Research on Parameter Optimization of ant colony algorithm based on genetic algorithm
| Autor: | Li-hua Tao, Peng-tao Shi, Jun-feng Bai |
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| Rok vydání: | 2017 |
| Předmět: |
Artificial bee colony algorithm
Meta-optimization Rate of convergence Fitness proportionate selection Computer science TheoryofComputation_ANALYSISOFALGORITHMSANDPROBLEMCOMPLEXITY Ant colony optimization algorithms Crossover MathematicsofComputing_NUMERICALANALYSIS ComputingMethodologies_ARTIFICIALINTELLIGENCE Metaheuristic Algorithm Decoding methods |
| Zdroj: | Proceedings of the 23rd International Conference on Industrial Engineering and Engineering Management 2016 ISBN: 9789462392540 |
| Popis: | The performance and convergence rate of the ant colony algorithm is affected by the parameters of the ant colony algorithm, and the optimal parameters of ant colony algorithm often vary from problem to problem. On the basis of analyzing the current research status of ant colony optimization, a genetic algorithm based on ant colony optimization is proposed, which carries out the combination optimization of the parameters of the ant colony algorithm through the binary chromosome coding, roulette wheel selection operation, multi-point crossover operation, single point mutation, re-insert preserving good operation and the decoding process based on ant colony algorithm. Finally, taking the TSP-Oliver30 and the TSP-20 as examples, the validity of the algorithm is verified through the simulation experiments. |
| Databáze: | OpenAIRE |
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