Golden jackal optimization (GJO) is an effective metaheuristic algorithm that imitates the cooperative hunting behavior of the golden jackal. However, since the update of the prey’s position often depends on the male golden jackal and there is insufficient diversity of golden jackals in some cases, it is prone to falling into a local optimal optimum. In order to address these drawbacks of GJO, this paper proposes an improved algorithm, called a hybrid GJO and golden sine (S) algorithm (Gold-SA) with dynamic lens-imaging (L) learning (LSGJO). First, this paper proposes novel dual golden spiral update rules inspired by Gold-SA. These rules give GJO the ability to think like a human (Gold-SA), making the golden jackal more intelligent in the process of preying, and improving the ability and efficiency of optimization. Second, a novel nonlinear dynamic decreasing scaling factor is introduced into the lens-imaging learning operator to maintain the population diversity. The performance of LSGJO is verified through 23 classical benchmark functions and 3 complex design problems in real scenarios. The experimental results show that LSGJO converges faster and more accurately than 11 state-of-the-art optimization algorithms, the global and local search ability has improved significantly, and the proposed algorithm has shown superior performance in solving constrained problems.

The sand cat is a creature suitable for living in the desert. Sand cat swarm optimization (SCSO) is a biomimetic swarm intelligence algorithm, which inspired by the lifestyle of the sand cat. Although the SCSO has achieved good optimization results, it still has drawbacks, such as being prone to falling into local optima, low search efficiency, and limited optimization accuracy due to limitations in some innate biological conditions. To address the corresponding shortcomings, this paper proposes three improved strategies: a novel opposition-based learning strategy, a novel exploration mechanism, and a biological elimination update mechanism. Based on the original SCSO, a multi-strategy improved sand cat swarm optimization (MSCSO) is proposed. To verify the effectiveness of the proposed algorithm, the MSCSO algorithm is applied to two types of problems: global optimization and feature selection. The global optimization includes twenty non-fixed dimensional functions (Dim = 30, 100, and 500) and ten fixed dimensional functions, while feature selection comprises 24 datasets. By analyzing and comparing the mathematical and statistical results from multiple perspectives with several state-of-the-art (SOTA) algorithms, the results show that the proposed MSCSO algorithm has good optimization ability and can adapt to a wide range of optimization problems.

Accurate traffic flow prediction is a key aspect of building data-driven intelligent transportation systems (ITSs) which relies on the Internet of Things (IoT) sensors deployed along roads, and dynamic spatial-temporal dependencies mining is a major area of interest in traffic flow prediction. Existing methods, however, overlook the diversities of traffic flow patterns from the perspectives of temporal and spatial dimensions. To this end, this article presents a multiview spatial-temporal adaptive transformer-GRU (MST-ATG) framework based on the encoder-decoder architecture to capture complex spatial-temporal dependencies from various perspectives. Specifically, a multiview embedding layer (MEL) containing original traffic data and spatial-temporal correlated features is designed to enrich the feature encoding. Then, based on the inherent characteristics of traffic flow, we introduce a periodicity-trend decomposition (PTD) method to fully consider the periodic- and trend-oriented features of time series. Finally, we propose a spatial-temporal adaptive transformer-GRU (ST-ATG) to dynamically extract spatial-temporal dependencies and adaptively choose computation steps in which a temporal adaptive stacked-GRU module (T-AGM) is proposed to extract correlations in temporal dimension and spatial dependencies captured by a spatial adaptive transformer module (S-ATM). Experimental results on six large-scale real-world datasets demonstrate that our MST-ATG framework outperforms the benchmarks in prediction accuracy. For instance, the average root-mean-square error of MST-ATG on PeMS08 is reduced by 48.3%, 41.09%, 12.95%, 17.67%, 18.64%, 2.4%, 14.67%, 9.15%, 1.1%, 2.4%, 2.51%, and 1.2% compared to that of autoregressive integrated moving average, long short-term memory (LSTM), DCRNN, STGCN, ASTGCN, GWNet, STSGCN, AGCRN, Bi-STAT, STAEformer, PDFormer, and STPGNN, respectively.

The reptile search algorithm is an effective optimization method based on the natural laws of the biological world. By restoring and simulating the hunting process of reptiles, good optimization results can be achieved. However, due to the limitations of natural laws, it is easy to fall into local optima during the exploration phase. Inspired by the different search fields of biological organisms with varying flight heights, this paper proposes a reptile search algorithm considering different flight heights. In the exploration phase, introducing the different flight altitude abilities of two animals, the northern goshawk and the African vulture, enables reptiles to have better search horizons, improve their global search ability, and reduce the probability of falling into local optima during the exploration phase. A novel dynamic factor (DF) is proposed in the exploitation phase to improve the algorithm’s convergence speed and optimization accuracy. To verify the effectiveness of the proposed algorithm, the test results were compared with ten state-of-the-art (SOTA) algorithms on thirty-three famous test functions. The experimental results show that the proposed algorithm has good performance. In addition, the proposed algorithm and ten SOTA algorithms were applied to three micromachine practical engineering problems, and the experimental results show that the proposed algorithm has good problem-solving ability.

The snow ablation optimizer (SAO) is a meta-heuristic technique used to seek the best solution for sophisticated problems. In response to the defects in the SAO algorithm, which has poor search efficiency and is prone to getting trapped in local optima, this article suggests a multi-strategy improved (MISAO) snow ablation optimizer. It is employed in the unmanned aerial vehicle (UAV) path planning issue. To begin with, the tent chaos and elite reverse learning initialization strategies are merged to extend the diversity of the population; secondly, a greedy selection method is deployed to retain superior alternative solutions for the upcoming iteration; then, the Harris hawk (HHO) strategy is introduced to enhance the exploitation capability, which prevents trapping in partial ideals; finally, the red-tailed hawk (RTH) is adopted to perform the global exploration, which, enhances global optimization capability. To comprehensively evaluate MISAO’s optimization capability, a battery of digital optimization investigations is executed using 23 test functions, and the results of the comparative analysis show that the suggested algorithm has high solving accuracy and convergence velocity. Finally, the effectiveness and feasibility of the optimization path of the MISAO algorithm are demonstrated in the UAV path planning project.