A new chaotic hybrid cognitive optimization algorithm

To solve the optimization problems in port planning and operation management, particle swarm optimization, Cat mapping, and cloud model were combined. A Chaos Cloud Particle Swarm Optimization (CCPSO) algorithm was proposed. It was used in port planning management. Its application in port throughput...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Cognitive systems research Ročník 52; číslo C; s. 537 - 542
Hlavní autoři:	Zhou, Yuhong, Su, Ke, Shao, Limin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.12.2018 Elsevier
Témata:	Chaos Hybrid optimization algorithm Particle swarm optimization (PSO) Chaos Particle swarm optimization (PSO) Hybrid optimization algorithm
ISSN:	1389-0417, 1389-0417
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	To solve the optimization problems in port planning and operation management, particle swarm optimization, Cat mapping, and cloud model were combined. A Chaos Cloud Particle Swarm Optimization (CCPSO) algorithm was proposed. It was used in port planning management. Its application in port throughput forecasting and berthing and pontoon bridge allocation was explored and studied. By analyzing the mixed properties of Cat maps, the chaotic characteristics of the map were good. Thus, it was introduced into the hybrid optimization algorithm for chaotic perturbation of poor individuals in a particle swarm. The selection of the parameter combination of the Gauss-SVAR model was troublesome. The parameter combination of Guass-vSVR model was optimized by CCPSO algorithm, and the Guass-vSVR-CCPSO model was obtained. Using CCPSO algorithm, a discrete berth bridge allocation model was established. The results showed that the particle feasible integer processing module was developed. Therefore, a new method for multi-objective discrete berth shore-bridge allocation based on CCPSO algorithm is feasible.
Bibliografie:	USDOE Office of Electricity (OE), Advanced Grid Research & Development. Power Systems Engineering Research 16ZG014
ISSN:	1389-0417 1389-0417
DOI:	10.1016/j.cogsys.2018.08.001