In order to improve the performance of the sparrow search algorithm (SSA), in this paper, a novel series of SSA variants is proposed by combining SSA with improved Tent chaos mutation (IT), Lévy flights mutation (LF), elite opposition-based learning mutation (EOBL), variable radius mutation (VR) and the combination of IT, LF, EOBL, and VR, namely, ITSSA, LFSSA, EOBLSSA, VRSSA, and CMSSA, respectively. Initially, the performance of these variants is evaluated on a comprehensive set of 31 benchmark test functions. Moreover, the performance of the best algorithm among these variants is compared with 19 state-of-the-art optimization algorithms to validate its performance on 31 benchmark test functions. The convergence and computational complexity of the best variant are also analyzed to test exploration, exploitation, and local optima avoidance. It is then employed on eight real-world constrained engineering problems to further verify its robustness. The experimental results reveal that the best algorithm of SSA variants outperforms other competitors and is highly effective in solving real-life cases.

In order to solve the difficulty of compound fault diagnosis of rolling bearings, a novel rolling bearings fault diagnosis method based on improved tunable Q-factor wavelet transform (TQWT) is proposed in this paper. Firstly, a new evaluation index of signal decomposition called KR is defined by summing kurtosis and root mean square (RMS) with weight. KR is the compromise between impulse factor and energy factor, which can better represent the fault characteristics of sub-bands obtained by TQWT. Secondly, the KR is used to improve the TQWT. The improved TQWT can adaptively determine the parameters Q-factor and decomposition level. Thirdly, the bearing vibration signal is decomposed by the improved TQWT and the sub-bands are sorted descending according to the KR. Finally, the Hilbert envelope analysis is carried out and the fault types are determined by comparing the fault characteristic frequencies obtained from the Hilbert envelopes to the fault characteristic frequencies calculated by formula. The proposed fault diagnosis method is fully evaluated by simulation and experiments. The results demonstrate that the KR takes advantage of kurtosis and RMS and can be better used to optimize the parameter of TQWT. And the compound fault features of rolling bearings can be accurately separated into different sub-bands by the improved TQWT, which is helpful to improve the accuracy of compound fault diagnosis of rolling bearings.

Optimization challenges are becoming more complex as the world advances. Since deterministic and heuristic approaches are no longer sufficient to deal with such complex problems, metaheuristics have recently emerged as a viable option to address optimization difficulties. Since Sand Cat Swarm Optimization (SCSO) is a famous meta-heuristic algorithm, SCSO has a weak ability to balance search between exploration and exploitation and slow convergence, so it may not be effective in finding the global optima, particularly for complex problems. Hence, this paper proposes an intensified SCSO with multiple strategies (IMSCSO). The performance of the IMSCSO algorithm was evaluated on 23 standard test functions and test suites of CEC 2017, CEC 2019, and CEC 2020. Experimental results show that the IMSCSO algorithm performs significantly better than or is on par with other state-of-the-art optimizers. The statistical results obtained from the Wilcoxon signed-rank test and the Friedman test also indicate that the IMSCSO algorithm has a high ability to significantly outperform and rank first among all methods. Moreover, seven typical engineering issues were employed to estimate the efficacy of IMSCSO in optimizing constrained problems. The experimental findings show that the suggested IMSCSO method can efficiently handle real-world application issues.

Enhancing the operational reliability of rotary machinery relies significantly on the effective diagnosis of faults in rolling bearings. This study introduces an innovative method to improve the accuracy of fault diagnosis of rolling bearings during operation. First, we propose a sine empirical mode decomposition (SEMD) designed to effectively mitigate mode mixing and decompose the vibration signals of rolling bearings into a series of intrinsic mode functions. Subsequently, we constructed and optimized a kernel extreme learning machine classifier (KELMC) using the improved sparrow search algorithm (ISSA). Within ISSA, the opposition-based Learning method is refined and applied to enhance the optimization performance of the sparrow search algorithm. Finally, the paper presents a novel method for the fault diagnosis of rolling bearings based on SEMD and ISSA-KELMC, which can effectively extract the fault features and accurately recognize the fault types of rolling bearings by taking advantage of the SEMD and ISSA-KELMC. The effectiveness of the proposed method was verified through two simulation and fault diagnosis experiments. The results demonstrated the efficiency of the method in diagnosing faults in rolling bearings under both consistent and variable working conditions. This approach is valuable for fault diagnosis and condition monitoring of rotating machinery.

Recently, underwater acoustic sensor networks can be widely applied to various aspects of ocean monitoring. In these applications, one of the critical issues is to improve the coverage efficiency of the monitoring events with minimum energy consumption. Here, we show an energy-efficient sensor deployment strategy to achieve the optimal coverage for events. Inspired by the foraging behavior of krill herd, the movement pattern of krill herd is improved to apply to the sensors with distributed communication to reduce energy consumption. Meanwhile, the coverage threshold is considered to improve the convergence and combined with the information about the surrounding sensors and events to make each sensor move to the optimal position to achieve the optimal coverage with minimum energy consumption. Furthermore, two important performance indexes: 1) coverage efficiency and 2) energy consumption are analyzed to better evaluate the deployment strategy. Simulation results confirm that the proposed deployment strategy performs good energy efficiency and coverage efficiency.