Social learning-integrated flower pollination algorithm for influence maximization

Qiwen Zhang () and Yueyue Liu
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Qiwen Zhang: chool of Computer and Communication, Lanzhou University of Technology, Gansu, Lanzhou 730050, P. R. China
Yueyue Liu: chool of Computer and Communication, Lanzhou University of Technology, Gansu, Lanzhou 730050, P. R. China

International Journal of Modern Physics C (IJMPC), 2024, vol. 35, issue 03, 1-23

Abstract: Social learning-integrated flower pollination algorithm (SLFPA) is a solution to issues that meta-heuristic algorithms face when solving the influence maximization problem. These issues include the high probability of entrapment in local optima, a decrease in population diversity during later iterations, and low accuracy of solution. In human society, people often learn from others behavior. This mechanism of social learning is incorporated into the flower pollination algorithm. A global pollination strategy is devised to increase population diversity and avoid being trapped in local optima, which utilizes both the global optimal individual and the most improved individual. To enhance the accuracy of the algorithm, we have developed a local pollination strategy that involves creating a learning object based on close friends. We tested the proposed algorithm on six real social networks and compared it to six other advanced heuristic algorithms, and the results demonstrate the effectiveness of algorithm and improved the accuracy of the solution.

Keywords: Social networks; flower pollination algorithms; social learning; global optimization (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S012918312450030X

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