Abstract
The problem of aircraft scheduling is a typical scheduling problem, by abstracting the problem into a typical personnel on duty, the support aircraft is regarded as a person who need to be laid off, requiring that at any time, there are sufficient number of support aircraft in the patrol area to ensure the safe flight of special aircraft, but also to meet the constraints. In order to solve the problem of shift discharge efficiently, an adaptive genetic algorithm is proposed. Especially in the algorithm, the two-layer coding method of individual coding is introduced innovatively, in which the first layer of coding represents the take-off airport serial number and the second layer of coding represents the take-off aircraft serial number. The traditional individual coding method directly composes the take-off time into chromosomes, which makes it difficult to know the number of schedules, increases the complexity of calculation, and the direct use of take-off time coding cannot guarantee that the initialized individual meets the constraints, and too much non-feasible generation will seriously reduce the efficiency of iterative evolution. The problem can be effectively avoided by using the two-layer coding method of individual coding.
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References
Ren, Z., San, Y.: A hybrid optimized algorithm based on simplex method and genetic algorithm. In: 2006 6th World Congress on Intelligent Control and Automation, Dalian, pp. 3547–3551 (2006)
Haupt, R., Haupt, S.E.: The creative use of genetic algorithms. Computers evolve into the artistic realm. IEEE Potentials 19(2), 26–29 (2000)
Frenzel, J.F.: Genetic algorithms. IEEE Potentials 12(3), 21–24 (1993)
Haupt, R.L., Werner, D.H.: Anatomy of a genetic algorithm. In: Genetic Algorithms in Electromagnetics. IEEE (2007)
Abd-Alhameed, R.A., Zhou, D., See, C.H., Excell, P.S.: A wire-grid adaptive-meshing program for microstrip-patch antenna designs using a genetic algorithm [EM Programmer’s Notebook]. IEEE Antennas Propag. Mag. 51(1), 147–151 (2009)
Asafuddoula, M., Ray, T., Sarker, R.: A decomposition-based evolutionary algorithm for many objective optimization. IEEE Trans. Evol. Comput. 19(3), 445–460 (2015)
Li, S., Zhou, H., Hu, J., Ai, Q., Cai, C.: A fast path planning approach for unmanned aerial vehicles. Concurrency Comput. Pract. Experience 27 (2014). https://doi.org/10.1002/cpe.3291
Farouki, R.T.: The elastic bending energy of pythagorean-hodograph curves. Comput. Aided Geom. Des. 13(3), 227–241 (1996)
Carnahan, J., Sinha, R.: Nature’s algorithms [genetic algorithms]. IEEE Potentials 20(2), 21–24 (2001)
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Ding, J. (2020). Aircraft Scheduling Problems Based on Genetic Algorithms. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1159. Springer, Singapore. https://doi.org/10.1007/978-981-15-3425-6_25
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DOI: https://doi.org/10.1007/978-981-15-3425-6_25
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