A new approach to detect epistasis utilizing parallel implementation of ant colony optimization by MapReduce framework

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.

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A New Approach to Modeling of Bio-inspired Information Diffusion with Ant Colony Optimization in Complex Networks

Nostradamus 2014: Prediction, Modeling and Analysis of Complex Systems - Advances in Intelligent Systems and Computing ◽

10.1007/978-3-319-07401-6_13 ◽

2014 ◽

pp. 131-140 ◽

Cited By ~ 1

Author(s):

Reisa Rahmatu Dewi ◽

Tae-Hyong Kim

Keyword(s):

Complex Networks ◽

Ant Colony Optimization ◽

Information Diffusion ◽

Ant Colony ◽

New Approach

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ACO powered by Local Searches Algorithms for the Solution of TSP Problems

Qubahan Academic Journal ◽

10.48161/qaj.v1n1a5 ◽

2020 ◽

Vol 1 (1) ◽

pp. 1-10

Author(s):

Nisreen L. Ahmed

Keyword(s):

Ant Colony Optimization ◽

Optimization Technique ◽

General Purpose ◽

Ant Colony ◽

Ant Colony Optimization Algorithm ◽

New Approach ◽

Local Searches ◽

Probabilistic Technique ◽

The Social ◽

Great Deluge Algorithm

Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and other animals. Ants, in particular, have inspired a number of methods and techniques among which the most studied and successful is the general-purpose optimization technique, also known as ant colony optimization, In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Ant Colony Optimization (ACO) algorithm is used to arrive at the best solution for TSP. In this article, the researcher has introduced ways to use a great deluge algorithm with the ACO algorithm to increase the ability of the ACO in finding the best tour (optimal tour). Results are given for different TSP problems by using ACO with great deluge and other local search algorithms.

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