Seeker Optimization Algorithm Research Articles

A Novel Dung Beetle Optimization Approach for Automatic CMOS Analog Circuit Design

The main objective of the automated circuit sizing technique is to deal with the challenges of design tradeoffs in analog circuit design with improved accuracy and efficacy. Analog circuit design represents a multi-objective optimization challenge, where designers must properly balance various performance factors such as input and output impedance, power dissipation, area, unity-gain bandwidth, slew rate, and open-loop DC gain. Traditional design equations provide a sizing of differential amplifier MOS transistors, further optimization can be achieved through the application of meta-heuristic search techniques. Meta-heuristic search techniques can be used as local optimizers in a smaller search area to improve the optimization of design parameters. The differential amplifier circuit with current mirror load is optimized through the application of the Dung Beetle Optimization (DBO) algorithm. By using an evolutionary algorithm called DBO, all the required parameters were achieved with the least amount of transistor area and power dissipation when compared to the results of the Seeker Optimization Algorithm (SOA), Opposition based Harmony Search Algorithm (OHS), craziness-based particle swarm optimization (CRPSO), and Cuckoo Search (CS) algorithms.

An efficient power management control strategy for grid-independent hybrid renewable energy systems with hybrid energy storage: Hybrid approach

The increase in renewable energy resource based electricity generation in the past few years can be attributed to dwindling fossil fuels and their negative environmental impact, as well as the exponential expansion of electricity consumption. Furthermore, RERs are environmentally benign and sustainable. This manuscript proposes a hybrid method for managing power in a Hybrid Energy Storage System within a grid-independent Hybrid Renewable Energy System. The proposed hybrid technique combines the Prairie Dog Optimization (PDO) and Multi-scale Attention Convolutional Neural Network, hence named the PDO-MACNN technique. The principal objective of the proposed method is to minimize Total Harmonic Distortion (THD), maintain active power stability among generation and consumption through the charging or discharging of the Hybrid Energy Storage System, and regulate the DC-link voltage within strict bounds. The PDO algorithm is used to optimize the parameters of the three-phase inverter controller, while the MACNN algorithm is used to predict the performance of the hybrid renewable energy systems. The proposed approach is evaluated and compared to other existing methods on the MATLAB platform, demonstrating superior performance to methods like the Whale Optimization Algorithm, Seeker Optimization Algorithm, and Grasshopper Optimization Algorithm. Based on the outcome, it is concluded that the proposed approach achieves a 0.8 % lower THD compared to the existing techniques.

Evaluation of heating load energy performance in residential buildings through five nature-inspired optimization algorithms

This study evaluates a new hybrid approach for determining homes’ heating load (HL). The crow search algorithm (CSA), heap-based optimizer (HBO), seeker optimization algorithm (SOA), political optimizer (PO), and harmony search (HS) are the five components of the suggested paradigm. A nonlinear analysis of the effects of eight independent factors on the HL was conducted using the best structure identified in each model. The assessment procedure for the HS technique in this study consisted of three parts. The appropriate population size to utilize in the first phase was found to be the one that yields the best coefficient of determination (R2) value and the lowest root mean squared error (RMSE) value. For CSA-MLP, HBO-MLP, SOA-MLP, PO-MLP, and HS-MLP, respectively, the first phase yielded R2 = 0.96473, 0.95618, 0.96931, 0.97048, and 0.96702, and RMSE = 2.57119, 2.85968, 2.40125, 2.3554, and 2.46872. A battery of tests using a range of different nNew values (between 10 and 100) was applied to the HS-MLP with a population size of 50 in the second phase. The data indicates that the most reliable results are obtained with a nNew-value of 60. For training and testing, this value has RMSE values of 2.61518 and 2.4387 and R2 values of 0.9669 and 0.96783. In the third stage, an experiment with a population size of 50 and nNew of 60 was examined using a range of HMCR values (between 0.5 and 1.4). Concerning training and testing, the results indicate that the HMCR value 1.1 produces the most reliable results; its R2 values are 0.9739 and 0.97207, and its RMSE values are 2.32691 and 2.27488. Lastly, the results demonstrate that the accuracy of the HS-MLP method has been improved by the 3-phase analysis method.

Optimized Intrusion Detection System in Fog Computing Environment Using Automatic Termination-based Whale Optimization with ELM

In fog computing, computing resources are deployed at the network edge, which can include routers, switches, gateways, and even end-user devices. Fog computing focuses on running computations and storing data directly on or near the fog devices themselves. The data processing occurs locally on the device, reducing the reliance on network connectivity and allowing for faster response times. However, the conventional intrusion detection system (IDS) failed to provide security during the data transfer between fog nodes to cloud, fog data centres. So, this work implemented the optimized IDS in fog computing environment (OIDS-FCE) using advanced naturally inspired optimization algorithms with extreme learning. Initially, the data preprocessing operation maintains the uniform characteristics in the dataset by normalizing the columns. Then, comprehensive learning particle swarm based effective seeker optimization (CLPS-ESO) algorithm extracts the intrusion specific features by analyzing the internal patterns of all rows, columns. In addition, automatic termination-based whale optimization algorithm (ATWOA) selects the best intrusion features from CLPS-ESO resultant features using correlation analysis. Finally, the hybrid extreme learning machine (HELM) classifies the varies instruction types from ATWOA optimal features. The simulation results show that the proposed OIDS-FCE achieved 98.52% accuracy, 96.38% precision, 95.50% of recall, and 95.90% of F1-score using UNSW-NB dataset, which are higher than other artificial intelligence IDS models.

Power distribution optimization for hybrid power source integrated with battery and super-capacitor considering vehicle energy consumption

The enhancement of energy utilization of battery/super-capacitor hybrid power source can improve the driving economy of electric vehicle. Introducing optimization algorithms to achieve optimal power distribution for battery/super-capacitor hybrid power source is an important means of effectively reducing energy consumption and is worth further exploration. This article takes the energy consumption rate as the optimization objective and establishes a fitness function based on the relationship between energy consumption rate and battery current, while achieving the minimum energy consumption rate, the optimal battery current can also be obtained. The established algorithm simulation models for power distribution strategies based on particle swarm optimization algorithm, seeker optimization algorithm, and whale optimization algorithm are integrated with circuit model of the hybrid power source-based driving system of vehicle, respectively, simulations are conducted based on the New European Driving Cycle working condition. The three control strategies can achieve average energy efficiency of about 87.5%, 85.5% and 86.0%, respectively, and average energy consumption rate of approximately 0.146 kWh/km, 0.147 kWh/km and 0.146 kWh/km. In addition, the simulation results of three control strategies are compared and analyzed in terms of simulation time, optimization iteration times, and solution stability. This work can provide reference for related research on power distribution optimization of hybrid power source.

An Improved Seeker Optimization Algorithm for Phase Sensitivity Enhancement of a Franckeite- and WS2-Based SPR Biosensor for Waterborne Bacteria Detection.

For the purpose of detecting waterborne bacteria, a high-phase-sensitivity SPR sensor with an Ag-TiO2-Franckeite-WS2 hybrid structure is designed using an improved seeker optimization algorithm (ISOA). By optimizing each layer of sensor construction simultaneously, the ISOA guarantees a minimum reflectance of less than 0.01 by Ag (20.36 nm)-TiO2 (6.08 nm)-Franckeite (monolayer)-WS2 (bilayer) after 30 iterations for E. coli. And the optimal phase sensitivity is 2.378 × 106 deg/RIU. Sensor performance and computing efficiency have been greatly enhanced using the ISOA in comparison to the traditional layer-by-layer technique and the SOA method. This will enable sensors to detect a wider range of bacteria with more efficacy. As a result, the ISOA-based design idea could provide SPR biosensors with new applications in environmental monitoring.

The optimal allocation of thyristor-controlled series compensators for enhancement HVAC transmission lines Iraqi super grid by using seeker optimization algorithm

Abstract Meta-heuristic approaches evaluate the optimum size and position of flexible alternative current transmission systems power systems. The seeker optimization algorithm (SOA) technique was used to solve power engineering optimization problems with better performance than traditional approaches. This article shows the application of SOA for optimal setting and allocation of thyristor-controlled series compensators (TCSCs) in a transmission line. TCSC devices are used to improve transmission systems’ capacity and voltage profile by controlling the transmission line reactance. The IEEE 30 bus system, as well as the Iraqi Super Grid 400 kV system, is used as a test system to illustrate the technique used. Results showed that the installation of TCSC unites the aims to reduce the voltage deviation, reduce the losses of active/reactive power, and increase transmission line reserves over thermal limits. TCSC devices are very effective for the better use of existing installations without sacrificing the stability margin. SOA is intended to identify the best location and size of TCSC devices that resolve the technological difficulties of reducing the TCSC devices’ costs.

A new cooperative control solution of subway BAS: an improved fuzzy PID control algorithm

The building automation system (BAS) of a subway is the core component of monitoring and managing urban rail transit systems. For the current problems such as low control efficiency, insufficient accuracy, and poor stability of metro BAS, this article proposes a cooperative control framework based on an improved fuzzy proportional-integral-derivative (PID) algorithm. Firstly, the concept of an integrated supervisory control system (ISCS) for subways is introduced by summarizing the previously implemented engineering construction and combining it with advanced automation technology. The system’s overall design under the ISCS framework is also improved by integrating it with the fire alarm system (FAS) with the BAS as the core unit of the reliance. Then, an improved seeker optimization algorithm (ISOA) is employed to optimize the parameters of the fuzzy PID control algorithm to achieve a coordinated control of the system based on considering the time lag problem. Finally, the accuracy, efficiency, and stability of the coordinated control response of the BAS under the ISCS framework are tested experimentally. The results suggest that the proposed cooperative control solution of BAS employing the improved fuzzy PID algorithm has good control accuracy and response efficiency and can also ensure the BAS’s higher stability in the coordinated control process, which thus greatly improves the automation level of the subway and provides a safer and more reliable high-performance for the ISCS of the subway in the urban rail transportation industry.

Voltage Feed-Forward Control of Photovoltaic- Battery DC Microgrid Based on Improved Seeker Optimization Algorithm

Voltage Feed-Forward Control of Photovoltaic- Battery DC Microgrid Based on Improved Seeker Optimization Algorithm

Optimization of Coupling Efficiency in Butterfly Optical Communication Laser Based on Chaotic Adaptive Seeker Optimization Algorithm.

A chaotic adaptive seeker optimization algorithm (CASOA) is proposed in this study to improve the coupling efficiency and accuracy of a butterfly optical communication laser. It primarily relies on chaotic disturbance to improve seeker search performance. The chaotic disturbance enables the algorithm to jump out from local extremes. Furthermore, chaos is associated with a novel strategy for optimizing search paths with a small population. A simulation and experiment are conducted to demonstrate that the CASOA with a few seekers has an excellent search success rate with few iterations in the coupling alignment. These results indicate that the proposed CASOA can reliably improve the coupling accuracy and efficiency of laser diodes and single-mode fibers.

An Exploratory Verification Method for Validation of Sea Surface Radiance of HY-1C Satellite UVI Payload Based on SOA Algorithm

To support the application of ocean surface radiance data from the ultraviolet imager (UVI) payload of the HY-1C oceanographic satellite and to improve the quantification level of ocean observation technology, the authenticity check study of ocean surface radiance data from the UVI payload was conducted to provide a basis for the quantification application of data products. The UVI load makes up for the lack of detection capabilities of modern ocean remote sensing satellites in the ultraviolet band. The UVDRAMS (Ultra-Violet Dual-band RadiAnce Measurement System) was used to verify the surface radiance data collected at 16 stations in the study area and the pupil radiance data collected by the UVI payload to establish an effective radiative transfer model and to identify the model parameters using the seeker optimization algorithm (SOA). The study of the UVDRAMS measurement system based on the SOA algorithm and the validation of the sea surface radiance of the UVI payload of the HY-1C satellite shows that 97.2% of the incident pupil radiance of the UVI payload is contributed by the atmospheric reflected radiance, and only 2.8% is from the real radiation of the water surface, while the high signal-to-noise ratio of the UVI payload of the HY-1C ocean satellite can effectively distinguish the reflectance of the water body. The high signal-to-noise ratio of the UVI payload of the HY-1C ocean satellite can effectively distinguish the amount of standard deviation in the on-satellite radiation variation, which meets the observation requirements and provides a new way of thinking and technology for further quantitative research in the future.

A Neural Network Trained by Multi-Tracker Optimization Algorithm Applied to Energy Performance Estimation of Residential Buildings

Energy performance analysis in buildings is becoming more and more highlighted, due to the increasing trend of energy consumption in the building sector. Many studies have declared the great potential of soft computing for this analysis. A particular methodology in this sense is employing hybrid machine learning that copes with the drawbacks of single methods. In this work, an optimized version of a popular machine learning model, namely feed-forward neural network (FFNN) is used for simultaneously predicting annual thermal energy demand (ATED) and annual weighted average discomfort degree-hours (WADDH) by analyzing eleven input factors that represent the building circumstances. The optimization task is carried out by a multi-tracker optimization algorithm (MTOA) which is a powerful metaheuristic algorithm. Moreover, three benchmark algorithms including the slime mould algorithm (SMA), seeker optimization algorithm (SOA), and vortex search algorithm (VSA) perform the same task for comparison purposes. The accuracy of the models is assessed using error and correlation indicators. Based on the results, the MTOA (with root mean square errors 2.48 and 5.88, along with Pearson correlation coefficients 0.995 and 0.998 for the ATED and WADHH, respectively) outperformed the benchmark techniques in learning the energy behavior of the building. This algorithm could optimize 100 internal variables of the FFNN and acquire the trend of ATED and WADHH with excellent accuracy. Despite different rankings of the four algorithms in the prediction phase, the MTOA (with root mean square errors 9.84 and 95.96, along with Pearson correlation coefficients 0.972 and 0.997 for the ATED and WADHH, respectively) was still among the best, and altogether, the hybrid of FFNN-MTOA is recommended for promising applications of building energy analysis in real-world projects.

SOSO: Symbiotic Organisms Search Optimization based Faster RCNN for Secure DataStorage in Cloud

Because of the Internet of Things (IoT) and wearable devices, information obtained is subject to cyber attacks, making intrusion detection an essential component. The increased number of attacks on IoT devices exposes them to ongoing exploitation and data theft. Mirai, denial of Service (DoS), scan, etc are the major types of attacks conducted on IoT. Traditional attack detection algorithms have more disadvantages like lack of adaptability in all scenarios, low local minima computational complexity, etc. Thus developing a method that is suitable for all environments and has good convergence speed is taken as the motivation of this study. This paper presents a novel Symbiotic Organisms Search Algorithm (SOSA) optimized Faster region-based convolutional neural network (FRCNN) for attack detection and data type classification. The data to be stored in the cloud is analyzed whether they have restricted access, confidential, or unclassified. Depending upon the variety of data, the Optimal homomorphic encryption (OHE) approach is provided for security. The key selection process is done by an Elastic Collision Seeker Optimization Algorithm (ECSOA) which offers secure access to the data owner and prevents the data from unauthorized access. The model is optimized using the algorithm which improves the feature extraction performance. The effectiveness of the proposed model in attack detection is evaluated using two datasets namely the CSE-CIC-ID2018 dataset and the IoTID20 dataset. When compared to the existing techniques, the proposed model offers optimal performance in terms of multiclass attack detection.

An optimal model for municipal solid waste management using hybrid dual faster R-CNN.

Economic development, population growth, and rapid urbanization are the reasons for an increasing generation of waste all over the world. Recently, the statistics showed that 2.1 million of tons municipal solid waste (MSW) was produced in 2016, which is estimated to enhance by 3.4 million tons in 2050. In recent times, municipal solid waste generation is dramatically increasing due to factors such as rapid urbanization, altered living standard, and increased population. These factors make the municipal solid waste management system complex and break the pollution-controlling strategies. So it necessitates the system to accurately predict the waste composition. Based on the waste classification, a suitable decomposition technique is preferred. Therefore, this paper proposed CMSOA optimized dual faster R-CNN based waste management system to accurately classify the waste composition. The proposed system is formed by hybridizing dual faster RCNN along with complex-valued encoding multi-chain seeker optimization algorithm (CMSOA). Various evaluation measures, namely accuracy, precision, recall, F-measure, RMSE, MAE, and MAPE metrics, are computed, and the case study analysis is conducted on the major five cities of Maharashtra. The comparative analysis is carried out for various approaches, and from the analysis, the results revealed that the proposed method provides better classification results than other methods.

Combining Artificial Neural Network and Seeker Optimization Algorithm for Predicting Compression Capacity of Concrete-Filled Steel Tube Columns

Accurate and reliable estimation of the axial compression capacity can assist engineers toward an efficient design of circular concrete-filled steel tube (CCFST) columns, which are gaining popularity in diverse structural applications. This study proposes a novel methodology based on computational intelligence for estimating the compression capacity of CCFST. Accordingly, a conventional artificial neural network (ANN) is hybridized with a metaheuristic algorithm called the seeker optimization algorithm (SOA). Utilizing information such as the column’s length, compressive strength of ultra-high-strength concrete, and the diameter, thickness, yield stress, and ultimate stress of the steel tube, the capacity of the column is predicted through non-linear calculations. In addition to the SOA, the future search algorithm (FSA) and social ski driver (SSD) are used as comparative benchmarks. The prediction results showed that the SOA-ANN can learn and predict the compression capacity pattern with high accuracy (relative error < 2.5% and correlation > 0.99). Also, this model outperformed both benchmark hybrids (i.e., FSA-ANN and SSD-ANN). Apart from accuracy, the configuration of the SOA-ANN is simpler owing to the smaller population recruited for the optimization task. An explicit formula for the proposed model is developed, which, owing to its observed efficiency, can be reliably applied to CCFST columns for the early estimation of the compression capacity.

Deep Learning Based Face Mask Detection in Religious Mass Gathering During COVID-19 Pandemic

Notwithstanding the religious intention of billions of devotees, the religious mass gathering increased major public health concerns since it likely became a huge super spreading event for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Most attendees ignored preventive measures, namely maintaining physical distance, practising hand hygiene, and wearing facemasks. Wearing a face mask in public areas protects people from spreading COVID-19. Artificial intelligence (AI) based on deep learning (DL) and machine learning (ML) could assist in fighting covid-19 in several ways. This study introduces a new deep learning-based Face Mask Detection in Religious Mass Gathering (DLFMD-RMG) technique during the COVID-19 pandemic. The DLFMD-RMG technique focuses mainly on detecting face masks in a religious mass gathering. To accomplish this, the presented DLFMD-RMG technique undergoes two pre-processing levels: Bilateral Filtering (BF) and Contrast Enhancement. For face detection, the DLFMD-RMG technique uses YOLOv5 with a ResNet-50 detector. In addition, the face detection performance can be improved by the seeker optimization algorithm (SOA) for tuning the hyperparameter of the ResNet-50 module, showing the novelty of the work. At last, the faces with and without masks are classified using the Fuzzy Neural Network (FNN) model. The stimulation study of the DLFMD-RMG algorithm is examined on a benchmark dataset. The results highlighted the remarkable performance of the DLFMD-RMG model algorithm in other recent approaches.

Robot path planner based on deep reinforcement learning and the seeker optimization algorithm

Path planning is one of the key technologies for mobile robot applications. However, the traditional robot path planner has a slow planning response, which leads to a long navigation completion time. In this paper, we propose a novel robot path planner (SOA+A2C) that produces global and local path planners with the seeker optimization algorithm (SOA) and the advantage actor-critic (A2C) algorithm, respectively. In addition, to solve the problems of poor convergence performance when training deep reinforcement learning (DRL) agents in complex path planning tasks and path redundancy when metaheuristic algorithms, such as SOA, are used for path planning, we propose the incremental map training method and path de-redundancy method. Simulation results show that first, the incremental map training method can improve the convergence performance of the DRL agent in complex path planning tasks. Second, the path de-redundancy method can effectively alleviate path redundancy without sacrificing the search capability of the metaheuristic algorithm. Third, the SOA+A2C path planner is superior to the Dijkstra & dynamic window approach (Dijkstra+DWA) and the Dijkstra & timed elastic band (Dijkstra+TEB) path planners provided by the robot operating system (ROS) in terms of path length, path planning response time, and navigation completion time. Therefore, the developed SOA+A2C path planner can serve as an effective tool for mobile robot path planning.

Synthesizing of concentric circular antenna arrays by using a combination of ant lion optimizer and sequential quadratic programming

AbstractAntenna array synthesis is quite a popular topic in electromagnetics and finding suitable array parameter values is a challenging nonlinear engineering problem in this field. In recent years, it has been realized that optimization algorithms can be used to achieve desired solutions in this area. Besides, improvements in the optimization algorithms make it possible to perform synthesis processes on different array structures and to obtain better results. In this paper, a method based on a combination of ant lion optimizer (ALO) and sequential quadratic programming (SQP) is proposed for the concentric circular antenna array (CCAA) synthesis. Excitation amplitudes of array elements are optimized for CCAAs with low maximum sidelobe level (MSL), narrow first null beamwidth (FNBW), and low dynamic range ratio (DRR). The results of proposed ALO‐SQP method are compared with those of 11 different algorithms: ALO, moth flame optimizer (MFO), SQP, symbiotic organisms search (SOS), biogeography based optimization (BBO), opposition‐based gravitational search algorithm (OGSA), cat swarm optimization (CSO), firefly algorithm (FA), evolutionary programming (EP), backtracking search optimization algorithm (BSA), and seeker optimization algorithm (SOA). The comparisons indicate that the CCAAs synthesis using the proposed method presents good MSLs, FNBWs, and DRRs.

A hybrid engineering algorithm of the seeker algorithm and particle swarm optimization

Abstract A newly hybrid algorithm is proposed based on the combination of seeker optimization algorithm and particle swarm optimization. The hybrid algorithm is based on a double population evolution strategy, and the populations of individuals are evolved from the seeker optimization algorithm and the particle swarm optimization separately. The populations of individuals employ an information sharing mechanism to implement coevolution. The hybrid algorithm enhances the individuals’ diversity and averts fall into the local optimum. The hybrid algorithm is compared with particle swarm optimization, the simulated annealing and genetic algorithm, the dragonfly algorithm, the brain storming algorithm, the gravitational search algorithm, the sine cosine algorithm, the salp swarm algorithm, the multi-verse optimizer, and the seeker optimization algorithm, then 15 benchmark functions, five proportional integral differential control parameters models, and six constrained engineering optimization problems are selected for optimization experiment. According to the experimental results, the hybrid algorithm can be used in the benchmark functions, the proportional integral differential control parameters optimization, and in the optimization constrained engineering problems. The optimization ability and robustness of the hybrid algorithm are better.

Swarm Intelligence with Deep Transfer Learning Driven Aerial Image Classification Model on UAV Networks

Nowadays, unmanned aerial vehicles (UAVs) have gradually attracted the attention of many academicians and researchers. The UAV has been found to be useful in variety of applications, such as disaster management, intelligent transportation system, wildlife monitoring, and surveillance. In UAV aerial images, learning effectual image representation was central to scene classifier method. The previous approach to the scene classification method depends on feature coding models with lower-level handcrafted features or unsupervised feature learning. The emergence of convolutional neural network (CNN) is developing image classification techniques more effectively. Due to the limited resource in UAVs, it can be difficult to fine-tune the hyperparameter and the trade-offs amongst computation complexity and classifier results. This article focuses on the design of swarm intelligence with deep transfer learning driven aerial image classification (SIDTLD-AIC) model on UAV networks. The presented SIDTLD-AIC model involves the proper identification and classification of images into distinct kinds. For accomplishing this, the presented SIDTLD-AIC model follows a feature extraction module using RetinaNet model in which the hyperparameter optimization process is performed by the use of salp swarm algorithm (SSA). In addition, a cascaded long short term memory (CLSTM) model is executed for classifying the aerial images. At last, seeker optimization algorithm (SOA) is applied as a hyperparameter optimizer of the CLSTM model and thereby results in enhanced classification accuracy. To assure the better performance of the SIDTLD-AIC model, a wide range of simulations are implemented and the outcomes are investigated in many aspects. The comparative study reported the better performance of the SIDTLD-AIC model over recent approaches.