A Study on Multi-objective Chaotic Evolution Algorithms Using Multiple Chaotic Systems

We investigate the optimization performance of multi-objective chaotic evolution (MOCE) algorithm with implementations using different chaotic systems. A comparison experiment of MOCE algorithms with four chaotic systems are employed in MOCE to analyse whether chaotic systems will affect the optimiz...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:International Conference on Awareness Science and Technology s. 1 - 6
Hlavní autoři: Wang, Zitong, Pei, Yan
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.10.2019
Témata:
chaos
Chaotic communication
chaotic evolution
chaotic system
evolutionary multi-objective optimization
Heuristic algorithms
Logistics
multi-objective chaotic evolution
Optimization
Sociology
Sorting
Statistics
ISSN:2325-5994
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:We investigate the optimization performance of multi-objective chaotic evolution (MOCE) algorithm with implementations using different chaotic systems. A comparison experiment of MOCE algorithms with four chaotic systems are employed in MOCE to analyse whether chaotic systems will affect the optimization performance of the MOCE algorithms. We analyze and discuss the performance of the MOCE algorithms implemented using different chaotic systems. Four chaotic systems are introduced in this work, i.e., the logistic map, the Hénon map, the tent map, and the Gauss map, respectively. The number of Pareto solution and the diversity of Pareto solution are two evaluation metrics to evaluate the performance of the multi-objective optimization algorithm. We apply the statistical tests to analyse and investigate the number of Pareto solution and their diversity. The evaluation results indicate that the MOCE with the logistic map has the best optimization performance in both the number of Pareto solution and their diversity. The statistical significance demonstrates that chaotic systems have a great influence on the optimization performance of MOCE algorithms.
ISSN:2325-5994
DOI:10.1109/ICAwST.2019.8923329