Due to the poor lighting conditions and the presence of a large amount of suspended dust in coal mines, obtained video has problems with uneven lighting and low differentiation of facial features. In order to address these problems, an improved image enhancement method is proposed. Firstly, the characteristics of underground coal mine images are analyzed, and median filtering is selected for noise removal. Then, the gamma function and fractional order operator are introduced, and an image enhancement algorithm based on particle swarm optimization is proposed. Finally, several experiments are conducted, and the results show that the proposed improved algorithm outperforms classical image enhancement algorithms, such as MSR, CLAHE and HF. Compared with the original image, the evaluation metrics of the enhanced Yale face images, including average local standard deviation, average gradient, information entropy and contrast, are improved by 113.1%, 63.8%, 22.8% and 24.1%, respectively. Moreover, the proposed algorithm achieves a superior enhancement effect in the simulated coal mine environment.

In order to improve the response characteristics of magnetorheological fluid brake (MRB) and reduce the braking fluctuation rate, an improved grey wolf optimization algorithm was proposed to adjust the parameters of the proportion integration differentiation (PID) controller. Firstly, an MRB system was designed and constructed, and its transfer function was determined. Moreover, by adopting the iterative method of logistic curve, an enhanced grey wolf optimization algorithm (EGWOA) was presented. Using the EGWOA, the parameters of the PID controller were optimized to improve the control performance of the system. Finally, the simulation and experiment were carried out. The results showed that EGWOA has a faster response output and overall better performance without overshoot compared with the conventional PID and grey wolf optimization algorithm (GWOA) PID controller.

To reduce engine vibration, a semi-active controlled magnetorheological suspension is designed. First, the magnetic circuit of the new magnetorheological suspension is simulated. Furthermore, the non-parametric forward and inverse dynamic models of the magnetorheological suspension with the Elman neural network are established, where the modeling results and accuracy are analyzed. Second, according to the characteristics of semi-active vibration control of engine suspension, an improved fruit fly optimized proportional-integral-differential (PID) controller is proposed. Compared with the original fruit fly optimized PID controller and particle swarm optimization PID controller, the control strategies based on the proposed methods are simulated by MATLAB. Then, a vibration control experiment is carried out under different control strategies. The simulation and experiment results show that the PID controller based on the improved fruit fly optimization algorithm has the advantages of short adjustment time and fast dynamic response.

Aiming at the inherent defects of the Horned Lizard Optimization Algorithm (HLOA), such as insufficient global exploration capability, premature convergence to local optima, and inadequate balance between exploration and exploitation, this paper proposes an enhanced Improved Horned Lizard Optimization Algorithm (IHLOA) integrated with multi-strategy improvements. Firstly, the Fuch chaotic mapping is introduced for population initialization, which enhances the ergodicity and diversity of the initial population by leveraging the pseudo-random and aperiodic characteristics of chaotic sequences, laying a high-quality foundation for subsequent optimization searches. Secondly, the golden sine strategy is embedded into the iterative update process to dynamically adjust the search step size and direction. This strategy utilizes the periodic amplitude variation in the sine function and the golden section coefficient to balance the global exploration for potential optimal regions and local exploitation for refined optimization, thereby accelerating convergence speed while avoiding local stagnation. Finally, the orthogonal crossover strategy is incorporated in the late iteration stage to promote effective information interaction between parent and offspring populations. By means of chromosome segment exchange and elitist retention mechanisms, this strategy reduces dimensional search blind spots and further enhances the algorithm’s ability to capture high-quality solutions. Comprehensive experimental evaluations are conducted based on classical benchmark test functions and eight state-of-the-art meta-heuristic algorithms. The results demonstrate that the IHLOA outperforms comparative algorithms in terms of optimization accuracy, convergence speed, and stability across 30-D, 50-D, and 80-D scenarios. For practical path planning applications, the IHLOA achieves remarkable performance improvements: in single-goal path planning, it reduces the path length by 2.54–87.64% compared with benchmark algorithms; in multi-goal path planning, it realizes a 1.24–7.99% reduction in path length and an 11.91% average reduction in the number of turning points relative to the original HLOA. Additionally, the IHLOA exhibits excellent robustness and adaptability in dynamic obstacle environments, effectively shortening the path length and reducing robot stuck times. This research not only enriches the improvement framework of meta-heuristic algorithms but also provides a high-efficiency optimization solution for mobile robot path planning in complex environments.

The Newton Downhill Optimizer (NDO) combines a derivative-free downhill relation with population differences. However, its Hybrid-Guided Operator uses a random reference and persistent best-solution guidance, which can cause directional fluctuations and premature population contraction. This study proposes the Adaptive Fitness–Distance-Guided Newton Downhill Optimizer (AFDNDO). Fitness–Distance Balance selection identifies guiding individuals that account for both solution quality and spatial diversity. Stage protection, elite protection, and historical success-rate feedback regulate activation of the improved branches. A tripodal heavy-tailed update and a wave-weighted masked differential update reconstruct the two original branches. A non-uniform mutation is also triggered for low-quality individuals when the global best value stagnates. Across 30 independent runs on CEC2017, CEC2020, and CEC2022, AFDNDO attained the lowest mean rank in all six formal benchmark configurations. Its mean ranks on the 10-, 30-, and 50-dimensional CEC2017 tests were 1.172, 1.241, and 1.276, respectively. Dynamic path-planning environments included rigid obstacles, three levels of soft-risk regions, and moving obstacles. In the single-target environments, AFDNDO–DWA achieved a 100% execution success rate without collisions. In the multi-target environments, it reduced the mean objective value by 4.46–6.64% relative to NDO. It also increased the success rate from 63.33% to 70.00% in the most constrained environment. These findings indicate that AFDNDO improves cross-landscape optimization performance while retaining the basic NDO framework. They also support its use as a global planner within the tested dynamic multi-task environments.