Abstract
ACO (Ants Colony Optimization) algorithm has already obtained promising effect on solving many problems of combinatorial optimization due to its high efficiency, well robustness, positive feedback and the simultaneousness. Unfortunately the main defects of slow convergence and easy stagnancy in ACO low its applications. Fully employing the advantages of ACO, the paper proposes the novel tactics of updating the whole and local pheromone to avoid early stagnancy. Furthermore, the constraint satisfaction techniques are used to solve the problems of slow convergence by reducing the search space, accelerating search rate and enhancing efficiency. Finally, the case study for travelling salesman problem demonstrates the validation and efficiency of the improved ants colony algorithm.
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References
Solimanpura, M., Vratb, P., Shankarc, R.: A heuristic makespan of cell scheduling problem. International Journal of Production Economics 88, 231–241 (2004)
Solimanpur, M., Vrat, P., Shankar, R.: A neuro-tabu search heuristic for the flow shop scheduling. Computers & Operations Research 31(13), 2151–2164 (2004)
Koonce, D.A., Tsai, S.C.: Use data mining to find patterns in genetic algorithm solutions to a job shop schedule. Computers and Industrial Engineering 38, 361–374 (2000)
Amirthagadeswaran, K.S., Arunachalam, V.P.: Enhancement of performance of Genetic Algorithm for job shop scheduling problems through inversion operator. The International Journal of Advanced Manufacturing Technology 32, 780–786 (2007)
Vigo, D., Maniezzo, V.: A genetic/tabu thresholding hybrid algorithm for the process allocation problem. Journal of Heuristics 3(2), 91–110 (1997)
Etiler, O., Toklu, B., Atak, M., et al.: Agenetic algorithm for flow shop scheduling problems. Operational Research Society 55, 830–835 (2004)
Jia, H.Z., Fuh, J.Y.H., Nee, A.Y.C., et al.: Integration of genetic algorithm and Gantt chart for job shop scheduling in distributed manufacturing systems. Computers & Industrial Engineering 53(2), 313–320 (2007)
Mönch, L., Schabacker, R., Pabst, D., et al.: Genetic algorithm-based subproblem solution procedures for a modified shifting bottleneck heuristic for complex job shops. European Journal of Operational Research 177(3), 2100–2118 (2007)
Maravelias, C.T., Grossmannie: A hybrid MILP/CP decomposition approach for the contimuous time scheduling of multipurpose batch plants. Computers and Chemical Engineering 28(10), 1921–(1949)
Colorni, A., Dorigo, M., Maniezzo, V.: Distributed Optimization by Ant Colonies. In: Varela, F., Bourgine, P. (eds.) Proceedings of the First European Conference on Artificial Life, Paris,France, pp. 134–142. Elsevier Publishing (1991)
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Yabo, L., Shikun, Z., Feng, Z. (2014). An Improved Ants Colony Algorithm for NP-hard Problem of Travelling Salesman. In: Zu, Q., Vargas-Vera, M., Hu, B. (eds) Pervasive Computing and the Networked World. ICPCA/SWS 2013. Lecture Notes in Computer Science, vol 8351. Springer, Cham. https://doi.org/10.1007/978-3-319-09265-2_44
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DOI: https://doi.org/10.1007/978-3-319-09265-2_44
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09264-5
Online ISBN: 978-3-319-09265-2
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