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...

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Bibliographic Details
Published in:	Cognitive systems research Vol. 52; no. C; pp. 537 - 542
Main Authors:	Zhou, Yuhong, Su, Ke, Shao, Limin
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.12.2018 Elsevier
Subjects:	Chaos Hybrid optimization algorithm Particle swarm optimization (PSO) Chaos Particle swarm optimization (PSO) Hybrid optimization algorithm
ISSN:	1389-0417, 1389-0417
Online Access:	Get full text
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Summary:	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.
Bibliography:	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