Some living organisms with hierarchical structures in nature have received extensive attention in various fields. The hierarchical structure with multiple pores, a large number of solid–gas interfaces and tortuous conduction paths provide a new direction for the development of thermal insulation materials, making the living creatures under these extreme conditions become the bionic objects of scientific researchers. In this review, the research progress of bionic hierarchical structure in the field of heat insulation is highlighted. Polar bears, cocoons, penguin feathers and wool are typical examples of heat preservation hierarchy in nature to introduce their morphological characteristics. At the same time, the thermal insulation mechanism, fractal model and several preparation methods of bionic hierarchical structures are emphatically discussed. The application of hierarchical structures in various fields, especially in thermal insulation and infrared thermal stealth, is summarised. Finally, the hierarchical structure is prospected.

Cutting tools are known as the “productivity” of the manufacturing industry, which affects the production efficiency and quality of the workpiece, and has become the focus of research and attention in academia and industry. However, traditional cutting tools often suffer from adhesion or wear during the cutting process, which considerably reduces the cutting efficiency and service life of the tools, and makes it difficult to meet current production requirements. To solve the above problems, scholars have introduced bionics into the tool’s design, applying the microscopic structure of the biological surface to the tool surface to alleviate the tool’s failure. This paper mainly summarizes the research progress of bionic textured cutting tools. Firstly, categorize whether the bionic texture design is inspired by a single organism or multiple organisms. Secondly, it is discussed that the non-smooth surface of the biological surface has five characteristics: hydrophilic lubricity, wear resistance, drag reduction and hydrophobicity, anti-adhesion, and arrangement, and the non-smooth structure of these different characteristics are applied to the surface of the tool is designed with bionic texture. Furtherly, the cutting performance of bionic textured cutting tools is discussed. The anti-friction and wear-resisting mechanism of bionic textured cutting tools is analyzed. Finally, some pending problems and perspectives have been proposed to provide new inspirations for the design of bionic textured cutting tools.

Bamboo is a typical biological material widely growing in nature with excellent physical and mechanical properties. It is lightweight with high strength and toughness. The naturally optimized bamboo structure, which has inspired global material scientists and engineers for decades, is significantly important for the bionic design of novel structural materials with ultra-light, ultra-strong, or ultra-tough and comprehensive properties. Typical literature on innovative composite materials and structural members inspired by bamboo are reviewed in this paper, and the research progress and prospects in this field are expounded in three parts. First, the structural characteristics of the bamboo wall layer along the thickness and height directions are described in terms of chemical composition, gradient structure, pore structure, and hollow structure with variable cross-section. Second, this paper summarizes the research progress on new composite materials and structural components by applying bamboo’s structural features from the perspective of sustainability, designability, and customization. Finally, given the limitations of current research, the biomimetic scientific research on bamboo’s structural characteristics is prospected from the interpretation of bamboo structure, new bamboo-like materials, and structural design optimization perspectives, providing a reference for future research on biomimetic aspects of biomass.

In this paper, inspired by lotus leaf surfaces, we fabricated biomimetic multi-scale micro-nano-structures by Two-Step Capillary Force Lithography (TS-CFL) and UV-assisted Capillary Force Lithography (UV-CFL). The experimental results indicated that TS-CFL was unfitted to fabricate large-area multi-scale micro-nano-structures. Conversely, UV-CFL can fabricate large-area multi-scale micro-nano-structures. We discussed the hydrophobic and anti-icing properties of the biomimetic surfaces fabricated by these two technologies. We found that small structures are significant for improving the hydrophobic anti-icing properties of single-structured or structureless surfaces. We believe that these results can complement the experimental details of both technologies and enable the development of more interesting micro-nano-structures biomimetic surfaces by both technologies in the future.

Compliance motion and footstep adjustment are active balance control strategies from learning human subconscious behaviors. The force estimation without direct end-actuator force measurement and the optimal footsteps based on complex analytical calculation are still challenging tasks for elementary and kid-size position-controlled robots. In this paper, an online compliant controller with Gravity Projection Observer (GPO), which can express the external force condition of perturbations by the estimated Projection of Gravity (PoG) with estimation covariance, is proposed for the realization of disturbance absorption, with which the robustness of the humanoid contact with environments can be maintained. The fuzzy footstep planner based on capturability analysis is proposed, and the Model Predictive Control (MPC) is applied to generate the desired steps. The fuzzification rules are well-designed and give the corresponding control output responding to complex and changeable external disturbances. To validate the presented methods, a series of experiments on a real humanoid robot are conducted. The results verify the effectiveness of the proposed balance control framework.

Aiming at the environment such as ravines and obstacles that may be encountered in the actual movement, this paper proposes a method for optimizing the bounding and jumping motion based on the ground touching force trajectory and the air motion trajectory of the quadruped robot. The method of optimizing the ground reaction force according to the speed of the demand and the height of the jump, and adjusting the stance and swing time according to the relationship of dynamics and momentum conservation. At the same time, under the constraints of dynamics and energy consumption of the robot system, considering the jumping distance and height, a method for optimizing the air trajectory of bounding and jumping is proposed. State switching and landing stability control are also added. Finally, the experimental results show that the quadruped robot has strong bounding and jumping ability, and has achieved stable bounding movement and forward jump movement of 0.8 m.

There are many theories and tools for human or robot motion simulation, but most of them require complex calculations. The LNZN model (a simplified model named by the proposers) simplifies the human model and facilitates simulation of the Ground Reaction Force (GRF) of body landing by spring damping model and ignoring joint rotation movements, which can reduce the amount of computation obviously. In this paper, the LNZN model of human running is selected as the basis and is modified to obtain the LNZN model of a robot, which expands the application of the LNZN model. According to the structure of the human foot, a foot structure is then added to the simplified model to reduce the GRF. We also applied driving forces to the new model to simulate the whole high jump motion of the robot to expand the functions of the LNZN model. The obtained GRF data were anastomotic to the actual experimental results. In addition, the effects of variables, such as the mass, hardness, and damping, of the foot on the GRF at the moment of landing were also explored. Finally, based on the guidelines obtained for the design of the robot’s foot structure, we fabricated new robot’s feet and installed them on the actual robot and achieved a better cushioning effect than the original foot in experiments.

Quadruped animals in the nature realize high energy efficiency locomotion by automatically changing their gait at different speeds. Inspired by this character, an efficient adaptive diagonal gait locomotion controller is designed for quadruped robot. A unique gait planning method is proposed in this paper. As the speed of robot varies, the gait cycle time and the proportion of stance and swing phase of each leg are adjusted to form a variety of gaits. The optimal joint torque is calculated by the controller combined with Virtual Model Control (VMC) and Whole-Body Control (WBC) to realize the desired motion. The gait and step frequency of the robot can automatically adapt to the change of speed. Several experiments are done with a quadruped robot made by our laboratory to verify that the gait can change automatically from slow-trotting to flying-trot during the period when speed is from 0 to 4 m/s. The ratio of swing phase is from less than 0.5 to more than 0.5 to realize the running motion with four feet off the ground. Experiments have shown that the controller can indeed consume less energy when robot runs at a wide range of speeds comparing to the basic controller.

With the increasing size of space facilities, on-orbit assembly requires robots to move on different heights of trusses. This paper proposes a bio-inspired attachment mechanism for robot feet to enable climbing on different heights of trusses. Inspired by the attachment and grasping abilities of Dynastes Hercules, we utilize its foot microstructures, such as microhooks and setae, to achieve efficient contact and firm grip with the surface. The morphology and arrangement of these structures can inspire the design of robot feet to improve their grasping and stability performance. We study the biological structure of Dynastes Hercules, design and optimize the bio-inspired structure, analyze the influence of various factors from theoretical and experimental perspectives, and verify the feasibility of the scheme through simulation. We propose an ideal climbing strategy that provides useful reference for robot applications in practice. Moreover, the influence laws of various factors in this paper can be applied to robot foot design to improve their operation ability and stability performance in the space environment. This bio-inspired mechanism can improve robot working range and efficiency, which is critical for on-orbit assembly in space.

A variety of soft wall-climbing robots have been developed that can move in certain patterns. Most of these soft robots can only move on conventional surfaces and lack adaptability to complex surfaces. Improving the adaptability of soft robots on complex surfaces is still a challenging problem. To this end, we study the layered structure of the starfish tube foot and the valve flap structure in the water vascular system, and use an ultrasonic stress detector to study the stiffness distribution of the arm structure. Inspired by the motion of the starfish, we present a bionic soft wall-climbing robot, which is driven by two groups of pneumatic feet and achieves body bending through active adaptation layers. We design the structure of the foot to flex to provide driving force, and there are suction cups at the end of the foot to provide suction. The soft foot has a simple structure design, adapts to a variety of surfaces, and does not damage the surface of the substrate. Variable stiffness layers achieve stiffness changes by the principle of line blocking. The Central Pattern Generator theory is introduced to coordinately control the multiple feet of the robot. After experiments, we verify the adaptability of the soft robot to curved surfaces. The research may provide a reference for the design and development of crawling soft robots on complex surfaces.

This paper focuses on a newly developed transmission for a milli-scale eight-legged crawling robot called OriSCO. The transmission allows intuitive steering by directly changing the direction of the propulsion force. The transmission is based on the constrained spherical six-bar linkage. The constrained spherical six-bar linkage passes only reciprocating motion out of the motor’s rotating motion, allowing the crawling legs to kick the ground and obtain propulsion. Steering is achieved by adjusting the geometric constraints of the spherical six-bar using a servomotor, allowing the direction of propulsion to be changed. As a result, the OriSCO can move along the ground at a speed of 2.15 body lengths/s, and the robot is 60 mm long.

Fish in nature exhibit a variety of swimming modes such as forward swimming, backward swimming, turning, pitching, etc., enabling them to swim in complex scenes such as coral reefs. It is still difficult for a robotic fish to swim autonomously in a confined area as a real fish. Here, we develop an untethered robotic manta as an experimental platform, which consists of two flexible pectoral fins and a tail fin, with three infrared sensors installed on the front, left, and right sides of the head to sense the surrounding obstacles. To generate multiple swimming modes of the robotic manta and online switching of different modes, we design a closed-loop Central Pattern Generator (CPG) controller based on distance information and use a combination of phase difference and amplitude of the CPG model to achieve stable and rapid adjustment of yaw angle. To verify the autonomous swimming ability of the robotic manta in complex scenes, we design an experimental scenario with a concave obstacle. The experimental results show that the robotic manta can achieve forward swimming, backward swimming, in situ turning within the concave obstacle, and finally exit from the area safely while relying on the perception of external obstacles, which can provide insight into the autonomous exploration of complex scenes by the biomimetic robotic fish. Finally, the swimming ability of the robotic manta is verified by field tests.

This paper is based on a previously developed bio-inspired Flapping Wing Aerial Vehicle (FWAV), RoboFalcon, which can fly with a morphing-coupled flapping pattern. In this paper, a simple flapping stroke control system based on Hall effect sensors is designed and applied, which is capable of assigning different up- and down-stroke speeds for the RoboFalcon platform to achieve an adjustable downstroke ratio. The aerodynamic and power characteristics of the morphing-coupled flapping pattern and the conventional flapping pattern with varying downstroke ratios are measured through a wind tunnel experiment, and the corresponding aerodynamic models are developed and analyzed by the nonlinear least squares method. The relatively low power consumption of the slow-downstroke mode of this vehicle is verified through outdoor flight tests. The results of wind tunnel experiments and flight tests indicate that increased downstroke duration can improve aerodynamic and power performance for the RoboFalcon platform.

Bioinspired flapping-wing micro-air-vehicles (FWMAVs) have the potential to be useful aerial tools for gathering information in various environments. With recent advancements in manufacturing technologies and better understanding of aerodynamic mechanisms behind of the flapping flight, outdoor flights have become a reality. However, to fully realize the potential of FWMAVs, further improvements are necessary, particularly in terms of stability and robustness under gusty conditions. In this study, the response and control of a tailless two-winged FWMAV under the wind gusts are investigated. Physical experiments are conducted with a one-degree-of-freedom gimbal to focus on effects of wind gusts on the rotational motion of the FWMAV. Proportional-derivative and sliding-mode controls are adopted for the attitude control. Results present that the body angles changed in time and reached approximately 50[Math Processing Error]° at the maximum due to the wing gusts. The sliding-mode controller can more effectively control the rotational angle in the presence of disturbances and both the wing speed and changes in wind speed have an impact on the effectiveness of attitude control. These results contribute to the development of of tailless two-winged, single-motor driven FWMAVs in terms of the design of attitude controller and testing apparatus.

Lower Limb Exoskeletons (LLEs) are receiving increasing attention for supporting activities of daily living. In such active systems, an intelligent controller may be indispensable. In this paper, we proposed a locomotion intention recognition system based on time series data sets derived from human motion signals. Composed of input data and Deep Learning (DL) algorithms, this framework enables the detection and prediction of users’ movement patterns. This makes it possible to predict the detection of locomotion modes, allowing the LLEs to provide smooth and seamless assistance. The pre-processed eight subjects were used as input to classify four scenes: Standing/Walking on Level Ground (S/WOLG), Up the Stairs (US), Down the Stairs (DS), and Walking on Grass (WOG). The result showed that the ResNet performed optimally compared to four algorithms (CNN, CNN-LSTM, ResNet, and ResNet-Att) with an approximate evaluation indicator of 100%. It is expected that the proposed locomotion intention system will significantly improve the safety and the effectiveness of LLE due to its high accuracy and predictive performance.

This paper proposes a new upper-limb exoskeleton to reduce worker physical strain. The proposed design is based on a novel PRRRP (P-Prismatic; R-Revolute) kinematic chain with 5 passive Degrees of Freedom (DoF). Utilizing a magnetic spring, the proposed mechanism includes a specially designed locking mechanism that maintains any desired task posture. The proposed exoskeleton incorporates a balancing mechanism to alleviate discomfort and spinal torsional effects also helping in limb weight relief. This paper reports specific models and simulations to demonstrate the feasibility and effectiveness of the proposed design. An experimental characterization is performed to validate the performance of the mechanism in terms of forces and physical strain during a specific application consisting of ceiling-surface drilling tasks. The obtained results preliminarily validate the engineering feasibility and effectiveness of the proposed exoskeleton in the intended operation task thereby requiring the user to exert significantly less force than when not wearing it.

Lower limb motion recognition techniques commonly employ Surface Electromyographic Signal (sEMG) as input and apply a machine learning classifer or Back Propagation Neural Network (BPNN) for classifcation. However, this artifcial feature engineering technique is not generalizable to similar tasks and is heavily reliant on the researcher’s subject expertise. In contrast, neural networks such as Convolutional Neural Network (CNN) and Long Short-term Memory Neural Network (LSTM) can automatically extract features, providing a more generalized and adaptable approach to lower limb motion recognition. Although this approach overcomes the limitations of human feature engineering, it may ignore the potential correlation among the sEMG channels. This paper proposes a spatial–temporal graph neural network model, STGNN-LMR, designed to address the problem of recognizing lower limb motion from multi-channel sEMG. STGNN-LMR transforms multi-channel sEMG into a graph structure and uses graph learning to model spatial–temporal features. An 8-channel sEMG dataset is constructed for the experimental stage, and the results show that the STGNN-LMR model achieves a recognition accuracy of 99.71%. Moreover, this paper simulates two unexpected scenarios, including sEMG sensors afected by sweat noise and sudden failure, and evaluates the testing results using hypothesis testing. According to the experimental results, the STGNN-LMR model exhibits a signifcant advantage over the control models in noise scenarios and failure scenarios. These experimental results confrm the efectiveness of the STGNN-LMR model for addressing the challenges associated with sEMG-based lower limb motion recognition in practical scenarios.

A machine learning model for regression of interrupted Surface Electromyography (sEMG) signals to future control-oriented signals (e.g., robot’s joint angle and assistive torque) of an active biomechatronic device for high-level myoelectric-based hierarchical control is proposed. A Recurrent Neural Network (RNN) was trained using output data, initially obtained from ofine optimization of the biomechatronic (human–robot) device and shifted by the prediction horizon. The input of the RNN consisted of interrupted sEMG signals (to mimic signal disconnections) and previous kinematic signals of the assistive system. The RNN with a 0.1-s prediction horizon could predict the control-oriented joint angle and assistive torque with 92% and 86.5% regression accuracy, respectively, for the test dataset. This proposed approach permits a fast, predictive, and direct estimation of control-oriented signals instead of an iterative process that optimizes assistive torque in the inverse dynamic simulation of a multibody human–robot system. Training with these interrupted input signals signifcantly improves the regression accuracy in the case of sEMG signal disconnection. This Robust Predictive Control-oriented Machine Learning (Robust-MuscleNET) model can support volitional high-level myoelectric-based control of biomechatronic devices, such as exoskeletons, prostheses, and assistive/resistive robots. Future work should study the application to prosthesis control as well as the repeatability of the high-level controller with electrode shift. The low-level hierarchical controller that manages the human–robot interaction, the assistance/resistance strategy, and the actuator coordination should also be studied.

Artifcial intelligence is currently achieving impressive success in all felds. However, autonomous navigation remains a major challenge for AI. Reinforcement learning is used for target navigation to simulate the interaction between the brain and the environment at the behavioral level, but the Artifcial Neural Network trained by reinforcement learning cannot match the autonomous mobility of humans and animals. The hippocampus–striatum circuits are considered as key circuits for target navigation planning and decision-making. This paper aims to construct a bionic navigation model of reinforcement learning corresponding to the nervous system to improve the autonomous navigation performance of the robot. The ventral striatum is considered to be the behavioral evaluation region, and the hippocampal–striatum circuit constitutes the position–reward association. In this paper, a set of episode cognition and reinforcement learning system simulating the mechanism of hippocampus and ventral striatum is constructed, which is used to provide target guidance for the robot to perform autonomous tasks. Compared with traditional methods, this system refects the high efciency of learning and better Environmental Adaptability. Our research is an exploration of the intersection and fusion of artifcial intelligence and neuroscience, which is conducive to the development of artifcial intelligence and the understanding of the nervous system.

In this study, diferent conditions of sandblasting on dental implant fxtures were investigated to achieve the best sandblasting conditions. 18 diferent sandblasting conditions (Using 152 implant fxture samples) were examined, including parameters such as particle size, particle blasting pressure, and particle blasting angle. The surface treatment of the samples was performed using the SLA+Anodizing method. AFM testing was performed for each of the 18 diferent states, and the average surface roughness of each of these was compared with each other. Then, a bone layer was placed on the sample with the closest average surface roughness to the standard and the least amount of aluminum oxide on its surface among the 18 different states, to confrm the accuracy and quality of the desired surface roughness by examining the bone formation process and speed. The results showed that state No. 4 (sandblast particle size: 75 μm, spraying pressure of sandblast particles: 4 bar, sandblast particle spraying angle: 30 degrees), which was prepared using the SLA+Anodizing method and had a surface roughness of 1.989 μm (The percentage of Al2O3 on the surface=6%), had the best sandblasting conditions and showed 95% cell viability and accelerated the treatment and bone formation process for about a week. The simulation results, using Abaqus software, indicated that the stress distribution on the surface of the implant fxture in contact with the bone surface has increased by approximately 4.3% for state No. 4. This will help prevent loosening of the dental implant fxture over time.

The rotor is the most important component of rotating machinery, and the vibration produced by its mass unbalance has a serious infuence on the secure and steady operation of the machine, so an efective online suppression technology is urgently needed. A new hydraulic unbalanced bionic self-recovery system is introduced, imitating the way of manually repairing faulty equipment. To accomplish the efect of actuator mass redistribution, the technology employs pressurized air to drive the quantitative transfer of liquid in the reservoir cavity at opposite positions. It can complete the online adjustment of the equipment’s balancing state and suppress the unbalanced vibration of equipment in real time, which gives the equipment the ability to maintain an autonomous health state and improve equipment performance. The composition and working principle of the system are introduced in detail, and the key performance parameters, such as the minimum running speed and the balancing liquid transfer speed, are analyzed theoretically. The fuid–solid coupling model of the actuator was established, and the two-phase fow from inside the hydraulic unbalanced bionic self-recovery actuator was simulated under multiple working conditions and the performance parameters were quantitatively analyzed. A balancing simulation test bed was built, and its efectiveness was verifed by performance parameter tests and unbalanced bionic self-recovery experiments. The experimental results show that the mass distribution adjustment of the balancing disk can be achieved using diferent viscosity balancing liquid, and the response of liquid viscosity 10 cSt is faster than that of liquid viscosity 100 cSt in the process of balancing liquid transfer, and the time is reduced by more than 75%; the system can reduce the simulated rotor amplitude from 18.3 μm to 10.6 μm online in real time, which provides technical support for the subsequent development of a new generation of bionic intelligent equipment.

There is a bottleneck in the design of vehicle sound that the subjective perception of sound quality that combines multiple psychological factors fails to be accurately and objectively quantifed. Therefore, EEG signals are introduced in this paper to investigate the evaluation and design method of vehicle acceleration sound with powerful sound quality. Firstly, the experiment of EEG acquisition and subjective evaluation under the stimulation of powerful vehicle sounds is conducted, respectively, then three physiological EEG features of PSD_β, PSD_γ and DE are constructed to evaluate the vehicle sounds based on the correlation analysis algorithms. Subsequently, the Adaptive Genetic Algorithm (AGA) is proposed to optimize the Elman model, where an intelligent model (AGA–Elman) is constructed to objectively predicate the perception of subjects for the vehicle sounds with powerful sound quality. The results demonstrate that the error of the constructed AGA–Elman model is only 2.88%, which outperforms than the traditional BP and Elman model; Finally, two vehicle acceleration sounds (Design1 and Design2) are designed based on the constructed AGA–Elman model from the perspective of order modulation and frequency modulation, which provide the acoustic theoretical guidance for the design of vehicle sound incorporating the EEG signals.

Over the past years, many eforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world problems. This study presents a new optimization method based on an unusual geological phenomenon in nature, named Geyser inspired Algorithm (GEA). The mathematical modeling of this geological phenomenon is carried out to have a better understanding of the optimization process. The efciency and accuracy of GEA are verifed using statistical examination and convergence rate comparison on numerous CEC 2005, CEC 2014, CEC 2017, and real-parameter benchmark functions. Moreover, GEA has been applied to several real-parameter engineering optimization problems to evaluate its efectiveness. In addition, to demonstrate the applicability and robustness of GEA, a comprehensive investigation is performed for a fair comparison with other standard optimization methods. The results demonstrate that GEA is noticeably prosperous in reaching the optimal solutions with a high convergence rate in comparison with other well-known natureinspired algorithms, including ABC, BBO, PSO, and RCGA. Note that the source code of the GEA is publicly available at https://www.optim-app.com/projects/gea.

Support Vector Machine (SVM) has become one of the traditional machine learning algorithms the most used in prediction and classifcation tasks. However, its behavior strongly depends on some parameters, making tuning these parameters a sensitive step to maintain a good performance. On the other hand, and as any other classifer, the performance of SVM is also afected by the input set of features used to build the learning model, which makes the selection of relevant features an important task not only to preserve a good classifcation accuracy but also to reduce the dimensionality of datasets. In this paper, the MRFO + SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fne-tune the SVM parameters and identify the optimal feature subset simultaneously. The proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking datasets. Additionally, it is applied to a disease Covid-19 dataset. The experimental results show the high ability of the proposed algorithm to fnd the appropriate SVM’s parameters, and its acceptable performance to deal with feature selection problem.

eature Subset Selection (FSS) is an NP-hard problem to remove redundant and irrelevant features particularly from medical data, and it can be efectively addressed by metaheuristic algorithms. However, existing binary versions of metaheuristic algorithms have issues with convergence and lack an efective binarization method, resulting in suboptimal solutions that hinder diagnosis and prediction accuracy. This paper aims to propose an Improved Binary Quantum-based Avian Navigation Optimizer Algorithm (IBQANA) for FSS in medical data preprocessing to address the suboptimal solutions arising from binary versions of metaheuristic algorithms. The proposed IBQANA’s contributions include the Hybrid Binary Operator (HBO) and the Distance-based Binary Search Strategy (DBSS). HBO is designed to convert continuous values into binary solutions, even for values outside the [0, 1] range, ensuring accurate binary mapping. On the other hand, DBSS is a twophase search strategy that enhances the performance of inferior search agents and accelerates convergence. By combining exploration and exploitation phases based on an adaptive probability function, DBSS efectively avoids local optima. The efectiveness of applying HBO is compared with fve transfer function families and thresholding on 12 medical datasets, with feature numbers ranging from 8 to 10,509. IBQANA's efectiveness is evaluated regarding the accuracy, ftness, and selected features and compared with seven binary metaheuristic algorithms. Furthermore, IBQANA is utilized to detect COVID-19. The results reveal that the proposed IBQANA outperforms all comparative algorithms on COVID-19 and 11 other medical datasets. The proposed method presents a promising solution to the FSS problem in medical data preprocessing.

In industrial wireless networks, data transmitted from source to destination are highly repetitive. This often leads to the queuing of the data, and poor management of the queued data results in excessive delays, increased energy consumption, and packet loss. Therefore, a nature-inspired-based Dragonfy Interaction Optimization Algorithm (DMOA) is proposed for optimization of the queue delay in industrial wireless networks. The term “interaction” herein used is the characterization of the “fying movement” of the dragonfy towards damselfies (female dragonfies) for mating. As a result, interaction is represented as the fow of transmitted data packets, or trafc, from the source to the base station. This includes each and every feature of dragonfy movement as well as awareness of the rival dragonfies, predators, and damselfies for the desired optimization of the queue delay. These features are juxtaposed as noise and interference, which are further used in the calculation of industrial wireless metrics: latency, error rate (reliability), throughput, energy efciency, and fairness for the optimization of the queue delay. Statistical analysis, convergence analysis, the Wilcoxon test, the Friedman test, and the classical as well as the 2014 IEEE Congress of Evolutionary Computation (CEC) on the benchmark functions are also used for the evaluation of DMOA in terms of its robustness and efciency. The results demonstrate the robustness of the proposed algorithm for both classical and benchmarking functions of the IEEE CEC 2014. Furthermore, the accuracy and efcacy of DMOA were demonstrated by means of the convergence rate, Wilcoxon testing, and ANOVA. Moreover, fairness using Jain’s index in queue delay optimization in terms of throughput and latency, along with computational complexity, is also evaluated and compared with other algorithms. Simulation results show that DMOA exceeds other bio-inspired optimization algorithms in terms of fairness in queue delay management and average packet loss. The proposed algorithm is also evaluated for the conficting objectives at Pareto Front, and its analysis reveals that DMOA fnds a compromising solution between the objectives, thereby optimizing queue delay. In addition, DMOA on the Pareto front delivers much greater performance when it comes to optimizing the queuing delay for industry wireless networks.

This research study aims to enhance the optimization performance of a newly emerged Aquila Optimization algorithm by incorporating chaotic sequences rather than using uniformly generated Gaussian random numbers. This work employs 25 diferent chaotic maps under the framework of Aquila Optimizer. It considers the ten best chaotic variants for performance evaluation on multidimensional test functions composed of unimodal and multimodal problems, which have yet to be studied in past literature works. It was found that Ikeda chaotic map enhanced Aquila Optimization algorithm yields the best predictions and becomes the leading method in most of the cases. To test the efectivity of this chaotic variant on real-world optimization problems, it is employed on two constrained engineering design problems, and its efectiveness has been verifed. Finally, phase equilibrium and semi-empirical parameter estimation problems have been solved by the proposed method, and respective solutions have been compared with those obtained from state-of-art optimizers. It is observed that CH01 can successfully cope with the restrictive nonlinearities and nonconvexities of parameter estimation and phase equilibrium problems, showing the capabilities of yielding minimum prediction error values of no more than 0.05 compared to the remaining algorithms utilized in the performance benchmarking process.

Curvature lines are special and important curves on surfaces. It is of great signifcance to construct developable surface interpolated on curvature lines in engineering applications. In this paper, the shape optimization of generalized cubic ball developable surface interpolated on the curvature line is studied by using the improved reptile search algorithm. Firstly, based on the curvature line of generalized cubic ball curve with shape adjustable, this paper gives the construction method of SGC-Ball developable surface interpolated on the curve. Secondly, the feedback mechanism, adaptive parameters and mutation strategy are introduced into the reptile search algorithm, and the Feedback mechanism-driven improved reptile search algorithm efectively improves the solving precision. On IEEE congress on evolutionary computation 2014, 2017, 2019 and four engineering design problems, the feedback mechanism-driven improved reptile search algorithm is compared with other representative methods, and the result indicates that the solution performance of the feedback mechanism-driven improved reptile search algorithm is competitive. At last, taking the minimum energy as the evaluation index, the shape optimization model of SGC-Ball interpolation developable surface is established. The developable surface with the minimum energy is achieved with the help of the feedback mechanism-driven improved reptile search algorithm, and the comparison experiment verifes the superiority of the feedback mechanism-driven improved reptile search algorithm for the shape optimization problem.

Over the last two decades, stochastic optimization algorithms have proved to be a very promising approach to solving a variety of complex optimization problems. Bald eagle search optimization (BES) as a new stochastic optimization algorithm with fast convergence speed has the ability of prominent optimization and the defect of collapsing in the local best. To avoid BES collapse at local optima, inspired by the fact that the volume of the sphere is the largest when the surface area is certain, an improved bald eagle search optimization algorithm (INMBES) integrating the random shrinkage mechanism of the sphere is proposed. Firstly, the INMBES embeds spherical coordinates to design a more accurate parameter update method to modify the coverage and dispersion of the population. Secondly, the population splits into elite and non-elite groups and the Bernoulli chaos is applied to elite group to tap around potential solutions of the INMBES. The non-elite group is redistributed again and the Nelder-Mead simplex strategy is applied to each group to accelerate the evolution of the worst individual and the convergence process of the INMBES. The results of Friedman and Wilcoxon rank sum tests of CEC2017 in 10, 30, 50, and 100 dimensions numerical optimization confrm that the INMBES has superior performance in convergence accuracy and avoiding falling into local optimization compared with other potential improved algorithms but inferior to the champion algorithm and ranking third. The three engineering constraint optimization problems and 26 real world problems and the problem of extracting the best feature subset by encapsulated feature selection method verify that the INMBES’s performance ranks frst and has achieved satisfactory accuracy in solving practical problems.