Abstract:
By use of the properties of ant colony algorithm and genetic algorithm, a hybrid algorithm is proposed to solve the traveling salesman problems. First, it adopts genetic ...Show MoreMetadata
Abstract:
By use of the properties of ant colony algorithm and genetic algorithm, a hybrid algorithm is proposed to solve the traveling salesman problems. First, it adopts genetic algorithm to give 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 genetic algorithm, the effective solutions are obtained. Compare with the simulated annealing algorithm, the standard genetic algorithm, the standard ant colony algorithm, and statistics initial 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: 2007 Chinese Control Conference
Date of Conference: 26-31 July 2007
Date Added to IEEE Xplore: 15 October 2007
ISBN Information:
Print ISSN: 1934-1768
Citations are not available for this document.
Cites in Papers - |
Cites in Papers - IEEE (4)
Select All
1.
Hongtao Zhang, "Analysis and Prospect of Existing UAV Path Planning Algorithms", 2023 International Conference on Power, Electrical Engineering, Electronics and Control (PEEEC), pp.301-304, 2023.
2.
Xin Zhao, Xue Jia, Tao Zhang, Tianwei Liu, Yahui Cao, "A Supervised Surrogate-Assisted Evolutionary Algorithm for Complex Optimization Problems", IEEE Transactions on Instrumentation and Measurement, vol.72, pp.1-14, 2023.
3.
Tanagrit Chansaeng, Pita Jarupunphol, "Combinational Models for Enhancing the PTPM Efficiency", 2018 18th International Symposium on Communications and Information Technologies (ISCIT), pp.499-503, 2018.
4.
S. K. Jha, Anuli Dass, "Investigation of intelligent robust controller for a position control system", 2014 6th IEEE Power India International Conference (PIICON), pp.1-6, 2014.
Cites in Papers - Other Publishers (5)
1.
Peiying Zhang, Fanglin Liu, Chunxiao Jiang, Abderrahim Benslimane, Juan-Luis Gorricho, Joan Serrat-Fernández, "A Multi-Domain VNE Algorithm Based on Load Balancing in the IoT Networks", Mobile Networks and Applications, vol.27, no.1, pp.124, 2022.
2.
Jinbiao Li, Lianchao Zhang, Anqian Yang, Qilong Zhang, Xiangping Chen, "An Artificial Intelligence-Based Fusion Method for Wind Power Prediction", Conference Proceedings of 2021 International Joint Conference on Energy, Electrical and Power Engineering, vol.916, pp.621, 2022.
3.
Chunxiao Jiang, Peiying Zhang, "A Multi-Domain VNE Algorithm Based on Load Balancing in the IoT Networks", QoS-Aware Virtual Network Embedding, pp.299, 2021.
4.
Chunxi Liu, Zhikun Chen, Dongliang Peng, "Fast DOA Estimation Based on the Transform Domain Weighted Noise Subspace Fitting Algorithm for Generalized Sparse Array", International Journal of Antennas and Propagation, vol.2021, pp.1, 2021.
5.
Masaya Yoshikawa, "Pheromone-Balance Driven Ant Colony Optimization with Greedy Mechanism", Machine Learning and Systems Engineering, vol.68, pp.111, 2010.
