Only the smell perception rule is considered in the butterfly optimization algorithm (BOA), which is prone to falling into a local optimum. Compared with the original BOA, an extra operator, i.e., color perception rule, is incorporated into the proposed hybrid-flash butterfly optimization algorithm (HFBOA), which makes it more in line with the actual foraging characteristics of butterflies in nature. Besides, updating the strategy of the control parameters by the logistic mapping is used in the HFBOA for enhancing the global optimal ability. The performance of the proposed method was verified by twelve benchmark functions, where the comparison experiment results show that the HFBOA converges quicker and has better stability for numerical optimization problems, which are compared with six state-of-the-art optimization methods. Additionally, the proposed HFBOA is successfully applied to six engineering constrained optimization problems (i.e., tubular column design, tension/compression spring design, cantilever beam design, etc.). The simulation results reveal that the proposed approach demonstrates superior performance in solving complex real-world engineering constrained tasks.
The accelerometers are commonly applied to measure the vibrations in the fields of motion control and precision measurement, whose sensitivities are essentially important to their applications. The vibration calibration is utilized to determine their sensitivities before they are used or after a period of time. At present, the Nyquist sampling (NS), bandpass sampling (BPS), and mixer and low-pass filter sampling (MLPFS) based heterodyne laser interferometry are widely utilized to accomplish the vibration calibration. Compared with the NS method, the latter two methods can significantly reduce the sampling rate and extend the calibration frequency range. However, the BPS method has to adopt the complex algorithm and prior information so as to get its sampling rate, and the MLPFS method is inevitably influenced by an extra phase delay. In this article, a novel heterodyne laser interferometry is investigated to simultaneously determine the sensitivity magnitude and phase of the accelerometers with high accuracy in a wide frequency range. This method significantly eliminates the phase delay by introducing an appropriate symmetric differential demodulation strategy, which can improve the sensitivity phase calibration accuracy, especially at higher frequencies. The comparison experiments with the Earth's gravitation and monocular vision methods at low frequencies Manuscript
Complex optimization (CO) problems have been solved using swarm intelligence (SI) methods. One of the CO problems is the Wireless Sensor Network (WSN) coverage optimization problem, which plays an important role in Internet of Things (IoT). A novel hybrid algorithm is proposed, named hybrid particle swarm butterfly algorithm (HPSBA), by combining their strengths of particle swarm optimization (PSO) and butterfly optimization algorithm (BOA), for solving this problem. Significantly, the value of individual scent intensity should be non-negative without consideration of the basic BOA, which is calculated with absolute value of the proposed HPSBA. Moreover, the performance of the HPSBA is comprehensively compared with the fundamental BOA, numerous potential BOA variants, and tried-and-true algorithms, for solving the twenty-six commonly used benchmark functions. The results show that HPSBA has a competitive overall performance. Finally, when compared to PSO, BOA, and MBOA, HPSBA is used to solve the node coverage optimization problem in WSN. The experimental results demonstrate that the HPSBA optimized coverage has a higher coverage rate, which effectively reduces node redundancy and extends WSN survival time.
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