Abstract
The theoretical research and application of vector optimization problem has a history of several decades. In the transportation network, there is usually two or more evaluation indexes, which is a problem that people often need to solve in real life. Therefore, it is of great theoretical value and practical significance to study the stability of optimization problems. The purpose of this paper is to study the stability of vector optimization problems. By using the adaptive genetic algorithm to optimize the traffic path vector, the local search ability of the adaptive genetic algorithm can be optimized to make it converge to the global optimal solution more quickly. Learning the basic theory of support vector machine algorithm, try to study the cuckoo search algorithm in support vector machine parameter optimization and algorithm improvement, and take MATLAB as the program platform to realize the method, and establish the stability identification model. The optimal path between the starting station and the target station is tested, and the stability of the traffic path vector optimization algorithm and the traffic path vector optimization is analyzed. The results show that the algorithm results are significantly different in terms of stability. The stability of genetic algorithm reaches 83%, while the particle swarm optimization algorithm is less than 60%.
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The work was supported by GJJ191330.
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Dong, X., Zhang, C., Zhang, L. (2021). The Stability of Vector Optimization Problems. In: Xu, Z., Parizi, R.M., Loyola-González, O., Zhang, X. (eds) Cyber Security Intelligence and Analytics. CSIA 2021. Advances in Intelligent Systems and Computing, vol 1342. Springer, Cham. https://doi.org/10.1007/978-3-030-70042-3_122
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DOI: https://doi.org/10.1007/978-3-030-70042-3_122
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