The Honey Badger Algorithm (HBA) is a recent addition to the growing family of nature-inspired optimization techniques within the domain of Swarm Intelligence (SI). Inspired by the intelligent foraging behavior of honey badgers, HBA has demonstrated remarkable effectiveness in solving complex optimization problems. This review provides an analysis of HBA, exploring its core principles, applications (particularly within the engineering domain), and the evolution of the algorithm through various modified and hybrid versions. The authors further leverage bibliometric analysis to unveil research trends and offer valuable insights into the current state of HBA research. Finally, this study provides insights for future advancements and applications in diverse problem-solving tasks while harnessing the HBA.

Fruit picking in complex orchard environments is limited by the low detection and recognition accuracy due to clustered background, illumination variation, and partial occlusion. To improve the picking performance, a hierarchical visual grasping architecture based on You Look Only Once (YOLO) algorithm and adaptive error compensation is proposed. The upper layer uses YOLOv5 and inverse kinematics to recognize and localize the target. Ant colony optimization is specifically used for hyperparameter tuning of YOLOv5 to improve the detection accuracy. The middle layer dynamically compensates for the output torque of the joint actuator through the feedback linearization method. The lower layer finishes precise grasping through nonlinear mapping model between the pulse width modulation signal and the servo angle. Experimental results validate that the proposed architecture outperforms traditional methods by reaching 98.2% and 97.7% recognition accuracy in obstacle-free scenarios and complex environments, a higher grasping success rate, and a lower positioning deviation.

This paper proposes a novel framework to combine deep learning-based pose estimation with Swarm Intelligence (SI) for real-time stage optimization. In details, a multi-stage convolutional neural network is first employed to extract accurate skeletal keypoints from live video feeds of dance performances, while temporal smoothing techniques mitigate noise and occlusion in complex choreographic scenarios. These pose data are then used by an SI optimizer to iteratively refine stage parameters—lighting color, brightness, projection content—under stringent low-latency requirements. By formulating an objective function that captures aesthetic, rhythmic, and latency considerations, the swarm adaptively converges on stage configurations that maintain strong synchronization with the dancer's motions and preserve artistic coherence. Extensive experiments on both curated datasets and real-stage recordings demonstrate that our method achieves substantially higher synchronization and aesthetic quality scores than static or rule-based approaches, while incurring manageable computational overhead.

The development of technology that enables network convergence and the rising acceptance of heterogeneous network architectures have made it possible for many of the most important cognitive radio networks to communicate with a broad variety of authorized networks. Traditional algorithms for network selection make use of selection approaches that are dependent on prior information about the network under consideration. We use the proposed algorithm to choose different networks and integrate them into computers. This paper offers a network selection technique that is based on deep reinforcement learning. This technique can be applied to cognitive radio networks that encompass a range of networks. We will utilize these findings to provide recommendations on how to remediate existing issues in the delivery of services to cognitive users. This paper aims to improve the service quality for cognitive users and find solutions to the identified problems.

This review paper delves into the original MRFO and its variants, focusing on single-objective algorithms including but not limited to hybrid algorithms, learning strategies, multiple populations and dynamic parameter adjustment, highlighting the improvements made to enhance the algorithm's efficiency in global optimization, accelerate convergence rates, and improve its capacity to evade local optima. MRFO has emerged as an effective tool for solving complex optimization problems across various domains, including energy optimization, biomedical field, engineering problems, and others. A comprehensive analysis of applications of MRFO in different fields is provided, emphasizing its adaptability and efficacy. The paper concludes with a discussion on the challenges faced by MRFO and potential future research directions, aiming to consolidate the current research status and guide future investigations.

In this article, a novel hybrid binary bat algorithm named HBBA is proposed for global optimization problems. First, to avoid simultaneous updating of bat velocity's dimensional components, i.e., elements of velocity vector, a random black hole model is modified to adapt to binary algorithm for updating in unknown spaces for each dimensional component individually. Through this way, the search ability of bats around the current group best is increased greatly. Second, a time-varying v-shaped transfer function, rather than a time-invariant one as in closely related works, is proposed to map velocity in continuous search space to a binary one. This accelerates the speed to switch individuals' positions, i.e., solutions in binary space. Third, a chaotic map is utilized to replace monotonous parameters in original binary bat algorithm, which is beneficial for avoiding premature convergence. Simulation results demonstrate the effectiveness of the proposed algorithm by three types of benchmark functions and unit commitment problem.

With the increase in the number of objectives, the number of non-dominated solutions will also increase sharply. The sorting method based on the traditional Pareto dominance is not sufficiently distinguishable from the solutions and cannot provide enough selection pressure when the population size is small. In this article, a new non-linear dominance (NLD) method is proposed. The main motivation of this method is from the perspective of storage solutions. The number of solutions is small and the difference between each component is as large as possible, so the part of the first quadrant, the second, and the fourth quadrant near the first quadrant becomes the dominant interval, except for the distance too far also defined as the dominant interval, for which construct a parabolic shape of the non-dominant interval. Based on this relationship, the authors propose a non-linear dominated many-objective evolutionary algorithm (NLDEA), which can solve the irregular Pareto front. Experiments show that NLDEA is competitive with the most advanced methods for various scalable benchmark problems.

Feature selection (FS) method is applied for extracting only the relevant information from the dataset. FS seemed to be an optimization concept because appropriate feature selection is the significant role of any classification problem. Similarly, feature weighting is employed to enhance the classification performance along with FS process. In this paper, feature selection and feature weighting has been performed by integrated an optimization algorithm called tunicate swarm genetic algorithm (TSGA) with deep residual network (DRN). TSGA is the combination of tunicate swarm algorithm (TSA) and genetic algorithm (GA) incorporated to increase the performance of the classifier. This wrapper method-based feature selection and feature weighting techniques are performed to reduce the computation time as well as complexity. The effectiveness of the proposed method is estimated and compared with different methods such as TSA, CS-GA, and PSO-GA. The performance of DRN classifier is also validated and compared to existing classifiers like KNN, C4.5, and RF.

A new classification framework for a Clinical Decision Support System, utilizing a Genetic algorithm and an Artificial Flora Optimized Neural Network is presented in this paper. GAFON is an artificial neural network whose topology is optimized with Genetic Algorithm and the learnable parameters are optimized with Artificial Flora Optimization algorithm. Drop out technique is used in the topology optimization phase and weight regularization is used in the parameter optimization phase. The proposed method minimizes the co-adaptation problem, reduces over-fitting of training data and improves the generalization of a feed forward neural network. The classification framework developed has been tested for classifying both multi class and binary class clinical datasets. The proposed method attained accuracy values of 86.82% for Hepatitis C Virus (HCV) for Egyptian patients, 84.91% for Vertebral Column 95.65% for Statlog Heart Disease (SHD), SHD and 93.79% for Early Stage Diabetes Risk Prediction (ESDRP), all datasets obtained from UCI repository