A New Two-Stage Evolutionary Algorithm for Many-Objective Optimization

Convergence and diversity are interdependently handled during the evolutionary process by most existing many-objective evolutionary algorithms (MaOEAs). In such a design, the degraded performance of one would deteriorate the other, and only solutions with both are able to improve the performance of...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE transactions on evolutionary computation Ročník 23; číslo 5; s. 748 - 761
Hlavní autoři:	Sun, Yanan, Xue, Bing, Zhang, Mengjie, Yen, Gary G.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.10.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Convergence Evolutionary algorithms Evolutionary computation Genetic algorithms Heuristic algorithms Many-objective evolutionary optimization algorithm Multiple objective analysis nadir point Optimization Pareto optimization Pareto optimum Pareto-optimal subspace Performance degradation Performance enhancement Performance evaluation Sociology Statistics Sun two-stage method
ISSN:	1089-778X, 1941-0026
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!