Abstract:
By use of the properties of ant colony algorithm and particle swarm optimization, a hybrid algorithm is proposed to solve the traveling salesman problems. First, it adopt...Show MoreMetadata
Abstract:
By use of the properties of ant colony algorithm and particle swarm optimization, a hybrid algorithm is proposed to solve the traveling salesman problems. First, it adopts statistics method to get several initial better solutions and in accordance with them, gives information pheromone to distribute. Second, it makes use of the ant colony algorithm to get several solutions through information pheromone accumulation and renewal. Finally, by using across and mutation operation of particle swarm optimization, the effective solutions are obtained. Compare with the simulated annealing algorithm, the standard genetic algorithm and the standard ant colony algorithm, all the 16 hybrid algorithms are proved effective. Especially the hybrid algorithm with across strategy B and mutation strategy B is a simple and effective better algorithm than others.
Published in: 2006 Chinese Control Conference
Date of Conference: 07-11 August 2006
Date Added to IEEE Xplore: 15 January 2007
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Cites in Papers - |
Cites in Papers - IEEE (1)
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1.
Xiaotang Wen, Minghe Huang, Jianhua Shi, "Study on Resources Scheduling Based on ACO Allgorithm and PSO Algorithm in Cloud Computing", 2012 11th International Symposium on Distributed Computing and Applications to Business, Engineering & Science, pp.219-222, 2012.
Cites in Papers - Other Publishers (3)
1.
Arzoo, Anil Kumar, "Hybrid Ant Particle Swarm Genetic Algorithm (APSGA) for Task Scheduling in Cloud Computing", Information and Communication Technology for Competitive Strategies (ICTCS 2021), vol.401, pp.9, 2023.
2.
Ha-Bang Ban, "The hybridization of ACO + GA and RVNS algorithm for solving the time-dependent traveling salesman problem", Evolutionary Intelligence, 2020.
3.
Hai Yang, "Improved Ant Colony Algorithm Based on PSO and its Application on Cloud Computing Resource Scheduling", Advanced Materials Research, vol.989-994, pp.2192, 2014.
