A Feature Selection Method Based on Hybrid Dung Beetle Optimization Algorithm and Slap Swarm Algorithm

Feature Selection(FS)is a key pre-processing step in pattern recognition and data mining tasks,which can effectively avoid the impact of irrelevant and redundant features on the performance of classification models.In recent years,meta-heuristic algorithms have been widely used in FS problems,so a Hybrid Binary Chaotic Salp Swarm Dung Beetle Optimization(HBCSSDBO)algorithm is proposed in this paper to improve the effect of FS.In this hybrid algorithm,the original continuous optimization algorithm is converted into binary form by the S-type transfer function and applied to the FS problem.By combining the K nearest neighbor(KNN)classifier,the comparative experiments for FS are carried out between the proposed method and four advanced meta-heuristic algorithms on 16 UCI(University of California,Irvine)datasets.Seven evaluation metrics such as average adaptation,average prediction accuracy,and average running time are chosen to judge and compare the algorithms.The selected dataset is also discussed by categorizing it into three dimensions:high,medium,and low dimensions.Experimental results show that the HBCSSDBO feature selection method has the ability to obtain a good subset of features while maintaining high classification accuracy,shows better optimization performance.In addition,the results of statistical tests confirm the significant validity of the method.

Applying an Improved Dung Beetle Optimizer Algorithm to Network Traffic Identification

Network traffic identification is critical for maintaining network security and further meeting various demands of network applications.However,network traffic data typically possesses high dimensionality and complexity,leading to practical problems in traffic identification data analytics.Since the original Dung Beetle Optimizer(DBO)algorithm,Grey Wolf Optimization(GWO)algorithm,Whale Optimization Algorithm(WOA),and Particle Swarm Optimization(PSO)algorithm have the shortcomings of slow convergence and easily fall into the local optimal solution,an Improved Dung Beetle Optimizer(IDBO)algorithm is proposed for network traffic identification.Firstly,the Sobol sequence is utilized to initialize the dung beetle population,laying the foundation for finding the global optimal solution.Next,an integration of levy flight and golden sine strategy is suggested to give dung beetles a greater probability of exploring unvisited areas,escaping from the local optimal solution,and converging more effectively towards a global optimal solution.Finally,an adaptive weight factor is utilized to enhance the search capabilities of the original DBO algorithm and accelerate convergence.With the improvements above,the proposed IDBO algorithm is then applied to traffic identification data analytics and feature selection,as so to find the optimal subset for K-Nearest Neighbor(KNN)classification.The simulation experiments use the CICIDS2017 dataset to verify the effectiveness of the proposed IDBO algorithm and compare it with the original DBO,GWO,WOA,and PSO algorithms.The experimental results show that,compared with other algorithms,the accuracy and recall are improved by 1.53%and 0.88%in binary classification,and the Distributed Denial of Service(DDoS)class identification is the most effective in multi-classification,with an improvement of 5.80%and 0.33%for accuracy and recall,respectively.Therefore,the proposed IDBO algorithm is effective in increasing the efficiency of traffic identification and solving the problem of the original DBO algorithm that converges slowly and falls into the local optimal solution when dealing with high-dimensional data analytics and feature selection for network traffic identification.

Restudies on Body Surface of Dung Beetle and Application of Its Bionics Flexible Technique

A scanning electron microscope was used to observe the structures of the setae on the surface of a dung beetle Copris ochus, Motschulsky. There are lots of setae on the body surface, especially on the ventral part surface and lateral to the legs which are different in size, arrangement and shape. These setae have different lengths and many thorns on the whole seta. The top ends of these setae stand up without furcations which direct uprightly towards the surface of the touched soil. By the method of removing these setae, getting the insect weight before and after digging into the dung we affirm farther that the setae on the beetle body surface form the anti-stick and non-adherent gentle interface. The soil machines and components made by imitating the gentle body surface of beetles have favorable non-adherent results.

Factors impacting nanoindentation testing results of the cuticle of dung beetle Copris ochus Motschulsky

The cuticle of dung beetle is a layered composite material in micro- or nano-scale. Dung beetle can fly, walk and dig. It can shovel and compact dung of mammals into balls. It use foreleg to walk, midleg and hindleg to hold and impel dung ball. Its two foreleges as digging legs are developed. The factors impacting the nanoindentation testing results of the femur cuticle of forelegs of dung beetle Copris ochus Motschulsky were examined. The nanomechanical test instrument used for the tests was Hysitron nanomechanical system. The results shown that the holding time and loading time are important factors im- pacting the accuracy of such indentation properties as reduced modulus (Er) and the harness ( H ) of the femur cuticle of the forelegs of dung beetle Copris ochus Motschulsky in nanoscale. There exists a threshold holding time of 20 s for the reduced modulus of the femur cuticle. The tests of nanoindentation creep property and the regression analysis of relationship between the depth increment at the maximum load and the time further confirmed the correction of the above threshold holding time. There exist visco-elastic-plastic behaviour and creep phenomenon in the femur cuticle during indenting. Its creep property during the holding procedure at maximum load can be regressed by a general logarithmic equation. The equation fitted by the testing data is ? h = 54.83452 ln(0.00723t +1.00486), where, ? h is the depth increment at the maximum load and t is the time.

Sliding resistance of plates with bionic bumpy surface against soil

The non-smooth surface morphology of dung beetle, Copris ochus, was analyzed. The bulldozing plates with bionic geometric non-smooth or the chemical uneven surface were designed for the soil sliding test based on the simulation of the bumpy surface of the dung beetle. Special black metals— with different contents of alloys of manganese, silicon, chromium, copper and rare earth— were developed for making geometric non-smooth and chemical uneven surfaces by means of surface welding at the surfaces of a middle carbon steel plate. Four metals, with different surface properties including hardness and water contact angle were used to make the bulldozing plates for measuring the soil sliding resistance. Test results of soil sliding resistance indicate that all the geometric non-smooth plates and the chemical uneven plates reducing soil friction. Considering the materials and surface morphology, the bionic plate can reduce the soil sliding resistance from 18.1 % up to 42.2%, compared to the traditional smooth bulldozing plate made from middle carbon steel. The test results also show that the smaller the normal load, the greater effect on resistance reduction by the bionic non-smooth surface plates.

Elite Dung Beetle Optimization Algorithm for Multi-UAV Cooperative Search in Mountainous Environments

This paper aims to address the problem of multi-UAV cooperative search for multiple targets in a mountainous environment,considering the constraints of UAV dynamics and prior environmental information.Firstly,using the target probability distribution map,two strategies of information fusion and information diffusion are employed to solve the problem of environmental information inconsistency caused by different UAVs searching different areas,thereby improving the coordination of UAV groups.Secondly,the task region is decomposed into several high-value sub-regions by using data clustering method.Based on this,a hierarchical search strategy is proposed,which allows precise or rough search in different probability areas by adjusting the altitude of the aircraft,thereby improving the search efficiency.Third,the Elite Dung Beetle Optimization Algorithm(EDBOA)is proposed based on bionics by accurately simulating the social behavior of dung beetles to plan paths that satisfy the UAV dynamics constraints and adapt to the mountainous terrain,where the mountain is considered as an obstacle to be avoided.Finally,the objective function for path optimization is formulated by considering factors such as coverage within the task region,smoothness of the search path,and path length.The effectiveness and superiority of the proposed schemes are verified by the simulation.

Heat tolerance may determine activity time in coprophagic beetle species (Coleoptera:Scarabaeidae)

Although reports have documented loss of species diversity and ecological services caused by stressful temperature changes that result from climate change,some species cope through behavioral compensation.As temperatures and magnitudes of temperature extremes increase,animals should compensate to maintain fitness(such as through temporary behavioral shifts in activity times).Appropriate timing of activity helps avoid competition across species.Although coprophagic dung beetles exhibit species-specific temporal activity times,it is unknown whether temperature drives evolution of these species-specific temporal activity times.Using nine dung beetle species(three each of diurnal,crepuscular,and nocturnal species),we explored differences in heat stress tolerance measured as critical thermal maxima(CTmax;the highest temperature allowing activity)and heat knockdown time(HKDT;survival time under acute heat stress)across these species,and examined the results using a phylogenetically informed approach.Our results showed that day-active species had significantly higher CTmax(diurnal>crepuscular=nocturnal species),whereas crepuscular species had higher HKDT(crepuscular>nocturnal>diurnal species).There was no correlation between heat tolerance and body size across species with distinct temporal activity,and no significant phylogenetic constraint for activity.Species with higher CTmax did not necessarily have higher HKDT,which indicates that species may respond differently to diverse heat tolerance metrics.Acute heat tolerance for diurnal beetles indicates that this trait may constrain activity time and,under high acute temperatures with climate change,species may shift activity times in more benign environments.These results contribute to elucidate the evolution of foraging behavior and management of coprophagic beetle ecosystem services under changing environments.