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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 When the solution obtained by the FPA is no longer updated, MC simulation is adopted to further find the optimal solution.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 When the solution obtained by the FPA is no longer updated, MC simulation is adopted to further find the optimal solution.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]

A fault-tolerant control (FTC) scheme for a class of nonlinear systems with unmatched actuator redundancy and unmatched disturbances is proposed in this article. A methodology to construct unified smooth sliding mode control laws and update laws are proposed such that the equivalent injections of the first-order time derivatives of the unmatched actuator faults and unmatched disturbances can appear in the unmatched channels. The unmatched actuator faults and unmatched disturbances are completely canceled by these equivalent injections. Based on this methodology and using the backstepping design procedure, a set of smooth FTC sliding surfaces, FTC laws and update laws are then designed. With the help of the FTC law selecting mechanism, the output tracking errors of the closed-loop FTC system converge to zero asymptotically, and time-varying faults and disturbances are reconstructed. A simulation example is presented to illustrate the effectiveness of the proposed FTC method. [ABSTRACT FROM AUTHOR]