This paper introduces a new hub-and-center transportation network problem for a new company competing against an operating company. The new company intends to locate p hubs and assign the center nodes to the located h...
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This paper introduces a new hub-and-center transportation network problem for a new company competing against an operating company. The new company intends to locate p hubs and assign the center nodes to the located hubs in order to form origin-destination pairs. It desires not only to maximize the total captured flow in the market but also aims to minimize the total transportation cost. Three competition rules are established between the companies which must be abided. According to the competition rules, the new company can capture the full percentage of the traffic in each origin-destination pair if its transportation cost for each route is significantly less than of the competitor. If its transportation cost for each route is not significantly less than one of the competitors, only a certain percentage of the traffic can be captured. A bi-objectiveoptimization model is proposed for the hub location, problem on hand under a competitive environment. As the problem is shown to be NP-hard, a novel meta-heuristic algorithm called multi-objective biogeography-based optimization is developed. As there is no benchmark in the literature, a popular non-dominated sorting algorithm is utilized to validate the results obtained. Moreover, to enhance the performance of the proposed Pareto-based algorithms, this paper intends to develop a binary opposition-based learning as a diversity mechanism for both algorithms. The algorithms are tuned to solve the problem, based on which their performances are compared, ranked, and analyzed statistically. Finally, the applicability of the proposed approach and the solution methodologies are demonstrated in three steps. (C) 2017 Elsevier B.V. All rights reserved.
To solve the problems that parameter can not be adjusted adaptively in most multi-focus image fusion algorithms, in this paper we propose a new algorithm based on nonsubsampled contourlet transform (NSCT) and multi-ob...
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ISBN:
(纸本)9781467316842;9781467346856
To solve the problems that parameter can not be adjusted adaptively in most multi-focus image fusion algorithms, in this paper we propose a new algorithm based on nonsubsampled contourlet transform (NSCT) and multi-objective biogeography-based optimization (MOBBO). Firstly, an improved bandpass directional subband fusion method in NSCT fusion framework is proposed by using a region-based fusion rule with consistency detection. Then, multiple objective fusion evaluation indicators are used as object functions and MOBBO algorithm is carried out by multi-objectiveoptimization search. The final resulting image is obtained through inverse transform. The experimental results show that the proposed algorithm has better objective evaluation and visual result than other exiting image fusion algorithms.
Lithium-ion (Li-ion) battery charging is a crucial issue in energy management of electric vehicles. Developing suitable charging patterns, while taking into account of various contradictory objectives and constraints ...
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Lithium-ion (Li-ion) battery charging is a crucial issue in energy management of electric vehicles. Developing suitable charging patterns, while taking into account of various contradictory objectives and constraints is a key but challenging topic in battery management. This paper develops a model based strategy that optimizes the charging patterns while considers various key parameters such as the charging speed, energy conversion efficiency as well as temperature variations. To achieve this, a battery model coupling both the electric and thermal characteristics is first introduced. Three key but conflicting objectives, including the charging time, energy loss and temperature rise especially for internal temperature, are formulated. Then, multi-objectivebiogeographybasedoptimization (M-BBO) approaches are employed to search the optimal charging patterns and to balance various objectives with different combinations. optimization results of four M-BBO approaches are compared, and the Pareto fronts for battery charging with various dual-objectives and triple-objectives are analysed in detail. Experimental results confirm that the developed strategy can offer feasible charging patterns and achieve a desirable trade-off among charging speed, energy conversion efficiency and temperature variations. The Pareto fronts obtained by this strategy can be adopted as references to adjust charging pattern to further satisfy various requirements in different charging applications.
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