scholarly journals Applying Data Clustering Feature to Speed Up Ant Colony Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Chao-Yang Pang ◽  
Ben-Qiong Hu ◽  
Jie Zhang ◽  
Wei Hu ◽  
Zheng-Chao Shan
Keyword(s):  
Ant Colony Optimization ◽  
Data Clustering ◽  
Optimization Problems ◽  
Ant Colony ◽  
Data Set ◽  
Local Clustering ◽  
Speed Up ◽  
Divide And Rule ◽  
Gene Data

Ant colony optimization (ACO) is often used to solve optimization problems, such as traveling salesman problem (TSP). When it is applied to TSP, its runtime is proportional to the squared size of problemNso as to look less efficient. The following statistical feature is observed during the authors’ long-term gene data analysis using ACO: when the data sizeNbecomes big, local clustering appears frequently. That is, some data cluster tightly in a small area and form a class, and the correlation between different classes is weak. And this feature makes the idea of divide and rule feasible for the estimate of solution of TSP. In this paper an improved ACO algorithm is presented, which firstly divided all data into local clusters and calculated small TSP routes and then assembled a big TSP route with them. Simulation shows that the presented method improves the running speed of ACO by 200 factors under the condition that data set holds feature of local clustering.

Study on Information Technology with MADS — A New Data Clustering Algorithm Based on Ant Colony Optimization

2013 ◽  
Vol 859 ◽  
pp. 572-576 ◽  
Author(s):  
Yong Li Liu
Keyword(s):  
Information Technology ◽  
Ant Colony Optimization ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Ant Colony ◽  
Cluster Validity ◽  
Cluster Validity Index ◽  
Validity Index ◽  
Data Set

In the field of information technology, data clustering algorithms are widely used. In this paper, we proposed a new data clustering algorithm, named MADS, It is based on ant colony Optimization. MADS can automatically find clusters, depending on a few parameters that are not directly related to the data set. In addition, there are some existence technique was also utilized in our method, such as the density concept and cluster validity index (DB-index). The experiment results verified that MADS is able to discover clusters with varying shapes and is effective when applied to image segmentation.

scholarly journals University course timetabling model using ant colony optimization algorithm approach

2019 ◽  
Vol 13 (1) ◽  
pp. 72 ◽  
Author(s):  
Munirah Mazlan ◽  
Mokhairi Makhtar ◽  
Ahmad Firdaus Khair Ahmad Khairi ◽  
Mohamad Afendee Mohamed
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Problems ◽  
Ant Colony ◽  
Data Set ◽  
Course Timetabling ◽  
Timetabling Problem ◽  
Combinatorial Optimization Problems ◽  
University Course Timetabling ◽  
Course Timetabling Problem ◽  
University Course

<p>Due to the increased number of students and regulations, all educational institutions have renewed their interest to appear in the number of complexity and flexibility since the resources and events are becoming more difficult to be scheduled. Timetabling is the type of problems where the events need to be organized into a number of timeslots to prevent the conflicts in using a given set of resources. Thus in the intervening decades, significant progress has been made in the course timetabling problem monitoring with meta-heuristic adjustment. In this study, ant colony optimization (ACO) algorithm approach has been developed for university course timetabling problem. ACO is believed to be a powerful solution approach for various combinatorial optimization problems. This approach is used according to the data set instances that have been collected. Its performance is presented using the appropriate algorithm. The results are arguably within the best results range from the literature. The performance assessment and results are used to determine whether they are reliable in preparing a qualifying course timetabling process.</p>

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.

scholarly journals High-Level Parallel Ant Colony Optimization with Algorithmic Skeletons

2021 ◽  
Author(s):  
Breno A. de Melo Menezes ◽  
Nina Herrmann ◽  
Herbert Kuchen ◽  
Fernando Buarque de Lima Neto
Keyword(s):  
Ant Colony Optimization ◽  
High Performance ◽  
Optimization Problems ◽  
Programming Model ◽  
Parallel Implementation ◽  
Ant Colony ◽  
Algorithmic Skeletons ◽  
Low Level ◽  
Programming Patterns ◽  
High Level

AbstractParallel implementations of swarm intelligence algorithms such as the ant colony optimization (ACO) have been widely used to shorten the execution time when solving complex optimization problems. When aiming for a GPU environment, developing efficient parallel versions of such algorithms using CUDA can be a difficult and error-prone task even for experienced programmers. To overcome this issue, the parallel programming model of Algorithmic Skeletons simplifies parallel programs by abstracting from low-level features. This is realized by defining common programming patterns (e.g. map, fold and zip) that later on will be converted to efficient parallel code. In this paper, we show how algorithmic skeletons formulated in the domain specific language Musket can cope with the development of a parallel implementation of ACO and how that compares to a low-level implementation. Our experimental results show that Musket suits the development of ACO. Besides making it easier for the programmer to deal with the parallelization aspects, Musket generates high performance code with similar execution times when compared to low-level implementations.

Ant Colony Algorithms for Steiner Trees

2011 ◽  
pp. 1551-1575 ◽  
Author(s):  
Gurdip Singh ◽  
Sanjoy Das ◽  
Shekhar V. Gosavi ◽  
Sandeep Pujar
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Ant Colony Optimization ◽  
Optimization Problems ◽  
Steiner Trees ◽  
Ant Colony ◽  
Wireless Sensor ◽  
Graph Theoretic ◽  
Ant Colony Algorithms ◽  
Complex Optimization

This chapter introduces ant colony optimization as a method for computing minimum Steiner trees in graphs. Tree computation is achieved when multiple ants, starting out from different nodes in the graph, move towards one another and ultimately merge into a single entity. A distributed version of the proposed algorithm is also described, which is applied to the specific problem of data-centric routing in wireless sensor networks. This research illustrates how tree based graph theoretic computations can be accomplished by means of purely local ant interaction. The authors hope that this work will demonstrate how innovative ways to carry out ant interactions can be used to design effective ant colony algorithms for complex optimization problems.

Risk Budgeted Portfolio Optimization Using an Extended Ant Colony Optimization Metaheuristic

2012 ◽  
Vol 3 (4) ◽  
pp. 25-42 ◽  
Author(s):  
G. A. Vijayalakshmi Pai
Keyword(s):  
Ant Colony Optimization ◽  
Portfolio Optimization ◽  
Stock Exchange ◽  
Experimental Studies ◽  
Optimization Methods ◽  
Investment Strategy ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Data Set ◽  
Exchange Data

Risk Budgeted portfolio optimization problem centering on the twin objectives of maximizing expected portfolio return and minimizing portfolio risk and incorporating the risk budgeting investment strategy, turns complex for direct solving by classical methods triggering the need to look for metaheuristic solutions. This work explores the application of an extended Ant Colony Optimization algorithm that borrows concepts from evolution theory, for the solution of the problem and proceeds to compare the experimental results with those obtained by two other Metaheuristic optimization methods belonging to two different genres viz., Evolution Strategy with Hall of Fame and Differential Evolution, obtained in an earlier investigation. The experimental studies have been undertaken over Bombay Stock Exchange data set (BSE200: July 2001-July 2006) and Tokyo Stock Exchange data set (Nikkei225: July 2001-July 2006). Data Envelopment Analysis has also been undertaken to compare the performance of the technical efficiencies of the optimal risk budgeted portfolios obtained by the three approaches.

scholarly journals Hybrid Algorithm Based on Ant Colony Optimization and Simulated Annealing Applied to the Dynamic Traveling Salesman Problem

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 884
Author(s):  
Petr Stodola ◽  
Karel Michenka ◽  
Jan Nohel ◽  
Marian Rybanský
Keyword(s):  
Simulated Annealing ◽  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Optimization Problems ◽  
Dynamic Environment ◽  
Population Diversity ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Local Optimum ◽  
Use Of Knowledge

The dynamic traveling salesman problem (DTSP) falls under the category of combinatorial dynamic optimization problems. The DTSP is composed of a primary TSP sub-problem and a series of TSP iterations; each iteration is created by changing the previous iteration. In this article, a novel hybrid metaheuristic algorithm is proposed for the DTSP. This algorithm combines two metaheuristic principles, specifically ant colony optimization (ACO) and simulated annealing (SA). Moreover, the algorithm exploits knowledge about the dynamic changes by transferring the information gathered in previous iterations in the form of a pheromone matrix. The significance of the hybridization, as well as the use of knowledge about the dynamic environment, is examined and validated on benchmark instances including small, medium, and large DTSP problems. The results are compared to the four other state-of-the-art metaheuristic approaches with the conclusion that they are significantly outperformed by the proposed algorithm. Furthermore, the behavior of the algorithm is analyzed from various points of view (including, for example, convergence speed to local optimum, progress of population diversity during optimization, and time dependence and computational complexity).

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