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Currently, multi-objective optimization is an important problem in various fields. This paper proposes an innovative multi-objective flower pollination algorithm combined with Monte Carlo simulation (called MFPAMC). MFPAMC incorporates the following three technologies: the flower pollination algorithm (FPA), the Monte Carlo method (MC), and a sorting function. Initially, the FPA is used to search for the optimal solution, which improves the search efficiency. When the solution obtained by the FPA is no longer updated, MC simulation is adopted to further find the optimal solution. The sorting function prevents the loss of objective function information, and the result obtained is objective and practical. A test function is used to verify whether the combination of the FPA and MC simulation can improve the search accuracy. The results confirm that the search accuracy is improved. Finally, MFPAMC is used for empirical analysis, and the experimental results are compared with those of four other methods. The comparison results confirm that the optimized results are ideal and further indicate that MFPAMC has significant potential for practical application. [ABSTRACT FROM AUTHOR]

Currently, multi-objective optimization is an important problem in various fields. This paper proposes an innovative multi-objective flower pollination algorithm combined with Monte Carlo simulation (called MFPAMC). MFPAMC incorporates the following three technologies: the flower pollination algorithm (FPA), the Monte Carlo method (MC), and a sorting function. Initially, the FPA is used to search for the optimal solution, which improves the search efficiency. When the solution obtained by the FPA is no longer updated, MC simulation is adopted to further find the optimal solution. The sorting function prevents the loss of objective function information, and the result obtained is objective and practical. A test function is used to verify whether the combination of the FPA and MC simulation can improve the search accuracy. The results confirm that the search accuracy is improved. Finally, MFPAMC is used for empirical analysis, and the experimental results are compared with those of four other methods. The comparison results confirm that the optimized results are ideal and further indicate that MFPAMC has significant potential for practical application. [ABSTRACT FROM AUTHOR]