Shark Smell Optimization Algorithm Research Articles

SSO-DRSS Axial Flux sensing in Switching Permanent Magnet Motor for cogging torque mitigation

This paper addresses the imperative requirements of high efficiency, power density, and speed in Axial Flux-Switching Permanent Magnet Motors (AxFSPM) for electric vehicles. Recognizing the limitations of conventional motor designs, especially their weight and size, hindering their universal applicability, this study proposes a novel approach. A Double Rotor Single Stator (DRSS) configuration is introduced, and its parameters are systematically optimized using the Shark Smell Optimization (SSO) algorithm. The SSO algorithm refines critical motor parameters, including axial length, turns per phase, and stator and rotor dimensions, resulting in a more compact AxFSPM motor with enhanced torque. Geometrical constraints are similarly optimized with SSO, and magnet skewing angles are employed for cogging torque reduction. The optimization process, implemented in MATLAB and ANSYS Maxwell, yields an average torque below 1.4 N m and cogging torque below 0.8 N m, demonstrating the effectiveness of the proposed approach. Comparative analyses against various motor models underscore the superiority of the SSO-optimized DRSS motor in terms of efficiency and compactness.

Economic, reliability, environmental and operation factors to achieve optimal operations of multiple microgrids

In this paper, a scenario-based strategy for optimizing multiple microgrids has been proposed by considering the demand and renewable energy sources (RES) unit unpredictability. The approach seeks to determine the appropriate capability, category, number, and position of renewable and controllable distributed RES, and the ideal switch site for clustering the traditional distributed network (DN) into a collection of interconnected micro-grids (MGs) with an economical and trustworthy structure. The suggested solution attempts to minimize all plans, counting asset and process expenses, system loss, air pollution, and the cost of microgrid s' unsupplied energy. The Monte Carlo method was adopted to generate and reduce the scenarios, and the Newton-Raphson method was used for power flow. To optimize the system, we used the improved shark smell optimization algorithm in the simulation. Obtained results of economic and reliability assessment demonstrate that by dividing the traditional DNs into a set of multiple MGs, the reductions in reliability costs and technical costs are provided due to the reduction in losses and the improvement of the voltage profile, thereby reducing the total system costs. Also, obtained numerical analyzes show 0.34%, 0.14% and 0.12% improvement, in comparison to genetic algorithms, particle community and artificial bee colony, respectively.

Artificial Intelligence Optimization Algorithms in the Finance Sector

Increasing efficiency and security expectations with the technological developments in the financial sector have necessitated more effective solutions in issues such as financial failure prediction, portfolio selection, market analysis, risk assessment, and stock price estimation. At this point, artificial intelligence optimization algorithms come to the fore in terms of lower cost, higher data storage capacity, and transaction volume, performing the same operations in similar problems and creating an effective order to prevent problems that may occur in the future. For this purpose, the use of artificial intelligence optimization algorithms in the finance sector is discussed in this study. To guide their use in decision-making and problem-solving processes, Artificial Bee Colony Algorithm, Tabu Search Algorithm, Flower Pollination Algorithm, Shark Smell Optimization Algorithm, and Differential Evolution Algorithm were explained and examples from their application areas were shared. As a result of the study, the advantages of artificial intelligence optimization algorithms were revealed and their importance in the finance sector was emphasized. This study is especially widely used in the solution of engineering problems; however, it contributes to the literature in terms of explaining algorithms whose applications in the finance sector are still limited.

Deep Learning with Optimal Hierarchical Spiking Neural Network for Medical Image Classification

Medical image classification becomes a vital part of the design of computer aided diagnosis (CAD) models. The conventional CAD models are majorly dependent upon the shapes, colors, and/or textures that are problem oriented and exhibited complementary in medical images. The recently developed deep learning (DL) approaches pave an efficient method of constructing dedicated models for classification problems. But the maximum resolution of medical images and small datasets, DL models are facing the issues of increased computation cost. In this aspect, this paper presents a deep convolutional neural network with hierarchical spiking neural network (DCNN-HSNN) for medical image classification. The proposed DCNN-HSNN technique aims to detect and classify the existence of diseases using medical images. In addition, region growing segmentation technique is involved to determine the infected regions in the medical image. Moreover, NADAM optimizer with DCNN based Capsule Network (CapsNet) approach is used for feature extraction and derived a collection of feature vectors. Furthermore, the shark smell optimization algorithm (SSA) based HSNN approach is utilized for classification process. In order to validate the better performance of the DCNN-HSNN technique, a wide range of simulations take place against HIS2828 and ISIC2017 datasets. The experimental results highlighted the effectiveness of the DCNN-HSNN technique over the recent techniques interms of different measures. Please type your abstract here.

Improved Shark Smell Optimization Algorithm for Human Action Recognition

Improved Shark Smell Optimization Algorithm for Human Action Recognition

An Efficient Policy-Based Scheduling and Allocation of Virtual Machines in Cloud Computing Environment

Cloud computing has become the most challenging research field in the current information technology scenario. In this, a set of user tasks are scheduled and allocated to numerous kinds of heterogeneous virtual machines (VMs) in cloud data centers (CDCs), and these VMs are hosted by diverse types of heterogeneous physical machines (PMs). It extends several features, encompassing elasticity, safety, sustainability, and even adequate maintenance compared to traditional data centers. There are numerous techniques available for VM scheduling and allocation. However, it still requires the existence of new mechanisms that can reduce the execution time (ET) of the tasks, improve the optimization of energy usage and resource utilization (RU), and reduce time consumption. Along with optimization, VM scheduling (VMS) and VM allocation (VMA) are two-level issues that need to be considered with essential policies to govern these mechanisms. Hence, for executing optimal VMS and VMA in the data center, new optimization methodologies, such as enhanced shark smell optimization algorithm (ESSOA) at the first level and Brownian movement-centered gravitation search algorithm (BMGSA) at the second level, are proposed in this work to define the policies. Firstly, the user requests for VMs are reserved on the most appropriate PM by the proposed ESSOA, which has the lowest execution cost within deadline limits, and the proposed BMGSA decides the allocation of the chosen VM on the most appropriate PM within the resource limitations at the second level. To demonstrate the proposed algorithm’s efficiency, the simulations are carried out using the Java language-based CloudSim simulator, and the proposed mechanism outcomes acquired are compared with the existing approaches. The simulation results show that the suggested algorithm is efficient in terms of the execution cost, degree of imbalance (DOI), make span (MS), and resource utilization (RU).

A fuzzy‐based method for cloud service migration using a shark smell optimization algorithm

AbstractComputation should develop and become more powerful and flexible as a result of the expansion of apps and the incorporation of novel consumers into the realm of computing systems. The potential of live virtual machine (VM) migration among various clouds is one of the growing study fields in cloud computing. It can be more important when the cloud service quality that a user is presently using deteriorates or when a new cloud service is launched that is superior in performance, quality, and pricing to the existing services. Because this problem is NP‐Hard in nature, this article proposes a method to migrate the load from an over‐loaded VMs to the active machine that is least loaded using a fuzzy‐based shark smell optimization algorithm. This algorithm is based on a shark's ability to discover prey as a strong hunter in nature, based on the shark's smell sense and motion to the odor source. The suggested optimization technique mathematically models diverse shark behaviors in the search environment, which is seawater. The CloudSim is utilized to illustrate the efficiency of the approach compared to others. The outcomes reveal that energy consumption and execution time are better than Genetic Algorithm and Particle Swarm Optimization algorithms.

Finger Vein Recognition Model for Biometric Authentication Using Intelligent Deep Learning

In recent years, biometric authentication systems have remained a hot research topic, as they can recognize or authenticate a person by comparing their data to other biometric data stored in a database. Fingerprints, palm prints, hand vein, finger vein, palm vein, and other anatomic or behavioral features have all been used to develop a variety of biometric approaches. Finger vein recognition (FVR) is a common method of examining the patterns of the finger veins for proper authentication among the various biometrics. Finger vein acquisition, preprocessing, feature extraction, and authentication are all part of the proposed intelligent deep learning-based FVR (IDL-FVR) model. Infrared imaging devices have primarily captured the use of finger veins. Furthermore, a region of interest extraction process is carried out in order to save the finger part. The shark smell optimization algorithm is used to tune the hyperparameters of the bidirectional long–short-term memory model properly. Finally, an authentication process based on Euclidean distance is performed, which compares the features of the current finger vein image to those in the database. The IDL-FVR model surpassed the earlier methods by accomplishing a maximum accuracy of 99.93%. Authentication is successful when the Euclidean distance is small and vice versa.

Placement and sizing of EESS bundled with uncertainty modeling by two-stage stochastic search based on improved shark smell optimization algorithm in micro-grids

Microgrids are low voltage small-scale grids which are used for supplying the thermal and electric loads in small areas or locations without access to the main grid. A microgrids can be a proper solution for power generation considering the environmental and economic problems of these locations. In this paper, the optimum size and placement of electric energy storage is presented considering the uncertainties associated with renewable energy resources and fault in the microgrid lines as well as impact of responsive loads in a microgrid. The studied hybrid system is composed of wind turbines and fuel cells. Basically, the wind energy is not certainly accessible and the newly suggested method is based on a two-stage stochastic search (TSSS) model for modeling the impact of uncertainty in the power generated by the wind generation resources by considering the wind energy prediction error as the main stochastic parameter in the planning. The standard probability distribution function is used for showing the error variation. With respect to continuity of this function, the prediction error probability function is discretized using a new method and a certain number of scenarios with specific probability are extracted. Finally, the studied problem is converted to an optimization problem and the developed shark smell optimization (SSO) algorithm is used to solve it. Numerous case studies show and approve the performance of the proposed model.

A new hybrid framework based on integration of optimization algorithms and numerical method for estimating monthly groundwater level

Prediction of groundwater level (GWL) is an important issue for optimal planning and management of groundwater resources. MODFLOW, which is a modular, 3D, finite-difference model, is widely used to simulate GWL. Although MODFLOW is a powerful model for estimating GWLs, it has some unknown parameters, such as specific yield (Sy) and hydraulic conductivity (K). The Dezful-Andimeshk plain, located in the southwest of Iran, was considered as a case study, and its monthly GWLs were simulated. Aquifer boundaries (inflow, outflow, and no-flow), piezometers, operational wells, recharge, drainage, rivers, and evaporation were deemed as the inputs for MODFLOW. Three different algorithms (i.e., shark smell optimization (SSO), particle swarm optimization (PSO), and firefly (FF)) were integrated with MODFLOW (called MODFLOW-SSO, MODFLOW-PSO, and MODFLOW-FF) to find the most accurate values of K, Sy, and GWL of the aquifer. Results revealed that MODFLOW-SSO decreased the root mean square error (RMSE) by 70.2%, 74.9%, and 84.7% at calibration stage and by 68.2%, 73.4%, and 84.2% at validation stage, compared to MODFLOW-PSO, MODFLOW-FF, and MODFLOW, respectively. Other statistical indexes of MODFLOW-SSO (e.g., coefficient of determination (R2) and RMSE) were satisfactory as well. The generalized likelihood uncertainty estimation (GLUE) was applied to calculate the uncertainty of the models with respect to Sy and K. Results indicated that MODFLOW-SSO had the least uncertainty compared to all other models. In general, performance of the MODFLOW was greatly improved by integrating with the SSO algorithm.

Adaptive Mechanism For Enhanced Performance Of Shark Smell Optimization

Various meta-heuristic approaches have been developed to find the optimal solution to optimization problems. However, different approach takes different amount of time and efficiency to achieve the optimal solution. Determination of high performance and lower computing time with simple algorithm is therefore continuously established. Shark Smell Optimization (SSO) algorithm has been proven to have high efficiency in many optimization applications. However, like the other swarm intelligence, SSO algorithm also has possibility to get trapped in local maxima or premature convergence. Thus, a new adaptive shark smell optimization (ASSO) is proposed to improve the convergence efficiency of standard SSO algorithm. An overview and performance comparison of six well-known meta-heuristic optimization algorithm is also presented in this paper. In order to verify the effectiveness of this newly developed method, the algorithm was tested on common benchmark functions used in the literature. Numerical results indicate that the ASSO algorithm strategy outperforms the basic SSO algorithm, Genertic Algorithm (GA), Particle Swarm Intelligence (PSO), Firefly Algorithm (FA), Artificial Bee Colony (ABC) and Teaching Learning Based Optimization (TBLO) in term of reaching for global solution.

Load balancing in the internet of things using fuzzy logic and shark smell optimization algorithm

PurposeBased on the positive features of the shark smell optimization (SSO) algorithm, the purpose of this paper is to propose a method based on this algorithm, dynamic voltage and frequency scaling (DVFS) model and fuzzy logic to minimize the energy consumption of integrated circuits of internet of things (IoT) nodes and maximize the load-balancing among them.Design/methodology/approachLoad balancing is a key problem in any distributed environment such as cloud and IoT. It is useful when a few nodes are overloaded, a few are under-loaded and the remainders are idle without interrupting the functioning. As this problem is known as an NP-hard one and SSO is a powerful meta-hybrid method that inspires shark hunting behavior and their skill to detect and feel the smell of the bait even from far away, in this research, this study have provided a new method to solve this problem using the SSO algorithm. Also, the study have synthesized the fuzzy logic to counterbalance the load distribution. Furthermore, DVFS, as a powerful energy management method, is used to reduce the energy consumption of integrated circuits of IoT nodes such as processor and circuit bus by reducing the frequency.FindingsThe outcomes of the simulation have indicated that the proposed method has outperformed the hybrid ant colony optimization – particle swarm optimization and PSO regarding energy consumption. Similarly, it has enhanced the load balance better than the moth flame optimization approach and task execution node assignment algorithm.Research limitations/implicationsThere are many aspects and features of IoT load-balancing that are beyond the scope of this paper. Also, given that the environment was considered static, future research can be in a dynamic environment.Practical implicationsThe introduced method is useful for improving the performance of IoT-based applications. We can use these systems to jointly and collaboratively check, handle and control the networks in real-time. Also, the platform can be applied to monitor and control various IoT applications in manufacturing environments such as transportation systems, automated work cells, storage systems and logistics.Originality/valueThis study have proposed a novel load balancing technique for decreasing energy consumption using the SSO algorithm and fuzzy logic.

Fingerprint Authentication using Shark Smell Optimization Algorithm


 Recognition of people relying on biometric characteristics is a common phenomenon in our society. It has increased in recent years in most areas of life such as government, department, companies, and banks. Fingerprint identification is one of the most common and credible personal biometric identification methods. Extracting features are one of the most important steps in the fingerprint identification; the strength of any system depends mainly on this step, where whenever the features obtained are accurate whenever the identification process is more accurate. Therefore, an effective and efficient method must be used to extract the features. This paper solved two main problems that were (1) improving security by designing and implementing an accurate, efficient, and fast authentication system for the identification and verification process using an intelligent algorithm to extract the best features from the fingerprint image and (2) evaluating the strength of the Shark Smell Optimization (SSO) in the search space with a chosen set of metrics. This paper aims to extract the best features of the fingerprint image using an algorithm that depends on nature for its movement and work; therefore, the SSO was used. In this paper, the SSO algorithm is used to extract the features. SSO is a new meta-heuristic algorithm that uses smart methods and random movements to get its prey. These methods and movements were used to extract features from the fingerprint image which will be used later for identification and verification process. The proposed method is implemented through four phases, namely, create a database to store and organize data, image pre-processing using median filter, feature extraction using SSO algorithm, and matching process using euclidean distance. The results demonstrated the strength, accurate, credible, and effectiveness of the algorithm used by applying it on (150) real fingerprint samples taken from university students, where the results of false acceptation rate, false rejection rate, and correct verification rate were 0.00, 0.00666, and 99.334%, respectively.

Uncertainties of instantaneous influent flow predictions by intelligence models hybridized with multi-objective shark smell optimization algorithm

Reliable prediction of influent time series has demonstrated importance in high-efficiency performance of wastewater treatment and reuse plants. Computational models are powerful tools that has been used for predicting influent time series. But, one of the major drawbacks of developed models is uncertainty analysis in instantaneous and multi-time step prediction of influent time series. For this purpose, a Multi-objective Shark Smell Optimization (MOSSO) algorithm is hybridized with multilayer perceptron (MLP) neural network, radial basis function (RBF) neural networks, and support vector machine (SVM) to optimize the models. These hybrid models of MLP-MOSSO, RBF-MOSSO, and SVM-MOSSO are used for predicting different prediction horizons of immediate, short-term, and long-term of influent time series. Accordingly, there are three objectives in MOSSO: (1) minimize mean absolute error (MAE) in different input vectors for best selection lag time influent time series, (2) achieve the optimal architecture of forecasting models by minimization root mean square error (RMSE) for model parameters, and (3) select accurate activate functions or kernel function of models for finding high accuracy of estimating models. The studied hybrid multi-objective models are modified using Taguchi method for determination of Pareto optimal solution sets. The proposed hybrid models and standalone models of MLP, RBF, and SVM are evaluated using Monte-Carlo uncertainty analysis, common evaluation criteria, and Taylor diagram in evaluation and reliability assessments. The results demonstrated that MLP-MOSSO model produce better results than RBF-MOSSO, SVM-MOSSO, and standalone models in all prediction horizons (S1I4: immediate-Q(t-5min), Q(t-10min); S2I16: short term-Q(t-12hr), Q(t-24hr); S3I15: long term-Q(t-2day), Q(t-4day)). The MLP-MOSSO model for immediate prediction horizon (Q(t-5min), Q(t-10min)) with one hidden layer and five neurons in hidden layer, correlation coefficient (R2) of 0.95, root mean square error (RMSE) of 1.1, Nash-Sutcliffe efficiency (NSE) of 0.91, RMSE-observations standard deviation ratio (RSR) of 0.14, and percent bias (PBIAS) of 12 for testing phase was chosen as the final and best model for predicting influent time series. Furthermore, it was found that all models have some uncertainties, but the uncertainties in the MLP-MOSSO model with immediate prediction horizon was less than other models for predicting influent time series. Finally based on the optimized results, an explicit equation is derived for the best MLP-MOSSO model to predict multi-step influent time series.

New Improved Optimized Method for Medical Image Enhancement Based on Modified Shark Smell Optimization Algorithm

Medical image enhancement is a principal category of the medical image processing which has a great impact on the final diagnosis results. In this paper, a new optimization technique has been presented for enhancing the contrast of the medical images. The main idea here is to propose an optimization problem by considering both global and local enhancement to achieve a strong image enhancement method. The other novelty here is to propose a new improved version of shark smell optimization algorithm to apply to the mentioned optimization problem for enhancing the algorithm convergence. Final results are analyzed based on five different measure indexes and are compared with five popular methods for illustrating the superiority of the presented technique.

Medical image fusion using a modified shark smell optimization algorithm and hybrid wavelet-homomorphic filter

Medical image fusion is a principal category in the medical applications which has great impacts on the final diagnosis results. In this study, a hybrid optimization technique is presented for developing a high efficiency technique for the fusion of the medical images. The presented method uses both advantages of the wavelet transform and the homomorphic filter for improving the system efficiency. For achieving the optimal values of the system, a new optimization algorithm based on two new introduced methods, shark smell optimization algorithm and world cup optimization algorithm is introduced. The new algorithm is then applied to the wavelet part of the system to get the optimal values. Simulations are applied on two classes of five clinical images including MR-CT, MR-SPECT, and MR-PET the results are compared with six popular methods. The final results showed that the proposed system has higher efficiency from the studied methods.

Photovoltaic cells parameters extraction using variables reduction and improved shark optimization technique

This article aimed to present a new approach which consists of three-points, and concerns each diode pattern separately. Distinct from traditional curve fitting processes, this approach is based on the simplistic view which flourishes the potential abilities in order to provide the efficient solutions with the least repetitions. The basic reason of this study is to substitute the three – point approach with the previous used approach and set it as core point. By using mentioned points, it reinforces the usage of improved shark smell optimization algorithm and regulates the left parts efficiently. Consequently, it decreases the elaborateness of algorithms. According to the conducted parts comparisons on the three case studies, proved that the presented method has more exactness than the traditional methods. As well as, the investigated the proposed method has high efficiency and presents the optimized solution in various cells type. Also the achieved solutions are stable and is compared with over 17 various algorithms which in whole of the standard deviation of root mean square error for three type of cells is lower than the determined scales in other studies. By having these values, the suggested approach had been predicted to work efficiently in all circumstances like offline analysis and online controlling and it requires an exact, steady approach exploitation tool.

Multi-Objective Shark Smell Optimization Algorithm Using Incorporated Composite Angle Cosine for Automatic Train Operation

In this paper, an improved multi-objective shark smell optimization algorithm using composite angle cosine is proposed for automatic train operation (ATO). Specifically, when solving the problem that the automatic train operation velocity trajectory optimization easily falls into local optimum, the shark smell optimization algorithm with strong searching ability is adopted, and composite angle cosine is incorporated. In addition, the dual-population evolution mechanism is adopted to restrain the aggregation phenomenon in shark population at the end of the iteration to suppress the local convergence. Correspondingly, the composite angle cosine, considering the numerical difference and preference difference, is used as the evaluation index, which ameliorates the shortcoming that the traditional evaluation index is not objective and reasonable. Finally, the Matlab/simulation and hardware-in-the-loop simulation (HILS) results for automatic train operation show that the improved optimization algorithm proposed in this paper has better optimization performance.

Development of Internet Protocol Traceback Scheme for Detection of Denial-of-Service Attack

To mitigate the challenges that Flash Event (FE) poses to IP-Traceback techniques, this paper presents an IP Traceback scheme for detecting the source of a DoS attack based on Shark Smell Optimization Algorithm (SSOA). The developed model uses a discrimination policy with the hop-by-hop search. Random network topologies were generated using the WaxMan model in NS2 for different simulations of DoS attacks. Discrimination policies used by SSOA-DoSTBK for the attack source detection in each case were set up based on the properties of the detected attack packets. SSOA-DoSTBK was compared with a number of IP Traceback schemes for DoS attack source detection in terms of their ability to discriminate FE traffics from attack traffics and the detection of the source of Spoofed IP attack packets. SSOA-DoSTBK IP traceback scheme outperformed ACS-IPTBK that it was benchmarked with by 31.8%, 32.06%, and 28.45% lower FER for DoS only, DoS with FE, and spoofed DoS with FE tests respectively, and 4.76%, 11.6%, and 5.2% higher performance in attack path detection for DoS only, DoS with FE, and Spoofed DoS with FE tests, respectively. However, ACS-IPTBK was faster than SSOA-DoSTBK by 0.4%, 0.78%, and 1.2% for DoS only, DoS with FE, and spoofed DoS with FE tests, respectively. Keywords : DoS Attacks Detection, Denial-of-Service, Internet Protocol, IP Traceback, Flash Event, Optimization Algorithm.

Implementing Flash Event Discrimination in IP Traceback using Shark Smell Optimisation Algorithm

Denial of service attack and its variants are the largest ravaging network problems. They are used to cause damage to network by disrupting its services in order to harm a business or organization. Flash event is a network phenomenon that causes surge in normal network flow due to sudden increase in number of network users, To curtail the menace of the Denial of service attack it is pertinent to expose the perpetrator and take appropriate action against it. Internet protocol traceback is a network forensic tool that is used to identify source of an Internet protocol packet. Most of presently available Internet protocol traceback tools that are based on bio-inspired algorithm employ flow-based search method for tracing source of a Denial of service attack without facility to differentiate flash event from the attack. Surge in network due to flash event can mislead such a traceback tool that uses flow-based search. This work present a solution that uses hop-by-hop search with an incorporated discrimination policy implemented by shark smell optimization algorithm to differentiate the attack traffic from other traffics. It was tested on performance and convergence against an existing bio-inspired traceback tool that uses flow-base method and yielded outstanding results in all the tests