An Improved Genetic Algorithm for Solving Tri-level Programming Problems

When genetic algorithm is adopted to solve tri-level programming, many problems exist, such as controlling population size, jumping out of local optima, and avoiding low efficiency. An improved genetic algorithm with parallel strategy is proposed in this paper to solve tri-level programming, as well...

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Veröffentlicht in:2022 4th International Conference on Industrial Artificial Intelligence (IAI) S. 1 - 5
Hauptverfasser: Su, Kai, Lei, Zhili, Niu, Haiming
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 24.08.2022
Schlagworte:
Aerospace electronics
Artificial intelligence
Computational complexity
crowding distance
elite reserving
genetic algorithm
Numerical models
Programming
Sociology
Statistics
tri-level programming
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Zusammenfassung:When genetic algorithm is adopted to solve tri-level programming, many problems exist, such as controlling population size, jumping out of local optima, and avoiding low efficiency. An improved genetic algorithm with parallel strategy is proposed in this paper to solve tri-level programming, as well as elites reserving and fitness value crowding strategy. Simulations with numerical examples are done to prove correctness and effectiveness of the proposed algorithm.
DOI:10.1109/IAI55780.2022.9976878