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A multi-objective lion swarm optimization based on multi-agent

  • *Corresponding author: Zhongqiang Wu

    *Corresponding author: Zhongqiang Wu 

The first author is supported by the Natural Science Foundation of Hebei Province under Grant F2020203014

Abstract / Introduction Full Text(HTML) Figure(4) / Table(1) Related Papers Cited by
  • This paper proposes a multi-objective lion swarm optimization based on multi-agent (MOMALSO) for solving the increasingly complex multi-objective optimization problem in engineering practice. First, the Multi-agent system is introduced into the lion swarm optimization (LSO) algorithm. The optimization mechanism of LSO and the information exchange between the agents are integrated to enhance the local search and global search ability of the algorithm, and the self-learning operation can accelerate the approximate Pareto front obtained by the algorithm near to real front. Besides, the external archive is introduced for extending the LSO into a multi-objective algorithm. Finally, the simulations compared with other three algorithms are performed, and the results show that MOMALSO has significant advantages in both convergence and coverage, which verifies the superiority and effectiveness of the algorithm in multi-objective optimization.

    Mathematics Subject Classification: Primary: 90C26, 90C59; Secondary: 30E1.


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  • Figure 1.  The topology structure of the MAS

    Figure 2.  Flow chart of update and maintenance of external archives

    Figure 3.  Obtained Pareto optimal solutions by 4 algorithms

    Figure 4.  IGD convergence curves of four algorithms

    Table 1.  Statistical results for IGD on UF1-UF10

    Avg. Std. Avg. Std. Avg. Std. Avg. Std.
    UF1 5.94e-3 2.02e-4 1.38e-2 1.27e-3 1.97e-2 2.50e-3 1.29e-2 1.24e-3
    UF2 7.14e-3 1.01e-3 9.26e-3 5.47e-4 2.03e-2 2.79e-3 9.70e-3 1.32e-3
    UF3 1.88e-2 2.10e-3 3.33e-2 1.16e-3 3.86e-2 4.84e-3 4.89e-2 5.28e-3
    UF4 5.16e-3 3.07e-4 7.45e-3 5.92e-4 8.81e-3 3.45e-4 1.03e-2 1.54e-4
    UF5 1.53e-1 4.26e-2 1.71e-1 3.35e-2 3.12e-1 2.76e-2 2.13e-1 4.86e-2
    UF6 3.65e-2 1.41e-3 3.64e-2 4.92e-4 7.87e-2 8.99e-3 4.17e-2 6.37e-3
    UF7 5.53e-3 2.78e-3 1.09e-2 3.34e-2 2.82e-2 4.12e-3 4.57e-2 2.02e-2
    UF8 3.29e-2 4.64e-3 3.67e-2 1.18e-2 8.59e-2 2.46e-2 6.17e-2 1.30e-2
    UF9 2.69e-2 4.64e-3 2.54e-2 3.16e-3 8.65e-2 1.04e-2 3.93e-2 2.72e-3
    UF10 2.67e-2 1.01e-2 7.49e-2 3.01e-2 4.21e-1 4.44e-2 1.21e-1 6.33e-2
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