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Learnable tabu search guided by estimation of distribution for maximum diversity problems

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Abstract

This paper presents a learnable tabu search (TS) guided by estimation of distribution algorithm (EDA), called LTS-EDA, for maximum diversity problem. The LTS-EDA introduces knowledge model and can extract knowledge during the search process of TS, and thus it adopts dual or cooperative evolution/search structure, consisting of probabilistic model space in clustered EDA and solution space searched by TS. The clustered EDA, as a learnable constructive method, is used to create a new starting solution, and the simple TS, as an improvement method, attempts to improve the solution created by the clustered EDA in the LTS-EDA. A distinguishing feature of the LTS-EDA is the usage of the clustered EDA with effective linkage learning to guide TS. In the clustered EDA, different clusters (models) focus on different substructures, and the combination of information from different clusters (models) effectively combines substructures. The LTS-EDA is tested on 50 large size benchmark problems with the size ranging from 2,000 to 5,000. Simulation results show that the LTS-EDA is better than the advanced algorithms proposed recently.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (60805026, 60905038, 61070076, 61033010), the Zhujiang New Star of Science and Technology in Guangzhou City (2011), and the Fundamental Research Funds for the Central Universities (10lgpy32).

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  1. Department of Computer Science, Sun Yat-sen University, Guangzhou Higher Education Mega Center, Guangzhou, 510006, China

    Jiahai Wang, Ying Zhou, Yiqiao Cai & Jian Yin

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  1. Jiahai Wang
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  2. Ying Zhou
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  3. Yiqiao Cai
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  4. Jian Yin
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Correspondence to Jiahai Wang.

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Wang, J., Zhou, Y., Cai, Y. et al. Learnable tabu search guided by estimation of distribution for maximum diversity problems. Soft Comput 16, 711–728 (2012). https://doi.org/10.1007/s00500-011-0780-6

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