Using Swarm Intelligence for solving NP-Hard Problems

  • Saman M. Abdulrahman Department of Computer science, College of Computer Science and IT, Nawroz University, Duhok, Kurdistan Region - Iraq

Abstract

Swarm Intelligence algorithms are computational intelligence algorithms inspired from the collective behavior of real swarms such as ant colony, fish school, bee colony, bat swarm, and other swarms in the nature. Swarm Intelligence algorithms are used to obtain the optimal solution for NP-Hard problems that are strongly believed that their optimal solution cannot be found in an optimal bounded time. Travels Salesman Problem (TSP) is an NP-Hard problem in which a salesman wants to visit all cities and return to the start city in an optimal time. This article applies most efficient heuristic based Swarm Intelligence algorithms which are Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Bat algorithm (BA), and Ant Colony Optimization (ACO) algorithm to find a best solution for TSP which is one of the most well-known NP-Hard problems in computational optimization. Results are given for different TSP problems comparing the best tours founds by BA, ABC, PSO and ACO.

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Published
2017-08-08
How to Cite
ABDULRAHMAN, Saman M.. Using Swarm Intelligence for solving NP-Hard Problems. Academic Journal of Nawroz University, [S.l.], v. 6, n. 3, p. 46-50, aug. 2017. ISSN 2520-789X. Available at: <http://journals.nawroz.edu.krd/index.php/ajnu/article/view/78>. Date accessed: 03 apr. 2020. doi: https://doi.org/10.25007/ajnu.v6n3a78.
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Articles