyword(s):  
Evolutionary Algorithm ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Artificial Immune ◽  
Combination Optimization ◽  
Continuous Optimization Problems

Ant colony optimization has been become a very useful method for combination optimization problems. Based on close connections between combination optimization and continuous optimization, nowadays some scholars have studied to apply ant colony optimization to continuous optimization problems, and proposed some continuous ant colony optimizations. To improve the performance of those continuous ant colony optimizations, here the principles of evolutionary algorithm and artificial immune algorithm have been combined with the typical continuous Ant Colony Optimization, and the adaptive Cauchi mutation and thickness selection are used to operate the ant individual, so a new Immunized Ant Colony Optimization is proposed.

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