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An improved master-apprentice evolutionary algorithm for minimum independent dominating set problem

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Abstract

The minimum independent dominance set (MIDS) problem is an important version of the dominating set with some other applications. In this work, we present an improved master-apprentice evolutionary algorithm for solving the MIDS problem based on a path-breaking strategy called MAE-PB. The proposed MAE-PB algorithm combines a construction function for the initial solution generation and candidate solution restarting. It is a multiple neighborhood-based local search algorithm that improves the quality of the solution using a path-breaking strategy for solution recombination based on master and apprentice solutions and a perturbation strategy for disturbing the solution when the algorithm cannot improve the solution quality within a certain number of steps. We show the competitiveness of the MAE-PB algorithm by presenting the computational results on classical benchmarks from the literature and a suite of massive graphs from real-world applications. The results show that the MAE-PB algorithm achieves high performance. In particular, for the classical benchmarks, the MAE-PB algorithm obtains the best-known results for seven instances, whereas for several massive graphs, it improves the best-known results for 62 instances. We investigate the proposed key ingredients to determine their impact on the performance of the proposed algorithm.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61806050, 61972063, 61976050), the Fundamental Research Funds for the Central Universities (2412020FZ030, 2412019ZD013, 2412019FZ051), and Jilin Science and Technology Association (QT202005). Thanks Dr. Jianan Wang for offering the technical support of the computing server.

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Authors and Affiliations

  1. School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, China

    Shiwei Pan, Yiming Ma, Yiyuan Wang, Zhiguo Zhou, Jinchao Ji, Minghao Yin & Shuli Hu

  2. Key Laboratory of Applied Statistics of MOE, Northeast Normal University, Changchun, 130117, China

    Shiwei Pan, Yiming Ma, Yiyuan Wang, Zhiguo Zhou, Jinchao Ji, Minghao Yin & Shuli Hu

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  1. Shiwei Pan
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Correspondence to Zhiguo Zhou, Jinchao Ji or Minghao Yin.

Additional information

Shiwei Pan received the BS and MS degrees from the Department of Computer Science and Technology, Northeast Normal University, China in 2018 and 2021. His current research interests include heuristic search and combinatorial optimization.

Yiming Ma studied at the School of Computer Science and Information Technology, Northeast Normal University, China, with a master’s degree in Computer Science, and the research direction is heuristic search, local search, algorithmic design, and combinatorial optimization.

Yiyuan Wang is Associate Professor at School of Computer Science and Information Technology, Northeast Normal University, China. He received his PhD degree from Jilin University, China. His research interests include heuristic search, local search, algorithmic design, and combinatorial optimization.

Zhiguo Zhou is Associate Professor of Northeast Normal University and received the PhD degree from the College of Computer Science and Technology, Jilin University, China in 2008. His current research interests include algorithm design and analysis.

Jinchao Ji is a Lecturer at School of Information Science and Technology, Northeast Normal University, China. He received his MS and PhD degrees in Computer Application Technology from Jilin University, China in 2010 and 2013, respectively. His research interests include machine learning, data mining, and artificial intelligence.

Minghao Yin is Professor at School of Computer Science and Information Technology, Northeast Normal University, China. He received his PhD degree in Computer Software and Theory from Jilin University, China. His research interests include heuristic search, data mining, and combinatorial optimization.

Shuli Hu is a Lecturer at Northeast Normal University, China. She received the PhD degree from the Department of Computer Science and Technology, Northeast Normal University, China in 2019. Her current research interests include heuristic search and combinatorial optimization.

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Pan, S., Ma, Y., Wang, Y. et al. An improved master-apprentice evolutionary algorithm for minimum independent dominating set problem. Front. Comput. Sci. 17, 174326 (2023). https://doi.org/10.1007/s11704-022-2023-7

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