- Volume 8 Issue 4
DOI QR Code
A Novel Hybrid Intelligence Algorithm for Solving Combinatorial Optimization Problems
- Deng, Wu (Software Institute, Dalian Jiaotong University) ;
- Chen, Han (Software Institute, Dalian Jiaotong University) ;
- Li, He (Software Institute, Dalian Jiaotong University)
- Received : 2014.07.06
- Accepted : 2014.11.16
- Published : 2014.12.30
The ant colony optimization (ACO) algorithm is a new heuristic algorithm that offers good robustness and searching ability. With in-depth exploration, the ACO algorithm exhibits slow convergence speed, and yields local optimization solutions. Based on analysis of the ACO algorithm and the genetic algorithm, we propose a novel hybrid genetic ant colony optimization (NHGAO) algorithm that integrates multi-population strategy, collaborative strategy, genetic strategy, and ant colony strategy, to avoid the premature phenomenon, dynamically balance the global search ability and local search ability, and accelerate the convergence speed. We select the traveling salesman problem to demonstrate the validity and feasibility of the NHGAO algorithm for solving complex optimization problems. The simulation experiment results show that the proposed NHGAO algorithm can obtain the global optimal solution, achieve self-adaptive control parameters, and avoid the phenomena of stagnation and prematurity.
- C. Blum and A. Roli, "Metaheuristics in combinatorial optimization: overview and conceptual comparison," ACM Computing Surveys, vol. 35, no. 3, pp. 268-308, 2003. https://doi.org/10.1145/937503.937505
- Y. Zheng and B. Liu, "Fuzzy vehicle routing model with credibility measure and its hybrid intelligent algorithm," Applied Mathematics and Computation, vol. 176, no. 2, pp. 673-683, 2006. https://doi.org/10.1016/j.amc.2005.10.013
- E. Corchado, A. Abraham, and A. de Carvalho, "Hybrid intelligent algorithms and applications," Information Sciences, vol. 180, no. 14, pp. 2633-2634, 2010. https://doi.org/10.1016/j.ins.2010.02.019
- H. C. Kuo and C. H. Lin, "Cultural evolution algorithm for global optimizations and its applications," Journal of Applied Research and Technology, vol. 11, pp. 510-522, 2013. https://doi.org/10.1016/S1665-6423(13)71558-X
- P. Tarasewich and P. R. McMullen, "Swarm intelligence: power in numbers," Communications of the ACM, vol. 45, no. 8, pp. 62-67, 2002.
- Z. J. Lee, S. F. Su, C. C. Chuang, and K. H. Liu, "Genetic algorithm with ant colony optimization (GA-ACO) for multiple sequence alignment," Applied Soft Computing, vol. 8, no. 1, pp. 55-78, 2008. https://doi.org/10.1016/j.asoc.2006.10.012
- S. Nemati, M. E. Basiri, N. Ghasem-Aghaee, and M. H. Aghdam, "A novel ACO-GA hybrid algorithm for feature selection in protein function prediction," Expert Systems with Applications, vol. 36, no. 10, pp. 12086-12094, 2009. https://doi.org/10.1016/j.eswa.2009.04.023
- M. Sheikhan and N. Mohammadi, "Neural-based electricity load forecasting using hybrid of GA and ACO for feature selection," Neural Computing and Applications, vol. 21, no. 8, pp. 1961-1970, 2012. https://doi.org/10.1007/s00521-011-0599-1
- B. Shuang, J. Chen, and Z. Li, "Study on hybrid PS-ACO algorithm," Applied Intelligence, vol. 34, no. 1, pp. 64-73, 2011. https://doi.org/10.1007/s10489-009-0179-6
- G. Dong, W. W. Guo, and K. Tickle, "Solving the traveling salesman problem using cooperative genetic ant systems," Expert Systems with Applications, vol. 39, no. 5, pp. 5006-5011, 2012. https://doi.org/10.1016/j.eswa.2011.10.012
- T. Saenphon, S. Phimoltares, and C. Lursinsap, "Combining new fast opposite gradient search with ant colony optimization for solving travelling salesman problem," Engineering Applications of Artificial Intelligence, vol. 35, pp. 324-334, 2014. https://doi.org/10.1016/j.engappai.2014.06.026
- J. H. Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, Ann Arbor, MI: University of Michigan Press, 1975.
- M. Dorigo and L. M. Gambardella, "Ant colonies for the travelling salesman problem," BioSystems, vol. 43, no. 2, pp. 73-81, 1997. https://doi.org/10.1016/S0303-2647(97)01708-5
- T. Kotzing, F. Neumann, H. Roglin, and C. Witt, "Theoretical analysis of two ACO approaches for the traveling salesman problem," Swarm Intelligence, vol. 6, no. 1, pp. 1-21, 2012. https://doi.org/10.1007/s11721-011-0059-7
- K. Y. Lee and F. F. Yang, "Optimal reactive power planning using evolutionary algorithms: a comparative study for evolutionary programming, evolutionary strategy, genetic algorithm, and linear programming," IEEE Transactions on Power Systems, vol. 13, no. 1, pp. 101-108, 1998. https://doi.org/10.1109/59.651620
- D. Mester, Y. Ronin, D. Minkov, E. Nevo, and A. Korol, "Constructing large-scale genetic maps using an evolutionary strategy algorithm," Genetics, vol. 165, no. 4, pp. 2269-2282, 2003.
- B. Angeniol, G. de La Croix Vaubois, and J. Y. Le Texier, "Self-organizing feature maps and the travelling salesman problem," Neural Networks, vol. 1, no. 4, pp. 289-293, 1998.
- R. Pasti and L. N. De Castro, "A neuro-immune network for solving the traveling salesman problem," in Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN2006), Vancouver, Canada, 2006, pp. 3760-3766.
- S. M. Chen and C. Y. Chien, "Solving the traveling salesman problem based on the genetic simulated annealing ant colony system with particle swarm optimization techniques," Expert Systems with Applications, vol. 38, no. 12, pp. 14439-14450, 2011. https://doi.org/10.1016/j.eswa.2011.04.163