Lion Optimization Research Articles

Bio-Inspired Ant Lion Optimizer for a Constrained Petroleum Product Scheduling

Real-world optimization problems demand sophisticated algorithms. Over the years bio-inspired approach, a subset of computational intelligence has demonstrated remarkable success in real-world use cases, especially where exact or deterministic algorithms are ineffective. Petroleum product scheduling is a complex optimization task belonging to the combinatorial problem category. The problem size and the constraints compound the complexity of the petroleum product scheduling problem. However, conventional optimization methods such as the exact or deterministic algorithm produced a poor solution quality to the petroleum products scheduling problem. Therefore, this study leverages the potency of a bio-inspired approach, Ant Lion Optimizer (ALO) in its basic state to enhance the solution quality. This is in line with She-Shin Yang’s proposition, father of bio-inspired algorithms who advocated for the application of existing bio-inspired algorithms to tackle real-world problems rather than developing new algorithms. Bio-inspired is a computational paradigm that models the characteristics of natural biological entities to solve complex problems. We also used the Chaotic Particle Swarm Optimization (CPSO) algorithm for the same problem to unveil the efficacy of the roulette wheel function in ALO. The results show a 24.8% and 23.9% reduction in the original cost of distribution on ALO and CPSO respectively. Also, 99.5% of the constraints are met. Thus, problems of scarcity, minimum allocation and product availability are solved using the penalty constraint handling method. The exact algorithm showed a 14% reduction in the original cost. However, despite the effectiveness, further work on constraint handling methods and other bio-inspired computation approaches such as Genetic algorithms and their variants could be possible in the future scope. Moreover, other real-world problem domains such as power distribution in the power, sector could be a possible application of the ALO.

Affinity Propagation and Chaotic Lion Swarm Optimization Based Clustering for Wireless Sensor Networks

By grouping nodes with similar attributes into clusters, the energy efficiency and lifespan of wireless sensor networks(WSNs) can be improved effectively. However, the number of clusters needs to be set in advance, and the optimal cluster heads (CHs) are difficult to determine, which will undoubtedly reduce the network’s overall performance. Therefore, a new clustering method using Affinity Propagation and Chaotic Lion swarm Optimization is proposed in this paper to form optimal clusters, which is called APCLO. In APCLO, Affinity Propagation (AP) is used to construct a cluster topology, forming the initial clustering according to the remaining energies of the nodes and the similarity of the distances between nodes. Moreover, the initial CHs are also determined simultaneously by AP. In order to eliminate the outliers from the initial CHs, Chaotic Lion Optimization(CLO) is presented to find the best CHs, in which a fitness function is set according to residual energy and distance to the BS, and chaotic map is used to speed up the convergence of CLO. Simulation results show that the APCLO protocol is superior to the comparison protocols in terms of energy consumption, network throughput, convergence speed, and lifetime. For network lifespan, it increases by 20.1%, 11.2%, and 13.5% respectively.

Smart energy management for optimal economic operation in grid-connected hybrid power system

ABSTRACT This manuscript presents a hybrid method for smart energy management (EM) in grid connected microgrid (MG) system. The grid connected micro grid system consists of photovoltaic (PV), wind turbine (WT), micro turbine (MT), and battery. The proposed method is the combined execution of Radial Basis Function Neural Network (RBFNN) and Squirrel Search Algorithm (SSA), hence it is called RBFNN-SSA method. Here, the necessary load demand of grid-connected MG system is constantly monitored by AI strategy. SSA has developed the perfect combination of MG considering the forecast load demand. The major intention of the RBFNN-SSA method is fuel cost involvement, grid power hourly power variation, operation with maintenance cost of grid connected micro grid system. The constraints are the accessibility of renewable energy sources (RES), power requirement and state of charge (SoC) of storage elements. Batteries are used as an energy source, to stabilize and allow the renewable power system units for maintaining constant output power. The proposed method is activated in MATLAB/Simulink working site. Then, the efficiency is assessed with existing methods such as improved artificial bee colony (IABC), bacterial foraging optimizer and artificial neural network (BFOANN), ant lion optimizer (ALO), grasshopper optimization algorithm with particle swarm optimization including artificial neural network (GOAPSNN). The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean bias error (MBE) of proposed and existing methods under 50 and 100 count of trails are also analyzed. The proposed technique achieves the RMSE is 9.3, MAPE is 4.2, and MBE is 2 for 50 number of trails. For 100 count of trails, the proposed method achieves the RMSE is 13.5, MAPE is 3.9 and MBE is 5.7. The mean, median and standard deviation of RBFNN-SSA method achieves 0.9681, 0.9062 and 0.1099. The elapsed time of RBFNN-SSA method attains 30.15 s.

Design of Human Adaptive Mechatronics Controller for Upper Limb Motion Intention Prediction

Human Adaptive Mechatronics (HAM) includes human and computer system in a closed loop. Elderly person with disabilities, normally carry out their daily routines with some assistance to move their limbs. With the short fall of human care takers, mechatronics devices are used with the likes of exoskeleton and exosuits to assist them. The rehabilitation and occupational therapy equipments utilize the electromyography (EMG) signals to measure the muscle activity potential. This paper focuses on optimizing the HAM model in prediction of intended motion of upper limb with high accuracy and to increase the response time of the system. Limb characteristics extraction from EMG signal and prediction of optimal controller parameters are modeled. Time and frequency based approach of EMG signal are considered for feature extraction. The models used for estimating motion and muscle parameters from EMG signal for carrying out limb movement predictions are validated. Based on the extracted features, optimal parameters are selected by Modified Lion Optimization (MLO) for controlling the HAM system. Finally, supervised machine learning makes predictions at different points in time for individual sensing using Support Vector Neural Network (SVNN). This model is also evaluated based on optimal parameters of motion estimation and the accuracy level along with different optimization models for various upper limb movements. The proposed model of human adaptive controller predicts the limb movement by 96% accuracy.

Deep Learning Empowered Cybersecurity Spam Bot Detection for Online Social Networks

Cybersecurity encompasses various elements such as strategies, policies, processes, and techniques to accomplish availability, confidentiality, and integrity of resource processing, network, software, and data from attacks. In this scenario, the rising popularity of Online Social Networks (OSN) is under threat from spammers for which effective spam bot detection approaches should be developed. Earlier studies have developed different approaches for the detection of spam bots in OSN. But those techniques primarily concentrated on hand-crafted features to capture the features of malicious users while the application of Deep Learning (DL) models needs to be explored. With this motivation, the current research article proposes a Spam Bot Detection technique using Hybrid DL model abbreviated as SBD-HDL. The proposed SBD-HDL technique focuses on the detection of spam bots that exist in OSNs. The technique has different stages of operations such as pre-processing, classification, and parameter optimization. Besides, SBD-HDL technique hybridizes Graph Convolutional Network (GCN) with Recurrent Neural Network (RNN) model for spam bot classification process. In order to enhance the detection performance of GCN-RNN model, hyperparameters are tuned using Lion Optimization Algorithm (LOA). Both hybridization of GCN-RNN and LOA-based hyperparameter tuning process make the current work, a first-of-its-kind in this domain. The experimental validation of the proposed SBD-HDL technique, conducted upon benchmark dataset, established the supremacy of the technique since it was validated under different measures.

Multiobjective Ant Lion Optimization for Performance Improvement of Modern Distribution Network

Multiobjective ant lion optimization (MALO) is a technique developed to imitate ant foraging behavior. This method has many advantages, including straightforward, scalable, flexible, balanced, and fast response. The MALO technique consists of five stages: ants perform optimization by random walking by updating their position, building traps, inserting ants into traps, capturing prey, and rebuilding traps. MALO has been successfully used to find optimal solutions to power system problems. Computer-assisted operations characterize modern distribution networks to solve complex problems. The complexity of the distribution network problem is owing to the integration of distributed energy resources (DERs). A DER is a renewable energy power plant with a capacity of up to 10 MW that has gained popularity in recent years. In its application, the integration of DERs into the distribution network can cause new problems, namely load imbalances or excessive voltage increases on the buses where the DER is injected. Therefore, good planning is required to place the DER. This study proposes a multiobjective optimization technique based on MALO to determine the optimal DER location and capacity. MALO is a relatively new optimization method that has the potential to improve distribution network performance. Test cases were conducted for an IEEE 33-bus radial power-distribution network. Four scenarios were considered, integrating DER types I, II, III, and IV. In each design, the placement of one DER, two DERs, and three DERs was modeled to optimize the location and capacity. The results of the multiobjective optimization show that the MALO technique can improve the distribution network performance, which is characterized by a significant power loss reduction, an increase in the bus voltage profile, and a balanced load on each feeder.

Hybrid meta-heuristic-based inventory management using block chain technology in cloud sector

This paper plans to develop an inventory management model in the supply chain with cloud and block chain assistance. This paper considers the inventory management of a supply chain that involves multiple suppliers, manufacturers, and distributers. The data management is done by the block chain technology, where each distributer holds a hash function to store its data, which cannot be restored by the other distributers. The proposed model intends to reduce the multi-objective inventory cost involving transaction cost, inventory holding cost, shortage cost, transportation cost, and time cost. The hybridisation of two meta-heuristic algorithms like spider monkey optimisation (SMO) and sea lion optimisation (SLnO) termed as spider monkey local leader-based sea lion optimisation algorithm (SMLL-SLnO) is used to improve the inventory management model. Finally, the feasibility and effectiveness of the proposed optimisation model are validated by comparing over the other traditional models.

Multistrategy Harris Hawks Optimization Algorithm Using Chaotic Method, Cauchy Mutation, and Elite Individual Guidance

Aiming at the shortcomings of the Harris hawks optimization algorithm (HHO), such as poor initial population diversity, slow convergence speed, poor local optimization ability, and easily falling into local optimum, a Harris hawks optimization algorithm (CCCHHO) integrating multiple mechanisms is proposed. First, the population diversity is enhanced by the initialization of the chaotic method. Second, the cosine function is used to better simulate the characteristics of the periodic change of the energy of the prey in the repeated contests with the group of hawks, to better balance the exploration and exploitation of the algorithm. Third, Cauchy mutation on the optimal individual in the exploration phase is performed, and the characteristics of the Cauchy distribution to enhance the diversity of the population are used, which can effectively prevent the algorithm from falling into the local optimum. Fourth, the local optimization ability of the algorithm by using the ergodicity of the chaotic system in the exploitation phase to perform a chaotic local search for the optimal individual is enhanced, which can effectively jump out after the algorithm falls into the local optimum. Finally, we use the elite individuals of the population to guide the position update of the population’s individuals, fully communicate with the dominant individuals, and speed up the convergence speed of the algorithm. Through the simulation experiments on CCCHHO with 11 different benchmark functions, CCCHHO is better than the gray wolf optimization algorithm (GWO), the Salp swarm algorithm (SSA), the ant lion optimization algorithm (ALO), and three improved HHO algorithms in terms of convergence speed and optimization accuracy, whether it is a unimodal benchmark function or a multimodal benchmark function. The experimental results show that CCCHHO has excellent algorithm efficiency and robustness.

Design of PIλDµ Controller for a Fractional-Order Automatic Voltage Regulator System

This paper identifies an analogous fractional-order model for an integer-order Automatic Voltage Regulator (AVR) using Particle Swarm Optimization (PSO) technique. Statistical analysis is carried out to justify the supremacy of PSO over other optimization methods like Ant Lion Optimization (ALO) and Sine Cosine Algorithm (SCA). Comparison is made between various predicted models in terms of time and frequency domain specifications considering different objective functions. Integer-order and fractional-order PID controllers are designed to compensate for the deficiencies present in the identified fractional-order AVR system. Parameter debugging of the proposed controllers is carried out by choosing suitable objective functions and using the PSO optimization method. The superiority of fractional-order controller over integer-order controller is inspected through comparisons. The robustness of the applied controller is examined in terms of the disturbance rejection capability of the controllers.

Swarm Optimization and Machine Learning for Android Malware Detection

Malware Security Intelligence constitutes the analysis of applications and their associated metadata for possible security threats. Application Programming Interfaces (API) calls contain valuable information that can help with malware identification. The malware analysis with reduced feature space helps for the efficient identification of malware. The goal of this research is to find the most informative features of API calls to improve the android malware detection accuracy. Three swarm optimization methods, viz., Ant Lion Optimization (ALO), Cuckoo Search Optimization (CSO), and Firefly Optimization (FO) are applied to API calls using auto-encoders for identification of most influential features. The nature-inspired wrapper-based algorithms are evaluated using well-known Machine Learning (ML) classifiers such as Linear Regression (LR), Decision Tree (DT), Random Forest (RF), K–Nearest Neighbor (KNN) & Support Vector Machine (SVM). A hybrid Artificial Neuronal Classifier (ANC) is proposed for improving the classification of android malware. The experimental results yielded an accuracy of 98.87% with just seven features out of hundred API call features, i.e., a massive 93% of data optimization.

Intelligent Leakage Location of Urban Small Water Supply Network

In this paper, the leakage location model of pipe network is established based on EPANET software. Two intelligent swarm optimization algorithms, ant lion optimization algorithm and particle swarm optimization algorithm, are used to solve the model. Taking the industrial water supply network of a coastal city in North China as an example, the operation of the two algorithms is analyzed and compared. The results show that the ant lion optimization algorithm has stronger global optimization ability and higher search efficiency in the problem of leakage location; it also has high application value in practical engineering.

Analysis of Brushless DC Motor Using Enhanced Fopid Controller with ALO Algorithm

The delivery of combined benefits of Alternating Current (AC) motor and Direct Current (DC) Motor makes the Brushless Direct Current (BLDC) motors as a unique feature in numerous industrial applications. The possibilities of running the motor at very high speed with extensive operating life span of BLDC with miniature and its compact design make it an un-ignorable option for Electrical Engineers. With many advantages, till managing as well as controlling the speed of BLDC is complicated. This work is intended to come up with an effective control of speed of the motor through Torque Ripple Minimization Route and an Enhanced-Fractional Order Proportional Integral and Derivative Controller based Ant-Lion Optimizer (EFCA)Technique, which is evaluated as well as its strategy is executed in this paper. The critical factors like BLDC’s Back Electro Magnetic Force, Values of Current, Torque level and the operating speed of the motor are considered and evaluated closely and from that, the ripple level present in the torque is controlled as well as it is brought down to minimalistic value by using Fractional Order (FO) Proportional Integral Derivative (FOPID) controller. To control the speed of the motor, the Gain parameters and the ripple present in the torque have to be reduced, the latter is obtained using FOPID controller and the former is achieved by employing Modified – Ant Lion Optimizer (ALO). The technique presented in this paper helps for the enhancement of controlling behavior of BLDC and improves the overall performance of the motor. The dynamic part of the BLDC is analyzed using the platform of Matrix Laboratory (MATLAB) simulink and the results are tabulated. The final values are compared with the results of the existing methodologies and the comparative study has yielded that the presented technique has outperformed than that of the available methodologies. A proto type model has been designed and results are executed in real time environment to ensure the effectiveness of the presented work.

Binary Drone Squadron Optimization Approaches for Feature Selection

A great quantity of data is being created these days, which is kept in massive datasets with different irrelevant attributes that are unrelated to the goal notion. Feature selection deals with the selection of most pertinent features that also aid to increase the classification accuracy. The topic of feature selection is viewed as a multi-objective optimization problem with two goals: improving classification accuracy and reducing the number of features used. Drone Squadron Optimization (DSO) is one of the most recent artifact-inspired optimization algorithm; having two key components: semi-autonomous drones that hover over a terrain and a command center that manages the drones. In this paper, two binary variants of the DSO are proposed to deal with the feature selection problem. The proposed binary algorithms are applied on 21 different benchmark datasets against five state-of-the-art algorithms, i.e., Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Flower Pollination Algorithm (FPA), Genetic Algorithm (GA) and Ant Lion Optimization (ALO). Different assessment indicators are used to assess the diversification and intensification of the optimization algorithms. When compared to current state-of-the-art wrapper-based algorithms, the suggested binary techniques are more efficient in scanning the dimension space and picking the most useful characteristics for categorization tasks, resulting in the lowest classification error rate.

An Energy-Efficient Compression Algorithm of ECG Signals in Remote Healthcare Monitoring Systems

Remote Healthcare Monitoring Systems (RHMs) that use ECG signals are very effective tools for the early diagnosis of various heart conditions. However, these systems are still confronted with a problem that reduces their efficiency, such as energy consumption in wearable devices because they are battery-powered and have limited storage. This paper presents a novel algorithm for the compression of ECG signals to reduce energy consumption in RHMs. The proposed algorithm uses discrete Krawtchouk moments as a feature extractor to obtain features from the ECG signal. Then the accelerated Ant Lion Optimizer (AALO) selects the optimum features that achieve the best-reconstructed signal. Our proposed algorithm is extensively validated using two benchmark datasets: MIT-BIH arrhythmia and ECG-ID. The proposed algorithm provides the average values of compression ratio (CR), percent root mean square difference (PRD), signal to noise ratio (SNR), Peak Signal to noise ratio (PSNR), and quality score (QS) are 15.56, 0.69, 44.52, 49.04 and 23.92, respectively. The comparison demonstrates the advantages of the proposed compression algorithm on recent algorithms concerning the mentioned performance metrics. It also tested and compared against other existing algorithms concerning Processing Time, compression speed and computational efficiency. The obtained results show that the proposed algorithm extremely outperforms in terms of (Processing Time = 6.89 s), (compression speed = 4640.19 bps) and (computational efficiency = 2.95). The results also indicate that the proposed compression algorithm reduces energy consumption in a wearable device by decreasing the wake-up time by 3600 ms.

Multi-Objective Optimization of 400 kV Composite Insulator Corona Ring Design

The electric field distribution is one of the main factors governing the long-term reliability of high voltage composite insulators. However, under severe pollution conditions, electric field stresses, when exceeding thresholds and applying for long periods, could lead to degradation and deterioration of the housing materials and, therefore, to failures of the composite insulators. This paper is intended to improve the distributions of the electric field and potential by minimizing the corona ring on a 400 kV AC transmission line composite insulator. The performances of three powerful multi-objective meta-heuristic algorithms, namely Ant Lion Optimizer (MOALO), Particle Swarm Optimizer (MOPSO), and non-dominated sorting genetic algorithm (NSGA-II) are established to achieve this goal. First, variations of electrical fields on the critical parts of the string are obtained using three-dimensional finite element method (FEM) software. Then, three objective functions are developed to establish the relationships between the electric field and the guard ring parameters. Finally, the optimization parameters consist of diameter, tube diameter, and installation height of the corona ring. The obtained results confirm the effectiveness of the three algorithms; the MOLAO is the better in terms of computing time and solution quality.

Diabetic macular oedema classification using gradient adaptive thresholding integrated active contour and ant lion spider monkey optimisation-based generative adversarial network

Diabetic macular oedema (DME) is an eye disease, which can highly affect the visual activity for the diabetic patients. The imaging tool, optical coherence tomography (OCT) is used for diagnosis by the ophthalmologists for retinal disease identification. A novel gradient-based adaptive thresholding integrated active contour ant lion spider monkey optimisation driven general adversarial network (G-AT_AC+ALSMO-GAN) is introduced for the DME detection. Here, G-AT_AC scheme is applied for layer segmentation process. In addition, texture features, layer specific features, and image level features are mined for effective classification. The DME classification is carried out using GAN classifier, which is trained by developed ALSMO algorithm, which is the integration of the ant lion optimisation (ALO) and spider monkey optimisation (SMO). During the classification process, 12 layers and 13 boundaries are used for the segmentation process. The DME affected region is classified by the GAN classifier and the classified output is normal or affected one. The proposed method obtained the maximal accuracy, specificity and sensitivity of 94.12%, 92.77% and 98% respectively.

Optimized Deformable Model-based Segmentation and Deep Learning for Lung Cancer Classification.

Lung cancer is one of the life taking disease and causes more deaths worldwide. Early detection and treatment is necessary to save life. It is very difficult for doctors to interpret and identify diseases using imaging modalities alone. Therefore computer aided diagnosis can assist doctors for the early detection of cancer very accurately. In the proposed work, optimized deformable models and deep learning techniques are applied for the detection and classification of lung cancer. This method involves pre-processing, lung lobe segmentation, lung cancer segmentation, Data augmentation and lung cancer classification. The median filtering is considered for pre-processing and the Bayesian fuzzy clustering is applied for segmenting the lung lobes. The lung cancer segmentation is carried out using Water Cycle Sea Lion Optimization (WSLnO) based deformable model. The data augmentation process is used to augment the size of segmented region in order to perform better classification. The lung cancer classification is done effectively using Shepard Convolutional Neural Network (ShCNN), which is trained by WSLnO algorithm. The proposed WSLnO algorithm is designed by incorporating Water cycle algorithm (WCA) and Sea Lion Optimization (SLnO) algorithm. The performance of the proposed technique is analyzed with various performance metrics and attained the better results in terms of accuracy, sensitivity, specificity and average segmentation accuracy of 0.9303, 0.9123, 0.9133 and 0.9091 respectively. J. Med. Invest. 69 : 244-255, August, 2022.

Automated Detection and Classification of Meningioma Tumor from MR Images Using Sea Lion Optimization and Deep Learning Models

Meningiomas are the most prevalent benign intracranial life-threatening brain tumors, with a life expectancy of a few months in the later stages, so this type of tumor in the brain image should be recognized and detected efficiently. The source of meningiomas is unknown. Radiation exposure, particularly during childhood, is the sole recognized environmental risk factor for meningiomas. The imaging technique of magnetic resonance imaging (MRI) is commonly used to detect most tumor forms as it is a non-invasive and painless method. This study introduces a CNN-HHO integrated automated identification model, which makes use of SeaLion optimization methods for improving overall network optimization. In addition to these techniques, various CNN models such as Resnet, VGG, and DenseNet have been utilized to give an overall influence of CNN with SeaLion in each methodology. Each model is tested on our benchmark dataset for accuracy, specificity, dice coefficient, MCC, and sensitivity, with DenseNet outperforming the other models with a precision of 98%. The proposed methods outperform existing alternatives in the detection of brain tumors, according to the existing experimental findings.

An elitist control scheme for power flow management in smart grid system: a hybrid optimization scheme

ABSTRACT In this manuscript, an elitist power flow management (PFM) scheme of PV/FC/battery/SCAP in smart grid (SG) system is proposed. The proposed structure has photovoltaic (PV) generator (Maximum Power Point Tracking (MPPT) and power limitation mode), battery storage, fuel cell (FC), and super capacitor (SCAP). The proposed method is the consolidation of binary adaptation of ant lion optimizer (BALO) and squirrel search optimizer (SSO), and hence it is known BALSO method. Here, BALO is generated the inverter control pulses depends on power transfer an assortments among source and load side. The formulation of multiobjective function in terms of available source power through grid required generated active with reactive power variety. Here, SSO is utilized to obtain that online control pulse using power flow varieties. Moreover, a power flow management method as per the global state of charge (SOC) of energy storage and load demand is proposed. Finally, the BALSO system is performed on MATLAB/Simulink work site. The efficiency of the BALSO system is analyzed through other existing systems. The elapsed time with proposed and existing technique is evaluated.

A Heuristic Methods-Based Power Distribution System Optimization Toolbox

This paper proposes a toolbox for simulating the effective integration of renewable energy sources into distribution systems. The toolbox uses four heuristic methods: the particle swarm optimization (PSO) method, and three recently developed methods, namely Gray Wolf Optimization (GWO), Ant Lion Optimization (ALO), and Whale Optimization Algorithm (WOA), for the efficient operation of power distribution systems. The toolbox consists of two main functionalities. The first one allows the user to select the test system to be solved (33-, 69-, or 141-bus test systems), the locations of the distributed generators (DGs), and the voltage regulators. In addition, the user selects the daily active power output profiles of the DGs, and the tool solves the voltage deviation problem for the specified time of day. The second functionality involves the simulation of energy storage systems and provides the optimal daily power output of the resources. With this program, a graphical user interface (GUI) allows users to select the test system, the optimization method to be used, the number of DGs and locations, the locations and number of battery energy storage systems (BESSs), and the tap changer locations. With the simple user interface, the user can manage the distribution system simulation and see the results by making appropriate changes to the test systems.