Krill Herd Algorithm Research Articles

Metaheuristics with Deep Learning Empowered Biomedical Atherosclerosis Disease Diagnosis and Classification

Atherosclerosis diagnosis is an inarticulate and complicated cognitive process. Researches on medical diagnosis necessitate maximum accuracy and performance to make optimal clinical decisions. Since the medical diagnostic outcomes need to be prompt and accurate, the recently developed artificial intelligence (AI) and deep learning (DL) models have received considerable attention among research communities. This study develops a novel Metaheuristics with Deep Learning Empowered Biomedical Atherosclerosis Disease Diagnosis and Classification (MDL-BADDC) model. The proposed MDL-BADDC technique encompasses several stages of operations such as pre-processing, feature selection, classification, and parameter tuning. Besides, the proposed MDL-BADDC technique designs a novel Quasi-Oppositional Barnacles Mating Optimizer (QOBMO) based feature selection technique. Moreover, the deep stacked autoencoder (DSAE) based classification model is designed for the detection and classification of atherosclerosis disease. Furthermore, the krill herd algorithm (KHA) based parameter tuning technique is applied to properly adjust the parameter values. In order to showcase the enhanced classification performance of the MDL-BADDC technique, a wide range of simulations take place on three benchmarks biomedical datasets. The comparative result analysis reported the better performance of the MDL-BADDC technique over the compared methods.

An Effective Signcryption with Optimization Algorithm for IoT-enabled Secure Data Transmission

Internet of Things (IoT) allows several low resources and controlled devices to interconnect, calculate processes and make decisions in the communication network. In the heterogeneous environment for IoT devices, several challenging issues such as energy, storage, efficiency, and security. The design of encryption techniques enables the transmission of the data in the IoT environment in a secured way. The proper selection of optimal keys helps to boost the encryption performance. With this motivation, the study presents a signcryption with quantum chaotic krill herd algorithm for secured data transmission (SCQCKH-SDT) in IoT environment. The proposed SCQCKH-SDT technique aims to effectively encrypts the data by the use of optimal keys generated by the CQKH algorithm. The proposed SCQCKH-SDT technique initially employs the signcryption technique for the encryption of data. In order to optimize the secrecy, the optimal key generation process is carried out using Chaotic Krill Herd (CQKH) algorithm. The CQKH algorithm incorporates the concept of quantum computing and chaotic theory into the traditional KH algorithm. The performance validation of the SCQCKH-SDT technique is performed using benchmark dataset. An extensive comparative analysis reported the superior performance of the SCQCKH-SDT technique over the recent approaches.

Artificial Intelligence Based Prostate Cancer Classification Model Using Biomedical Images

Medical image processing becomes a hot research topic in healthcare sector for effective decision making and diagnoses of diseases. Magnetic resonance imaging (MRI) is a widely utilized tool for the classification and detection of prostate cancer. Since the manual screening process of prostate cancer is difficult, automated diagnostic methods become essential. This study develops a novel Deep Learning based Prostate Cancer Classification (DTL-PSCC) model using MRI images. The presented DTL-PSCC technique encompasses EfficientNet based feature extractor for the generation of a set of feature vectors. In addition, the fuzzy k-nearest neighbour (FKNN) model is utilized for classification process where the class labels are allotted to the input MRI images. Moreover, the membership value of the FKNN model can be optimally tuned by the use of krill herd algorithm (KHA) which results in improved classification performance. In order to demonstrate the good classification outcome of the DTL-PSCC technique, a wide range of simulations take place on benchmark MRI datasets. The extensive comparative results ensured the betterment of the DTL-PSCC technique over the recent methods with the maximum accuracy of 85.09%.

Rat Swarm Optimizer for Data Clustering

Rat Swarm Optimization (RSO) is one of the newest swarm intelligence optimization algorithms that is inspired from the behaviors of chasing and fighting of rats in nature. In this paper we will apply the RSO to one of the most challenging problems, which is data clustering. The search capability of RSO is used here to find the best clusters centers. The proposed algorithm RSO for clustering (RSOC) is tested on several benchmarks and compared to some other optimization algorithms for data clustering including some wellknown and powerful algorithms such as Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) and other recent such as the Hybridization of Krill Herd Algorithm and harmony search (H-KHA), hybrid Harris Hawks Optimization with differential evolution (H-HHO), and Multi-Verse Optimizer (MVO). Results are validated through a bunch of measures: homogeneity, completeness, v-measure, purity, and error rate. The computational results are encouraging, Where they demonstrate the effectiveness of RSOC over other techniques.

An Adaptive Differential Chaotic Krill Herd Algorithm for Optimal Reactive Power Dispatch Considering Dimension-Reduction

An Adaptive Differential Chaotic Krill Herd Algorithm for Optimal Reactive Power Dispatch Considering Dimension-Reduction

Intrusion Detection System for Energy Efficient Cluster Based Vehicular Adhoc Networks

A vehicular ad hoc network (VANET), a subfield of mobile adhoc network (MANET) is defined by its high mobility by demonstrating the dissimilar mobility patterns. So, VANET clustering techniques are needed with the consideration of the mobility parameters amongst the nearby nodes for constructing the stable clustering techniques. At the same time, security is also a major design issue in VANET, this can be resolved by the intrusion detection systems (IDS). In contrast to the conventional IDS, VANET based IDS are required to be designed in such a way that the functioning of the system does not affect the real-time efficiency of the performance of VANET applications. With this motivation, this paper presents an efficient Fuzzy Logic based Clustering with optimal fuzzy support vector machine (FSVM), called FLC-OFSVM based on the Intrusion Detection System for VANET. The proposed FLC-OFSVM model involves two stages of operations namely clustering and intrusion detections. Primarily, FLC technique is employed for selecting an appropriate set of cluster heads (CHs) and for constructing the clusters. Besides, a lightweight anomaly IDS model named FSVM optimized with krill herd (KH) optimization algorithm is developed for detecting the existence of malevolent attacks in VANET. The KH algorithm based on the herding behavior of krills is used for optimally tuning the parameters of the FSVM model. In order to investigate the performance of the FLC-OFSVM model, an extensive set of simulations have been carried out and the results thus showcased that the OFSVM model has gained maximum outcome with an accuracy of 99.98%.

Krill Herd Algorithm for Color Image Segmentation with Kapur, Otsu and Minimum Cross Entropy Fitness Functions

Color image segmentation is essential to analyze information from the desired image with RGB color space. Generally, visual information can be easily retrieved through the simple, effective technique called thresholding. Segmentation of complex images is accurately achieved, through MultiLevel Thresholding (MLT) with most optimistic objective functions such as Kapur, Otsu and Minimum Cross Entropy (MCE), than Bilevel Thresholding (BLT). However, the complexity in exploring the optimal threshold increases with the increase in levels of threshold. The key to breach this barrier is by the most computationally effective, flexible Krill Herd Algorithm (KHA). The behavior of the krill movement, the foraging activity and the diffusion methods are utilized for global and local searches. KHA incorporates the swarm intelligence and with crossover, mutation operators improve the convergence rate. The performance of the KHA is compared with Teaching-Learning Based Optimization (TLBO) and cuckoo search algorithm (CSA). Experimental results disclose that the Otsu based MLT outperforms the Kapur and the MCE fitness functions. Quantitative and qualitative validations by metrics such as computational time, Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) confirm that Kapur, Otsu and MCE based KHA outperform the existing techniques for real life applications.

Utilizing Noun-Verb Extraction in Enhancing Information Retrieval

The increasing growth of the news and a large number of users on information retrieval (IR) has resulted in making the retrieval of documents complex and more difficult. The IR process consists of pre-processing, extraction and representation, feature selection and indexing, querying, and retrieving results. The weakness of IR is concerned with the process of extraction, where most important words focused on verbs or nouns. Unsupervised Feature selection is an important task in content classification for being among the most popular and effective methods for retrieval reduction. The use of the verbs and nouns as extraction was recently introduced in the IR technique to avoid irrelevant and redundant unsupervised features. This paper aims to enhance IR using noun-verb extraction using Word-Net and Krill Herd Algorithm as Unsupervised Feature Selection (KHUFS) combine with Simulated Annealing as Unsupervised Feature Selection to find the suitable retrieval of ranking. The external of Mean Average Precision (MAP) and Mean Average Recall (MAR) internal of Mean Average Distance (MAD) as measurements were used to verify the proposed retrieval of ranking. The results demonstrate that the proposed nouns verbs method extraction outperformed other extraction methods, in which the proposed extraction was 26.15 % using MAP measure and using MAR was 45.41%. In comparison with other unsupervised feature selection algorithms such as Harmony Search, Simulated Annealing, Particle Swarm, and Genetic Algorithm, the combined combination outperformed other unsupervised feature selection algorithms with an accuracy 26.163 % MAP, and 11.653% MAR. On the other side, the effect of use proposed extraction on the proposed unsupervised feature selection was 39.563% MAP, and 8.96% MAR. The other evaluation using the number of features, the using combined Krill Herd with Simulated Annealing number of features has decreased to more than 50 %, which the total feature in the dataset was 10682 features and after used the proposed was 4723 features.

A novel krill herd algorithm with orthogonality and its application to data clustering

Krill herd algorithm (KHA) is an emerging nature-inspired approach that has been successfully applied to optimization. However, KHA may get stuck into local optima owing to its poor exploitation. In this paper, the orthogonal learning (OL) mechanism is incorporated to enhance the performance of KHA for the first time, then an improved method named orthogonal krill herd algorithm (OKHA) is obtained. Compared with the existing hybridizations of KHA, OKHA could discover more useful information from historical data and construct a more promising solution. The proposed algorithm is applied to solve CEC2017 numerical problems, and its robustness is verified based on the simulation results. Moreover, OKHA is applied to tackle data clustering problems selected from the UCI Machine Learning Repository. The experimental results illustrate that OKHA is superior to or at least competitive with other representative clustering techniques.

Applying a Theta-Krill Herd Algorithm to Energy Management of a Microgrid Considering Renewable Energies and Varying Weather Conditions

Abstract Recently, researchers have shown an increased interest in using renewable-based distributed generations (DGs) in microgrids (MGs). Therefore, the economic operation of MGs plays a vital role in reducing total daily costs and greenhouse gas emissions in the modern power system. This study presents a day-ahead optimization management for a grid-tied MG supplied by small-scale renewable energy sources (RESs) like photovoltaic (PV) systems. The major aim of the suggested optimal energy management system is to minimize the cost of RERs and storage facilities in the MG for power generation while satisfying technical constraints. In addition, an improved mathematical model is suggested for the PV power generation using real data for four dissimilar days. To attain accurate results, uncertainties in the generations, load demand, and market price are probabilistically modeled. To handle the optimization problem, the θ-modified krill herd (θ-MKH) algorithm is used. The suggested algorithm for solving the optimization problem is investigated on numerical examples with different RESs and storages in the MG and compared with conventional approaches. The results attained illustrate that the recommended algorithm can utilize the cheapest sources while covering technical constraints.

The forecasting of air transport passenger demands in Turkey by using novel meta‐heuristic algorithms

AbstractThe imbalance between modes of transport in our country appears as the most important problem. Therefore, in air transportation, which has a significant increasing trend, estimating the passenger demand with directly related parameters and novel algorithms is important for Turkey. In this study, different prediction models were developed applying for the first time with five different meta‐heuristic algorithms which are Flower Pollination Algorithm (FPA), Artificial Bee Colony Algorithm (ABC), Crow Search Algorithm (CSA), Krill Herd Algorithm (KH), and the Butterfly Optimization Algorithm (BOA) to estimate Turkey's air transport demand. While developing the models, Fuel Price, Gross Domestic Product per Capita, Seat Capacity, and Annual Fuel Consumption were selected as the model parameters. Although each model developed using different approaches is applicable, quadratic and power models developed using CSA showed the highest performance. For this reason, future projections were based on these models. Air transport passenger demand was examined using two scenarios in a process until 2035. In the first scenario, according to model forms, Turkey's future air transport passenger demand will reach about 460 and 490 million passengers, respectively. In the second scenario, the number of passengers will reach approximately 375 and 660 million for quadratic and power models, respectively. The results of this study will contribute to the evaluation of the current investment plans and the development of strategic plans that will meet the demands. Additionally, they will help take necessary measures and introduce some necessary regulations to ensure the income and expense balance so that the efficiency of airline companies can be improved.

Improved krill herd algorithm based sliding mode MPPT controller for variable step size P&O method in PV system under simultaneous change of irradiance and temperature

In recent years, Photovoltaic (PV) systems have been received great attention all over the words as they are sustainable, unlimited and environmentally friendly energy. However, it is required for the PV system to apply a tracking controller to guarantee efficient operation by extracting the maximum power, which named maximum power point tracking (MPPT) method. Due to the simple structure, the conventional perturb and observe (P&O) MPPT algorithm is very popular in the literature. Nevertheless, conventional methods show inaccurate performance, particularly when high variations occur in irradiance, resulting in fluctuations around the MPP. To deal with these challenges, a novel technique on the basis of the variable-step size of P&O MPPT and sliding mode controller (SMC) adjusted by the θ-modified krill herd (θ-MKH) algorithm is presented. The θ-MKH algorithm is utilized to fine-tune the optimal SMC parameters to drive the variable step of the classical P&O algorithm. Simulations are prepared to compare the performance of the suggested scheme with conventional methods by considering simultaneous fast changes of irradiance and temperature. The results show that the suggested scheme have proper performance in both transient and steady-state, particularly under quickly varying climate circumstances.

Modified Krill Herd Algorithm for constrained economic load dispatch problem

ABSTRACT Meta-heuristic techniques are gaining attractiveness to solve constrained economic load dispatch problems due to higher global solution capacity, flexibility and derivative-free structure. In this paper, Modified Krill Herd Algorithm (MKHA) is used to solve economic load dispatch problem having non-linear characteristics of generators and considering transmission losses. The proposed MKHA is a nature-inspired technique that incorporates crossover and mutation process designed in differential evolution which causes the krill to execute local search within definite section. Constraints manipulation trial is also made functional in the direction of approaching equality constraints to change total power generation depending upon the requirement of power by consumers. The proposed algorithm is testified on three systems and assessment is prepared on the basis of minimisation of fuel cost and arithmetic procedures. Relative examination of obtained outcome illustrates the efficacy of the proposed algorithm, with regard to the quality of solutions and higher efficiency.

Assessment of Mechanical Properties and Structural Morphology of Alkali-Activated Mortars with Industrial Waste Materials

Alkali-activated products composed of industrial waste materials have shown promising environmentally friendly features with appropriate strength and durability. This study explores the mechanical properties and structural morphology of ternary blended alkali-activated mortars composed of industrial waste materials, including fly ash (FA), palm oil fly ash (POFA), waste ceramic powder (WCP), and granulated blast-furnace slag (GBFS). The effect on the mechanical properties of the Al2O3, SiO2, and CaO content of each binder is investigated in 42 engineered alkali-activated mixes (AAMs). The AAMs structural morphology is first explored with the aid of X-ray diffraction, scanning electron microscopy, and Fourier-transform infrared spectroscopy measurements. Furthermore, three different algorithms are used to predict the AAMs mechanical properties. Both an optimized artificial neural network (ANN) combined with a metaheuristic Krill Herd algorithm (KHA-ANN) and an ANN-combined genetic algorithm (GA-ANN) are developed and compared with a multiple linear regression (MLR) model. The structural morphology tests confirm that the high GBFS volume in AAMs results in a high volume of hydration products and significantly improves the final mechanical properties. However, increasing POFA and WCP percentage in AAMs manifests in the rise of unreacted silicate and reduces C-S-H products that negatively affect the observed mechanical properties. Meanwhile, the mechanical features in AAMs with high-volume FA are significantly dependent on the GBFS percentage in the binder mass. It is also shown that the proposed KHA-ANN model offers satisfactory results of mechanical property predictions for AAMs, with higher accuracy than the GA-ANN or MLR methods. The final weight and bias values given by the model suggest that the KHA-ANN method can be efficiently used to design AAMs with targeted mechanical features and desired amounts of waste consumption.

Color image segmentation using Kapur, Otsu and Minimum Cross Entropy functions based on Exchange Market Algorithm

Color image segmentation is the primary step to elicit detailed information of the image with RGB color space. The key to acquire the region of interest is the straightforward, instinctual technique called thresholding. Bi-level thresholding can be assessed concretely for simple images, whereas revealing various classes from a complex image is achieved only through multiple threshold values. Hence, multilevel thresholding with the most propitious maximizing objective functions such as Kapur, Otsu and Minimum Cross Entropy (MCE) to extract the precise threshold are employed in this paper. As the segmentation level increases, execution of non-parametric objective functions leads to exponential increase in computational time. Several researches to enhance the speed of objective functions along with various metaheuristics such as Krill Herd Algorithm (KHA), Teaching-Learning Based Optimization (TLBO) and Cuckoo Search Algorithm (CSA) are performed. The need to unlock the computational complexity drifted our view towards the most powerful, robust, recently introduced metaheuristic Exchange Market Algorithm (EMA). EMA involves exchange of shares among investors to gain profit. The strategic approach of share members in stable and unstable modes of the market results in precise exploration and exploitation. Furthermore, low fitness shareholders gaining experience from their high and moderate counterparts, stands out as an exceptional step to reduce the time consumption. This concept is utilized in our paper for the first time to obtain the exact details from the image without any delay. Empirical outcome of the results indicates that exceptional image segmentation is achieved by EMA in less time compared to extensive search techniques such as KHA, TLBO and CSA. Quantitative and qualitative validation furnished by metrics such as Structural Similarity Index (SSIM), computational time, Peak Signal to Noise Ratio (PSNR) and statistical Wilcoxon test affirm that Otsu, Kapur and MCE based EMA outperforms the existing techniques to analyze the real-world images.

Multi objective taguchi–grey relational analysis and krill herd algorithm approaches to investigate the parametric optimization in abrasive water jet drilling of stainless steel

This novel study deals with the investigation of abrasive mixtures on abrasive water jet (AWJ) drilling of stainless steel 304 using multi objective soft computing techniques. In this study, the drilling parameters such as abrasive mixture, stand-off distance and feed rate were varied. The abrasive mixture was prepared with the composition of different types of abrasives such as silicon carbide & garnet, and aluminium oxide & garnet, and different mixture ratios. This mixing ratio was done based on the total mass of the abrasive mixture. The effect of abrasive water jet drilling parameters was examined on the hole features such as the hole diameter, circularity, cylindricity, and surface roughness. Multi objective optimization and algorithm techniques were employed in this study, namely Taguchi–Grey Relational Analysis (TGRA) and Krill Herd Algorithm (KHA). In this research work, the performance of KHA method was also compared with another recent metaheuristic technique i.e. grey wolf optimization (GWO) based on the quality measurement tools such as Spacing and Inverted generational distance. For this approach, the main parameters of metaheuristics algorithms were tuned using a robust design approach to acquire the best feasible solution. Besides, different multiple linear regression model equations were established to determine the best model for the KHA method based on the similarity between experimental and calculated attributes. With the assistance of these approaches, it is found that the abrasive mixtures have improved the performance of the AWJ drilling process in SS 304 rather than the use of a single type abrasive such as 100% Garnet. The results of this study proved that the KHA optimization technique is successfully utilized to find the best configuration parameter setting for AWJ drilling process, and that results are found to be efficient than the TGRA. To validate the predicted results of KHA, confirmation test was conducted. The results of the confirmation test showed that the predicted hole features of KHA were acceptable as that the error deviation was found as less than 2% with the experimental results. It is also noticed that the computational time and the selected quality metrics of KHA are found to be lower than the GWO method. Hence, it is confirmed that a new metaheuristic algorithm namely, KHA was found suitable for AWJ drilling process. The outcome of the present work explores a new paradigm to the AWJ machining to improve performance features in various operations.

LNNLS-KH: A Feature Selection Method for Network Intrusion Detection

As an important part of intrusion detection, feature selection plays a significant role in improving the performance of intrusion detection. Krill herd (KH) algorithm is an efficient swarm intelligence algorithm with excellent performance in data mining. To solve the problem of low efficiency and high false positive rate in intrusion detection caused by increasing high-dimensional data, an improved krill swarm algorithm based on linear nearest neighbor lasso step (LNNLS-KH) is proposed for feature selection of network intrusion detection. The number of selected features and classification accuracy are introduced into fitness evaluation function of LNNLS-KH algorithm, and the physical diffusion motion of the krill individuals is transformed by a nonlinear method. Meanwhile, the linear nearest neighbor lasso step optimization is performed on the updated krill herd position in order to derive the global optimal solution. Experiments show that the LNNLS-KH algorithm retains 7 features in NSL-KDD dataset and 10.2 features in CICIDS2017 dataset on average, which effectively eliminates redundant features while ensuring high detection accuracy. Compared with the CMPSO, ACO, KH, and IKH algorithms, it reduces features by 44%, 42.86%, 34.88%, and 24.32% in NSL-KDD dataset, and 57.85%, 52.34%, 27.14%, and 25% in CICIDS2017 dataset, respectively. The classification accuracy increased by 10.03% and 5.39%, and the detection rate increased by 8.63% and 5.45%. Time of intrusion detection decreased by 12.41% and 4.03% on average. Furthermore, LNNLS-KH algorithm quickly jumps out of the local optimal solution and shows good performance in the optimal fitness iteration curve, convergence speed, and false positive rate of detection.

A novel approach in selective assembly with an arbitrary distribution to minimize clearance variation using evolutionary algorithms: a comparative study

The minimization of surplus components with normal dimensional distributions while making selective assemblies was the only objective considered in the previous research works carried out by various researchers in different periods. Seldom works have been found on selective assembly by considering all dimensional distributions. In this proposed work, a novel method is developed for making assemblies with zero surplus components and minimum clearance variation by considering arbitrary distribution, to demonstrate the greater improvement in the results than the past literature. Krill Herd algorithm has been implemented for identifying the best combination of groups. Computational results showed that the proposed krill herd algorithm outperformed as compared with existing literature and as well as the results by gaining-sharing knowledge-based algorithm, differential evolution algorithm, and particle swarm optimization algorithm.

A hybrid approach to energy efficient clustering and routing in wireless sensor networks

Wireless Sensor Networks are developed as a vital tool for monitoring diverse real time applications such as environmental monitoring factors, health care, wide area surveillance, and many more. Though the advantages of WSNs are plenty, the present challenge is to gain effective control over the depleting battery power and the network lifetime. Recent researches have proved that the energy consumption can be minimized if effective clustering mechanisms are incorporated. This paper proposes HOCK and HECK - novel energy efficient clustering algorithms to increase the network lifetime for homogeneous and heterogeneous environments, respectively. Both these algorithms are built using Krill herd and Cuckoo search. While the optimal cluster centroid positions are computed using the Krill herd algorithm, and the Cuckoo search is applied to select the optimal cluster heads. The performance of the HOCK algorithm is evaluated by varying base station locations and node density. To evaluate the HECK algorithm, two and three level heterogeneity are considered. The simulation results show that the proposed protocol is more effective in improving the network lifetime of WSNs compared to other existing methods such as GAECH, Hybrid HSAPSO, and ESO-LEACH.

Hybrid BWO-IACO Algorithm for Cluster Based Routing in Wireless Sensor Networks

Wireless Sensor Network (WSN) comprises a massive number of arbitrarily placed sensor nodes that are linked wirelessly to monitor the physical parameters from the target region. As the nodes in WSN operate on inbuilt batteries, the energy depletion occurs after certain rounds of operation and thereby results in reduced network lifetime. To enhance energy efficiency and network longevity, clustering and routing techniques are commonly employed in WSN. This paper presents a novel black widow optimization (BWO) with improved ant colony optimization (IACO) algorithm (BWO-IACO) for cluster based routing in WSN. The proposed BWO-IACO algorithm involves BWO based clustering process to elect an optimal set of cluster heads (CHs). The BWO algorithm derives a fitness function (FF) using five input parameters like residual energy (RE), inter-cluster distance, intra-cluster distance, node degree (ND), and node centrality. In addition, IACO based routing process is involved for route selection in inter-cluster communication. The IACO algorithm incorporates the concepts of traditional ACO algorithm with krill herd algorithm (KHA). The IACO algorithm utilizes the energy factor to elect an optimal set of routes to BS in the network. The integration of BWO based clustering and IACO based routing techniques considerably helps to improve energy efficiency and network lifetime. The presented BWO-IACO algorithm has been simulated using MATLAB and the results are examined under varying aspects. A wide range of comparative analysis makes sure the betterment of the BWO-IACO algorithm over all the other compared techniques.