Krill Herd Algorithm Research Articles

An optimal configuration for a battery and PEM fuel cell-based hybrid energy system using developed Krill herd optimization algorithm for locomotive application

A new methodology has been proposed for optimal size selection of a hybrid energy system (HES) including lithium-ion battery and polymer electrolyte membrane (PEM) fuel cell to supply the driving force of a locomotive. The main purpose is to minimize the total cost of HES with different constraints including the capacity constraint of the battery and the fuel cell state-of-charge limit The optimization problem has been solved based on a new improved model of the Krill Herd (KH) algorithm, converged krill herd optimization algorithm (CKH). Simulation results are analyzed based on the average power demand, speed demand of the locomotive, and the locomotive slope. The results of the presented CKH algorithm have been compared with the standard KH and PSO algorithm and the results declared that the total cost for HES based on CKH has the minimum value such that the value for the CKH for 0%, 1%, and 2% slope are 3.15×106, 3.56×106, and 3.93×106 toward KH with 3.47×106, 4.01×106, and 4.56×106 and PSO with 3.74×106, 4.27×106, and 4.72×106 HES, respectively.

Optimal Location of FACTS Devices for Active and Reactive Loadability Improvement Using KHA Algorithm

Optimal reactive power dispatch in power system is referred to minimizing the power loss in transmission lines and voltage deviations at the load bus by control of reactive power. This paper presents the comparison between particle swarm optimization (PSO) and an improved evolutionary krill herd algorithm (KHA) for computing optimal steady-state performance of power system by finding the optimal position for placement of flexible AC transmission system (FACTS). For this purpose thyristor controlled series compensator (TCSC), static VAR compensator (SVC). A comparative analysis between the two optimization techniques is done and KHA is found to give better results as compared to PSO. IEEE 30- bus system is considered for the optimal placement of FACTS controllers.

Frequency Control of Microgrid with Renewable Generation using PID Controller based Krill Herd

The main of this paper is to provide optimal control of a state microgrid system. The proposed configuration composes of renewable generation systems such as solar photovoltaic system and wind turbine generator with a Diesel Engine Generator and Fuel-Cell. An Aqua electrolyzer and other energy storage systems such as battery and flywheel are also used in the proposed microgrid. A standard PID (Proportional Integral Derivative) controller scheme is introduced whose its parameters are determined using different optimizations algorithm such as Algorithm Genetic, Particle Swarm Optimization, and Krill Herd algorithm for minimizing frequency and power deviations, in order to enhance the operation of this system. The PID controller gains are optimized by resolving an objective function. The simulation results are shown, and given that the Krill Herd algorithm improves the performance of the system in comparison with GA and PSO based on PID. The efficiency of the system is improved.

Active and Reactive Load Ability Improvement using BBO Algorithm with FACTS Devices

Biogeography-based optimization (BBO) technique is used for the optimal positioning of FACTS devices under various loading cases in IEEE 14 bus system. In this paper, the effects of active and reactive loadings are studied and power flow analysis is performed for the proper placement of FACTS devices. The suggested method with active and reactive loading using FACTS devices is compared with Krill herd algorithm (KHA). It is found that the BBO algorithm improves real power loss, operating cost and system loadability significantly.

Static optimal load flow of combined heat and power system with valve point effect and prohibited operating zones using Krill Herd algorithm

Cogeneration of heat as well as electrical energy is an efficient and matured method which consumes less primary energy and reduces emission level significantly. In this process, heat production is reliant on power production and is restricted by the feasible operation region (FOR). This paper considered prohibited operating zones and valve point effect in its maiden formulation of static load flow problem of power system involving cogeneration units for maintaining voltage level of buses within specified limits while satisfying other linear and non-linear constraints in order to make the production cost effective. Cost of production and transmission loss is optimized using Krill Herd algorithm. This algorithm is based on the foraging activities of individual Krill. The distance of Krill individual from food and the denser swarm constitutes the objective function. The efficacy of the algorithm is showed through results of two test systems. Both test systems have been adapted from IEEE 30 bus 6 generators standard test system.

HKF-SVR Optimized by Krill Herd Algorithm for Coaxial Bearings Performance Degradation Prediction.

In real industrial applications, bearings in pairs or even more are often mounted on the same shaft. So the collected vibration signal is actually a mixed signal from multiple bearings. In this study, a method based on Hybrid Kernel Function-Support Vector Regression (HKF–SVR) whose parameters are optimized by Krill Herd (KH) algorithm was introduced for bearing performance degradation prediction in this situation. First, multi-domain statistical features are extracted from the bearing vibration signals and then fused into sensitive features using Kernel Joint Approximate Diagonalization of Eigen-matrices (KJADE) algorithm which is developed recently by our group. Due to the nonlinear mapping capability of the kernel method and the blind source separation ability of the JADE algorithm, the KJADE could extract latent source features that accurately reflecting the performance degradation from the mixed vibration signal. Then, the between-class and within-class scatters (SS) of the health-stage data sample and the current monitored data sample is calculated as the performance degradation index. Second, the parameters of the HKF–SVR are optimized by the KH (Krill Herd) algorithm to obtain the optimal performance degradation prediction model. Finally, the performance degradation trend of the bearing is predicted using the optimized HKF–SVR. Compared with the traditional methods of Back Propagation Neural Network (BPNN), Extreme Learning Machine (ELM) and traditional SVR, the results show that the proposed method has a better performance. The proposed method has a good application prospect in life prediction of coaxial bearings.

An efficient krill herd algorithm for color image multilevel thresholding segmentation problem

The conventional thresholding methods are very efficient for bi-level thresholding, but the computational complexity may be excessively high for color image multilevel thresholding. Color image multilevel thresholding segmentation can be considered as a constrained optimization problem, therefore swarm intelligence algorithms are widely used to reduce the complexity. In this paper, an efficient krill herd (EKH) algorithm is proposed to search optimal thresholding values at different level for color images and the Otsu’s method, Kapur’s entropy and Tsallis entropy are employed as objective functions. Seven different algorithms, KH without any genetic operators (KH I), KH with crossover operator (KH II), KH with crossover and mutation operators (KH IV), modified firefly algorithm (MFA), modified grasshopper optimization algorithm (MGOA), bat algorithm (BA) and water cycle algorithm (WCA), are compared with the EKH algorithm. Experiments are performed on ten color benchmark images in terms of optimal threshold values, objective values, PSNR, SSIM and standard deviation of the objective values at different levels. The experimental results show that the presented EKH algorithm is superior to the other algorithms for color image multilevel thresholding segmentation. On the other hand, Kapur’s entropy is found to be more accurate and robust for color image multilevel thresholding segmentation.

Influence of Initializing Krill Herd Algorithm With Low-Discrepancy Sequences

The krill herd (KH) algorithm is a global metaheuristic algorithm that was initially proposed for solving continuous optimization problems. The KH algorithm, since inception, has generated considerable real-world application interests in the research community. The standard algorithm solution implementation steps follow the initialization mechanism, which relies mainly on educated guesses or random initialization solution generation. Therefore, to improve the performance of the KH algorithm, the current study is set to investigate the influence of initializing the KH algorithm with three low-discrepancy sequences, such as the Faure sequence, Sobol sequence, and Van der Corput sequence. These low-discrepancy sequences are known to be more uniformly distributed across the problem search space than the commonly used random number initialization method. The study also evaluates the influence of population size on the performance of the proposed variants of the improved KH algorithms. The experimental results show significant improvements for the enhanced KH algorithms in terms of performance and the quality of solutions obtained; particularly on standard benchmarked high-dimension test problem instances, where the enhanced KH variants outperformed the existing basic KH algorithm for all the test functions evaluated. Similarly, the results for low dimension test cases showed less sensitivity to the initialization schemes, as the performance of our proposed improved scheme was comparable to that of the basic KH algorithm. However, in most cases, as the problem dimension was scaled up, the enhanced KH outperformed the basic KH. Evaluation results based on the population size of the algorithm, revealed that when the number of Krill is set at 25, the Sobol based KH initialization scheme performed better than did the other methods. Although, the Van der Corput and Faure based KH initialization schemes showed similar sensitivity when the dimension was set at 20. As we varied the population size of Krill, it was observed that the performance of the Sobol based KH initialization scheme deteriorated, whereas the other two methods showed superior performance. Overall, the findings from this study revealed that there are significant improvements in the performance of KH algorithm when initialized with low-discrepancy sequences.

A coevolutionary quantum krill herd algorithm for solving multi-objective optimisation problems

Multi-objective optimisation (MOO) has always been a challenging problem that received considerable attention in practical engineering applications due to the multicriteria objectives. This paper presents a coevolutionary quantum krill herd algorithm (CQKH) as a novel numerical method for solving MOO. The CQKH is a quantum-inspired evolutionary algorithm which improves the krill herd algorithm (KH) based on quantum representation and quantum rotation gate. As a result, CQKH has a stronger robustness and the capability of finding the optimal or near optimal solution faster by fewer individuals. In addition, the CQKH adopts a coevolutionary technique named multiple populations for multiple objectives (MPMO) to obtain the whole Pareto optimal front. The computation results of CQKH on numerical tests with various characteristics demonstrate its effectiveness and superiority compared to some state-of-the- art algorithms.

A coevolutionary quantum krill herd algorithm for solving multi-objective optimisation problems

Multi-objective optimisation (MOO) has always been a challenging problem that received considerable attention in practical engineering applications due to the multicriteria objectives. This paper presents a coevolutionary quantum krill herd algorithm (CQKH) as a novel numerical method for solving MOO. The CQKH is a quantum-inspired evolutionary algorithm which improves the krill herd algorithm (KH) based on quantum representation and quantum rotation gate. As a result, CQKH has a stronger robustness and the capability of finding the optimal or near optimal solution faster by fewer individuals. In addition, the CQKH adopts a coevolutionary technique named multiple populations for multiple objectives (MPMO) to obtain the whole Pareto optimal front. The computation results of CQKH on numerical tests with various characteristics demonstrate its effectiveness and superiority compared to some state-of-the- art algorithms.

Automatic Vehicle License Plate Recognition Using Optimal K-Means With Convolutional Neural Network for Intelligent Transportation Systems

Due to recent developments in highway research and increased utilization of vehicles, there has been significant interest paid on latest, effective, and precise Intelligent Transportation System (ITS). The process of identifying particular objects in an image plays a crucial part in the fields of computer vision or digital image processing. Vehicle License Plate Recognition (VLPR) process is a challenging process because of variations in viewpoint, shape, color, multiple formats and non-uniform illumination conditions at the time of image acquisition. This paper presents an effective deep learning-based VLPR model using optimal K-means (OKM) clustering-based segmentation and Convolutional Neural Network (CNN) based recognition called OKM-CNN model. The proposed OKM-CNN model operates on three main stages namely License Plate (LP) detection, segmentation using OKM clustering technique and license plate number recognition using CNN model. During first stage, LP localization and detection process take place using Improved Bernsen Algorithm (IBA) and Connected Component Analysis (CCA) models. Then, OKM clustering with Krill Herd (KH) algorithm get executed to segment the LP image. Finally, the characters in LP get recognized with the help of CNN model. An extensive experimental investigation was conducted using three datasets namely Stanford Cars, FZU Cars and HumAIn 2019 Challenge dataset. The attained simulation outcome ensured effective performance of the OKM-CNN model over other compared methods in a considerable way.

Optimal Reallocation of Generators using Line Utilization Factor and L-Index with UPFC

In the electrical industry, globalization and privatization have greatly increased competition. Therefore, it has become completely necessary to make full use of existing power assets. In this paper, a technique was proposed in the presence of UPFC to optimize the sizing of generators with Krill Herd algorithm. The UPFC is based on an index incorporating both the L-index and the LUF index. For tuning the generators, a multi-objective function has been selected. The multi-objective feature consists of deviation of voltage, cost of active generation of power and loss of transmission line. This approach was tested and implemented for regular loading and extreme network conditions due to line failure on an IEEE 30 bus system.

A Novel Hybrid Krill Herd Algorithm for Improving the Performance of Electric Power Systems Operation

A Novel Hybrid Krill Herd Algorithm for Improving the Performance of Electric Power Systems Operation

An Iterative Two-Layer Optimization Charging and Discharging Trading Scheme for Electric Vehicle Using Consortium Blockchain

This paper introduces the concept of hierarchical and zonal scheduling and proposes an iterative two-layer model to optimize the charging and discharging trading of electric vehicles (EVs), so as to minimize the overall load variance of the distribution network under the constraints of power flow and vehicle travel demand. In order to solve the mixed-integer programming (MIP) problem that exists in this model, an improved heuristic algorithm, the adaptive inertia weight krill herd (KH) algorithm is proposed. In addition, we design a decentralized trading architecture and related electricity trading process based on the consortium blockchain to ensure the security and privacy of two-way electricity trading between EVs and the smart grid. The IEEE nodes based simulation experiment shows that our scheme can effectively smooth power load fluctuations, and the improved KH algorithm can effectively improve the efficiency of model solving. Security analysis qualitatively proves that our scheme can ensure the security and privacy-preserving of electricity trading. Finally, our scheme is implemented in the Hyperledger Fabric to evaluate the feasibility and effectiveness.

Link-based multi-verse optimizer for text documents clustering

Text document clustering (TDC) represents a key task in text mining and unsupervised machine learning, which partitions a specific documents’ collection into varied K-groups according to certain similarity/dissimilarity criterion. There exists a considerable amount of knowledge in the text clustering field and many attempts were carried out to resolve the TDC problem and improve the learning performance. The multi-verse optimizer algorithm (MVO) is a stochastic population-based algorithm, which was recently introduced and successfully utilized to tackle many optimization problems that are complex. The original MVO performance is limited to the utilization of only the best solution in the exploitation phase (local search capability), which makes it suffer from entrapment in local optima and low convergence rate. This paper aims to propose a novel method of modifying the MVO algorithm called link-based Multi-verse optimizer algorithm (LBMVO) to enhance the exploitation phase in the original MVO. The enhancement involves adding a neighbor operator to the MVO algorithm to enhance the search capability via a novel probability factor, namely neighborhood selection strategy (NSS). The proposed LBMVO’s effectiveness was tested on six standard datasets, which are used in the text clustering domain in addition to five standard datasets, which are utilized in the data clustering domain. The experiments revealed that the modified MVO with NSS has boosted the results in terms of error rate, accuracy, recall, precision, F-measure, purity, entropy criteria, and high convergence rate. Generally, LBMVO has outperformed or at least showed that it is profoundly competitive compared with the original MVO algorithm and with widely known clustering techniques like Spectral, Agglomerative, Density-based spatial clustering of applications with noise (DBSCAN), K-means, K-means++ clustering techniques and the optimization algorithms like harmony search (HS), genetic algorithm (GA), particle swarm optimization (PSO), krill herd algorithm (KHA), covariance matrix adaptation evolution strategy (CMAES), coyote optimization algorithm (COA), as well as original MVO.

Integrated Reactive Power Optimization Method for Active Distribution Networks Based on a Quantum Krill Herd Algorithm

Aiming at the problem that the reactive power optimization of traditional distribution network can’t adapt to the active distribution network (ADN) of large-scale distributed power access, a comprehensive reactive power optimization method based on quantum krill herd algorithm for ADN is proposed. Firstly, a reactive power optimization model of ADN based on proportional coefficient is proposed by analyzing the characteristics of active control and active management. Secondly, the krill herd algorithm is easy to fall into local optimum when solving optimization problems. To overcome this shortcoming, a quantum krill swarm algorithm is proposed. The algorithm uses the probability amplitude of quantum bits to represent the information of particle position, uses quantum revolving gate to increase population diversity and generates new population through chaotic crossover, which improves the convergence accuracy of the algorithm. Finally, simulation experiments are carried out on the modified IEEE33 node and IEEE 69 node to verify the effectiveness of the proposed model and algorithm.

Hybrid Algorithm using the Advantage of Krill Herd Algorithm with Opposition- Based Learning for Dynamic Resource Allocation in Cloud Environment

The cloud computing systems have more consideration due to the growing control for elevated concert computing and data storage. Resource allocation plays a vital role in cloud systems. To overcome the obscurity present in resources allocation system. In this paper, we design and develop a technique for dynamic resource allocation. A Hybridized approach is designed with the help of multi-objective oppositional krill herd optimization algorithm (OKHA). It is a combination of the krill herd algorithm and Opposition-based learning (OBL), OBL is added to get enhanced performance of the krill herd algorithm. The objective of this hybridization is to reduce the cost. In this Hybridized process each task consists of two cost i.e monetary cost and computational cost. Here each task is divided into many subtasks and assigns the respective resources to it. Our proposed multi-objective optimization algorithm will decide allocation of resource for the each subtask in this process. Finally, the testing is passed out, we evaluate our proposed algorithm with PSO, and GA algorithm we verified the performance levels of our proposed Multi-objective optimization algorithm.

Optimized cluster head selection using krill herd algorithm for wireless sensor network

ABSTRACT Wireless Sensor Network (WSNs) can perform transmission within themselves and examination is performed based on their range of frequency. It is quite difficult to recharge devises under adverse conditions. The main limitations are area of coverage, network’s lifetime and aggregating and scheduling. If the lifetime of a network should be prolonged, then it can become a success along with reliability of the data transferred, conservation of sensor and scalability. Through many research works, this challenge can be overcome which are being proposed and the network’s lifespan improved which can preserve the sensor’s energy. By schemes of clustering, a low overhead is provided and the resources are efficiently allocated thus increasing the ultimate consumption of energy and reducing interfaces within the sensor nodes. Challenges such as node deployment and energy-aware clustering can be considered as issues of optimization with regards to WSNs, along with data collection. An optimal solution can be gotten through evolutionary and SI algorithm, pertaining to Non-deterministic Polynomial (NP)-complete along with a number of techniques. In this work, Krill Herd Algorithm based clustering is proposed.

Energy-aware virtual machines allocation by krill herd algorithm in cloud data centers

The growing demand for computational power has led to the emergence of large-scale data centers that consume massive amounts of energy, thus resulting in high operating costs and CO2 emission. Furthermore, cloud computing environments are required to provide a high Quality of Service (QoS) to their clients and, therefore, need to handle power shortages. An optimized virtual machine allocation to physical hosts lowers energy consumption and allows for high-quality services. In this study, a novel solution was proposed for the allocation of virtual machines to physical hosts in cloud data centers using the Krill Herd algorithm, which is the fastest collective intelligence algorithm recently introduced. The performance of the proposed method was evaluated using the CloudSim simulator, and the results are suggestive of a 35% reduction in energy consumption.

Krill herd algorithm with chaotic time interval and elitism scheme

ABSTRACTWe propose a new chaotic krill herd (CKH) in terms of the recently developed krill herd (KH) algorithm, to solve global numerical optimization problems. In CKH, chaos characteristics are introduced into the KH so as to further enhance its global search ability. The elitism scheme is also applied to store the best krill during the process when updating the krill. This new approach can speed up the global convergence, while preserving the advantage of the standard KH, thus making the approach more feasible for a wider range of practical applications. Here, thirteen different chaotic maps are used to tune the time interval of the krill in the KH algorithm. Twenty-four standard benchmark functions are utilized to verify the effects of the CKH and it has been demonstrated that, in most cases, the performance of CKH with a proper chaotic map is superior to, or at least highly competitive with, the standard KH and other population-based optimization methods.HighlightsA new meta-heuristic algorithm, namely CKH is proposed for global optimization.13 different chaotic maps are applied to tune the main parameter of the KH algorithm.The elitism scheme is applied to keep the best fitness krill.The CKH algorithm is compared with ten well-known methods.