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Background Tumor metastasis often occurs in hepatocellular carcinoma (HCC) and influences the patient’s prognosis, and microRNAs are reported to play key roles in tumor metastasis. This study was conducted to explore the effect of microRNAs on HCC metastasis. Methods The levels of miR-181a in HCC tissues, adjacent tissues, metastatic HCC tissues, and non-metastatic HCC tissues at different stages were determined by qRT-PCR. Effect of miR-181a on the proliferation, invasion, and metastasis of HCC cells was estimated by cell counting kits-8 (CCK-8), wound-healing, and Transwell assays. Software analysis and luciferase assays were used to explore the target gene of miR-181a. Results MiR-181a was up-regulated in HCC tissues and its expression level in metastatic HCC tissues was much higher than in non-metastasis samples. PTEN was found to be a target gene of miR-181a. MiR-181a had multiple binding sites with the long non-coding RNA (lncRNA) XIST. The regulation of miR-181a on PTEN was mediated by lncRNA XIST. The proliferation and invasion of cells with siXIST were significantly enhanced compared with those of control cells, while knockdown of miR-181a abolished the enhancing effects. Conclusions MiR-181a can promote HCC metastasis by targeting PTEN, which is regulated by lncRNA XIST.

The Crested Porcupine Optimizer (CPO), as a newly emerging swarm intelligence algorithm, demonstrates advantages in balancing global exploration and local exploitation but still suffers from limitations in convergence speed and local exploitation precision. To address these issues, this paper proposes an enhanced variant, the Butterfly Search and Triangular Walk Crested Porcupine Optimizer (BTCPO). The method achieves a dynamic balance between exploration and exploitation by combining triangular walk to boost local exploitation and butterfly search to increase global variety. Experimental results on 23 classical benchmark functions and the CEC2021 test suite show that BTCPO outperforms CPO as well as seven state-of-the-art algorithms (DBO, HBA, BKA, HHO, GWO, GOOSE, and SSA). Specifically, BTCPO achieves the best performance on more than 80% of CEC2021 functions, with convergence speed improved by approximately 25% compared to CPO. Furthermore, BTCPO exhibits higher efficiency and usefulness in engineering design problems such as trusses, welded beams, and cantilever beams. These findings demonstrate the theoretical and practical advantages of BTCPO, making it a workable approach to solving difficult optimization problems.

The secretary bird optimization algorithm is a recently developed swarm intelligence method with potential for solving nonlinear and complex optimization problems. However, its performance is constrained by limited global exploration and insufficient local exploitation. To address these issues, an enhanced variant, ORSBOA, is proposed by integrating an optimal neighborhood perturbation mechanism with a reverse learning strategy. The algorithm is evaluated on the CEC2019 and CEC2022 benchmark suites as well as four classical engineering design problems. Experimental results demonstrate that ORSBOA achieves faster convergence, stronger robustness, and higher solution quality than nine state-of-the-art algorithms. Statistical analyses further confirm the significance of these improvements, validating the effectiveness and applicability of ORSBOA in solving complex optimization tasks.

To address the drawbacks of the traditional snake optimization method, such as a random population initialization, slow convergence speed, and low accuracy, an adaptive t-distribution mixed mutation snake optimization strategy is proposed. Initially, Tent-based chaotic mapping and the quasi-reverse learning approach are utilized to enhance the quality of the initial solution and the population initialization process of the original method. During the evolution stage, a novel adaptive t-distribution mixed mutation foraging strategy is introduced to substitute the original foraging stage method. This strategy perturbs and mutates at the optimal solution position to generate new solutions, thereby improving the algorithm’s ability to escape local optima. The mating mode in the evolution stage is replaced with an opposite-sex attraction mechanism, providing the algorithm with more opportunities for global exploration and exploitation. The improved snake optimization method accelerates convergence and improves accuracy while balancing the algorithm’s local and global exploitation capabilities. The experimental results demonstrate that the improved method outperforms other optimization methods, including the standard snake optimization technique, in terms of solution robustness and accuracy. Additionally, each improvement technique complements and amplifies the effects of the others.

In recent years, swarm intelligence optimization methods have been increasingly applied in many fields such as mechanical design, microgrid scheduling, drone technology, neural network training, and multi-objective optimization. In this paper, a multi-strategy particle swarm optimization hybrid dandelion optimization algorithm (PSODO) is proposed, which is based on the problems of slow optimization speed and being easily susceptible to falling into local extremum in the optimization ability of the dandelion optimization algorithm. This hybrid algorithm makes the whole algorithm more diverse by introducing the strong global search ability of particle swarm optimization and the unique individual update rules of the dandelion algorithm (i.e., rising, falling and landing). The ascending and descending stages of dandelion also help to introduce more changes and explorations into the search space, thus better balancing the global and local search. The experimental results show that compared with other algorithms, the proposed PSODO algorithm greatly improves the global optimal value search ability, convergence speed and optimization speed. The effectiveness and feasibility of the PSODO algorithm are verified by solving 22 benchmark functions and three engineering design problems with different complexities in CEC 2005 and comparing it with other optimization algorithms.

From collective intelligence to evolutionary computation and machine learning, symmetry can be leveraged to enhance algorithm performance, streamline computational procedures, and elevate solution quality. Grasping and leveraging symmetry can give rise to more resilient, scalable, and understandable algorithms. In view of the flaws of the original Sailfish Optimization Algorithm (SFO), such as low convergence precision and a propensity to get stuck in local optima, this paper puts forward an Osprey and Cauchy Mutation Integrated Sailfish Optimization Algorithm (OCSFO). The enhancements are mainly carried out in three aspects: (1) Using the Logistic map to initialize the sailfish and sardine populations. (2) In the first stage of the local development phase of sailfish individual position update, adopting the global exploration strategy of the Osprey Optimization Algorithm to boost the algorithm’s global search capability. (3) Introducing Cauchy mutation to activate the sailfish and sardine populations during the prey capture stage. Through the comparative analysis of OCSFO and seven other swarm intelligence optimization algorithms in the optimization of 23 classic benchmark test functions, as well as the Wilcoxon rank-sum test, it is evident that the optimization speed and convergence precision of OCSFO have been notably improved. To confirm the practicality and viability of the OCSFO algorithm, it is applied to solve the optimization problems of piston rods, three-bar trusses, cantilever beams, and topology. Through experimental analysis, it can be concluded that the OCSFO algorithm has certain advantages in solving practical optimization problems.

In wireless sensor networks, each sensor node has a finite amount of energy to expend. The clustering method is an efficient way to deal with the imbalance in node energy consumption. A topology optimization technique for wireless sensor networks based on the Cauchy variation optimization crow search algorithm (CM-CSA) is suggested to address the issues of rapid energy consumption, short life cycles, and unstable topology in wireless sensor networks. At the same time, a clustering approach for wireless sensor networks based on the enhanced Cauchy mutation crow search algorithm is developed to address the issue of the crow algorithm’s sluggish convergence speed and ease of falling into the local optimum. It utilizes the Cauchy mutation to improve the population’s variety and prevent settling for the local optimum, as well as to broaden the range of variation and the capacity to carry out global searches. When the leader realizes he is being followed, the discriminative probability is introduced to improve the current person’s location update approach. According to the simulation findings, the suggested CM-CSA algorithm decreases the network’s average energy consumption by 66.7%, 50%, and 33.3% and enhances its connectivity performance by 52.9%, 37.6%, and 23.5% when compared to the PSO algorithm, AFSA method, and basic CSA algorithm.

In recent years, swarm intelligence optimization algorithms have been proven to have significant effects in solving combinatorial optimization problems. Introducing the concept of evolutionary computing, which is currently a hot research topic, into swarm intelligence optimization algorithms to form novel swarm intelligence optimization algorithms has proposed a new research direction for better solving combinatorial optimization problems. The longhorn beetle whisker search algorithm is an emerging heuristic algorithm, which originates from the simulation of longhorn beetle foraging behavior. This algorithm simulates the touch strategy required by longhorn beetles during foraging, and achieves efficient search in complex problem spaces through bioheuristic methods. This article reviews the research progress on the search algorithm for longhorn beetles from 2017 to present. Firstly, the basic principle and model structure of the beetle whisker search algorithm were introduced, and its differences and connections with other heuristic algorithms were analyzed. Secondly, this paper summarizes the research achievements of scholars in recent years on the improvement of longhorn whisker search algorithms. Then, the application of the beetle whisker search algorithm in various fields was explored, including function optimization, engineering design, and path planning. Finally, this paper summarizes the research achievements of scholars in recent years on the improvement of the longhorn whisker search algorithm, and proposes future research directions, including algorithm deep learning fusion, processing of multimodal problems, etc. Through this review, readers will have a comprehensive understanding of the research status and prospects of the longhorn whisker search algorithm, providing useful guidance for its application in practical problems.

   The application of optimization theory and the algorithms that are generated from it has increased along with science and technology's continued advancement. Numerous issues in daily life can be categorized as combinatorial optimization issues. Swarm intelligence optimization algorithms have been successful in machine learning, process control, and engineering prediction throughout the years and have been shown to be efficient in handling combinatorial optimization issues. An intelligent optimization system called the chicken swarm optimization algorithm (CSO) mimics the organic behavior of flocks of chickens. In the benchmark problem's optimization process as the objective function, it outperforms several popular intelligent optimization methods like PSO. The concept and advancement of the flock optimization algorithm, the comparison with other meta-heuristic algorithms, and the development trend are reviewed in order to further enhance the search performance of the algorithm and quicken the research and application process of the algorithm. The fundamental algorithm model is first described, and the enhanced chicken swarm optimization algorithm based on algorithm parameters, chaos and quantum optimization, learning strategy, and population diversity is then categorized and summarized using both domestic and international literature. The use of group optimization algorithms in the areas of feature extraction, image processing, robotic engineering, wireless sensor networks, and power. Second, it is evaluated in terms of benefits, drawbacks, and application in comparison to other meta-heuristic algorithms. Finally, the direction of flock optimization algorithm research and development is anticipated.