期刊
COMPUTATIONAL OPTIMIZATION AND APPLICATIONS
卷 78, 期 2, 页码 491-528出版社
SPRINGER
DOI: 10.1007/s10589-020-00251-6
关键词
Multi-objective optimization; Weight-set decomposition; Minimum spanning tree; Neighborhood search
资金
- FCT Foundation for Science and Technology, I.P. [CISUC UID/CEC/00326/2020]
- FCT [SFRH/BD/91647/2012]
- FCT under POCH program [SFRH/BSAB/139892/2018]
- DOME (Discrete Optimization Methods for Energy management) FCT Research Project [PTDC/CCI-COM/31198/2017]
- Fundação para a Ciência e a Tecnologia [PTDC/CCI-COM/31198/2017, SFRH/BD/91647/2012] Funding Source: FCT
This article introduces a new algorithm based on the connectedness property for computing the set of supported non-dominated points and corresponding efficient solutions for the multi-objective spanning tree problem. The algorithm utilizes decomposition of the weight set and adjacency relation in the decision space to determine efficient spanning trees and indifference regions. An in-depth computational analysis is presented for different types of networks with three objectives.
This article introduces a new algorithm for computing the set of supported non-dominated points in the objective space and all the corresponding efficient solutions in the decision space for the multi-objective spanning tree (MOST) problem. This algorithm is based on the connectedness property of the set of efficient supported solutions and uses a decomposition of the weight set in the weighting space defined for a parametric version of the MOST problem. This decomposition is performed through a space reduction approach until an indifference region for each supported non-dominated point is obtained. An adjacency relation defined in the decision space is used to compute all the supported efficient spanning trees associated to the same non-dominated supported point as well as to define the indifference region of the next points. An in-depth computational analysis of this approach for different types of networks with three objectives is also presented.
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