A Hybrid Algorithm Based on Particle Swarm Optimization and Ant Colony Optimization Algorithm

Lu, J. and Hu, W. and Wang, Y. (2017) A Hybrid Algorithm Based on Particle Swarm Optimization and Ant Colony Optimization Algorithm. In: First International Conference, SmartCom 2016, Shenzhen, China, December 17-19, 2016, Proceedings. Springer, pp. 22-31. ISBN 978-3-319-52015-5

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Abstract

Particle swarm optimization (PSO) and Ant Colony Optimization (ACO) are two important methods of stochastic global optimization. PSO has fast global search capability with fast initial speed. But when it is close to the optimal solution, its convergence speed is slow and easy to fall into the local optimal solution. ACO can converge to the optimal path through the accumulation and update of the information with the distributed parallel global search ability. But it has slow solving speed for the lack of initial pheromone at the beginning. In this paper, the hybrid algorithm is proposed in order to use the advantages of both of the two algorithm. PSO is first used to search the global solution. When it maybe fall in local one, ACO is used to complete the search for the optimal solution according to the specific conditions. The experimental results show that the hybrid algorithm has achieved the design target with fast and accurate search.

Item Type:	Book Section
Dates:	Date Event 2017 Published
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Faculty of Computing, Engineering and the Built Environment Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User:	Oana-Andreea Dumitrascu
Date Deposited:	30 Jun 2017 08:57
Last Modified:	22 Mar 2023 12:01
URI:	https://www.open-access.bcu.ac.uk/id/eprint/4751

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