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1. Large-scale global optimization using cooperative coevolution with self-adaptive differential grouping | |||
FANG Wei,FANG Wei,MIN Ruigao,ZHOU Jianhong,ZHOU Jianhong,ZHOU Jianhong | |||
Computer Science and Technology 05 December 2018 | |||
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Abstract:Cooperative co-evolution (CC) is a popular evolutionary computation approach that can divide a large problem into a set of smaller sub-problems and solve them independently. CC has been an important divide-and-conquer algorithm for large-scale global optimization (LSGO) problems. Identification of variable interactions is the main challenge in CC to decompose the LSGO problems. Differential Grouping (DG) is a competitive variable grouping algorithm that can address the non-separable components of a continuous problem. As an improved version of DG, Global Differential Grouping (GDG) addresses the drawbacks of DG which are variables interactions missing during grouping and grouping performance sensitive to the threshold. In this paper, a Self-adaptive Differential Grouping (SDG) based on GDG is proposed in order to further improve the grouping accuracy on the CEC'2010 LSGO benchmark suite. The threshold for grouping in SDG can adjust adaptively along with the magnitude of different functions and is determined by only two points which is a randomly sampled point and its corresponding opposite point in the decision space. A self-adaptive pyramid allocation (SPA) strategy that can allocate different computational resource to subcomponents is also studied in this paper. The proposed algorithm, where SDG and SPA working with the optimizer $SaNSDE$ (CCSPA-SDG), is used to optimize the CEC'2010 LSGO benchmark suite. Experimental results show that SDG achieved ideal decomposition of the variables for all the CEC'2010 LSGO benchmark functions. The optimization performance of CCSPA-SDG also outperforms the state-of-the-art results. | |||
TO cite this article:FANG Wei,FANG Wei,MIN Ruigao, et al. Large-scale global optimization using cooperative coevolution with self-adaptive differential grouping[OL].[ 5 December 2018] http://en.paper.edu.cn/en_releasepaper/content/4746608 |
2. Solving the Fixed Spectrum Frequency Assignment Problem via Iterated Tabu Search | |||
Zhipeng L"u, Zhaojing Luo, Tao Ye | |||
Computer Science and Technology 03 December 2014 | |||
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Abstract:This paper presents an Iterated Tabu Search (ITS) algorithm for solving the fixed spectrum frequency assignment problem (FS-FAP), which integrates several distinguishing features, such as a novel dynamic tabu tenure mechanism and three kinds of perturbation operators. Computational results assessed on two sets of 47 public benchmark instances demonstrate the efficacy of the proposed ITS algorithm in terms of both solution quality and efficiency. Particularly, for the first set, ITS is able to find new best objective values for 16 instances while matching the previous best objective values for 21 ones, and for the COST 259 benchmark set, compared with reference algorithm, ITS obtains 2 better results in a reasonable time. Furthermore, several important ingredients of the ITS algorithm are also analyzed. | |||
TO cite this article:Zhipeng L"u, Zhaojing Luo, Tao Ye. Solving the Fixed Spectrum Frequency Assignment Problem via Iterated Tabu Search[OL].[ 3 December 2014] http://en.paper.edu.cn/en_releasepaper/content/4619809 |
3. The Algorithm for the Special Case of Two-sided SF-MNSA Problem | |||
LIU Nan,ZHU Da-Ming | |||
Computer Science and Technology 16 January 2013 | |||
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Abstract:Scaffold filling is a new combinatorial optimization problem in genome sequencing and can improve the integrity of the sequencing results.The two-sided Scaffold Filling to Maximize the Number of String Adjacencies(SF-MNSA) problem can be described as: given two incomplete gene sequences $A$ and $B$, respectively fill the missing genes into $A$ and $B$ such that the number of adjacencies between the resulting sequences $A'$ and $B'$ is maximized.The two-sided scaffold filling problem is NP-complete for genomes with duplicated genes and there is no effective approximation algorithm.In this paper, a new version problem is proposed that symbol # is added to each endpoint of each input sequence for any instance of two-sided SF-MNSA problem and a polynomial algorithm for the special instance of this new version problem is designed and proved. | |||
TO cite this article:LIU Nan,ZHU Da-Ming. The Algorithm for the Special Case of Two-sided SF-MNSA Problem[OL].[16 January 2013] http://en.paper.edu.cn/en_releasepaper/content/4512994 |
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