Salp Swarm Optimization Algorithm Research Articles

Design an optimal robust integral signum of the error controller for electrical vehicle based on salp swarm optimization algorithm

Design an optimal robust integral signum of the error controller for electrical vehicle based on salp swarm optimization algorithm

A Novel Equal Area-Equal Width-Equal Bin Numbers Technique Using Salp Swarm Optimization Algorithm for Maximizing the Success Rate of Ball Bearing Assembly

In this work, an algorithmic technique is used to minimize the excess parts and maximize the success rate of selective assembly. In this study, a unique method known as Equal Area-Equal Width-Equal Bin Numbers is introduced to group the parts of a ball bearing assembly by taking into account their range of tolerance. A full factorial design is used to conduct the experiments, and the salp swarm optimization (SSO) algorithm is employed to evaluate the best bin combinations and identify the possibility of making the maximum number of assemblies. Computational results showed a 13.16 percent increase in success rate when compared to prior research when employing the proposed method. Comparing the computational outcomes versus those obtained by the Antlion optimization and Genetic algorithms validates the adoption of the SSO algorithm. A paired T-test is performed to assess the statistical significance of the findings. The convergence plot further supports the superiority of the SSO algorithm.

Multi-objective optimization of permanent magnet motor based on Improved Salp Swarm Algorithm and Spearman correlation

In order to weaken the torque ripple caused by cogs torque and increase the output torque of permanent magnet synchronous motor, this paper proposes a multi-objective optimization method of permanent magnet synchronous motor based on Improved Multi-Objective Salp Swarm Optimization Algorithm (IMOSSA) and Spearman correlation analysis. Firstly, the improved strategy of Multi-Objective Salp Swarm Algorithm (MOSSA) was proposed, and the effectiveness of the improved algorithm was further verified by the performance test of the algorithm. Then, the motor simulation model was established, and the high correlation parameters were filtered out by Spearman correlation analysis. The response surface model was established by Response Surface Method (RSM), and the regression equation was given. The IMOSSA was used for iterative optimization. Through optimization, the experimental results show that under the premise of meeting the efficiency design requirements, the average output torque of the motor is increased by 21.35%, the torque ripple and cogging torque are reduced by 45.04% and 39.17% respectively, which verifies the effectiveness and feasibility of the proposed method.

Quantum Inspired Chaotic Salp Swarm Optimization for Dynamic Optimization

Many real-world problems are dynamic optimization problems that are unknown beforehand. In practice, unpredictable events such as the arrival of new jobs, due date changes, and reservation cancellations, changes in parameters or constraints make the search environment dynamic. Many algorithms are designed to deal with stationary optimization problems, but these algorithms do not face dynamic optimization problems or manage them correctly. Although some of the optimization algorithms are proposed to deal with the changes in dynamic environments differently, there are still areas of improvement in existing algorithms due to limitations or drawbacks, especially in terms of locating and following the previously identified optima. With this in mind, we studied a variant of SSA known as QSSO, which is integrating the principles of quantum computing. An attempt is made to improve the overall performance of standard SSA to deal with the dynamic environment effectively by locating and tracking the global optima for DOPs. This work is an extension of the proposed new algorithm QSSO, known as the Quantum-inspired Chaotic Salp Swarm Optimization (QCSSO) Algorithm, which is detailing the various approaches taken into consideration while solving DOPs. A chaotic operator is employed with quantum computing to respond to change and guarantee to increase individual searchability by improving population diversity and the speed at which the algorithm converges. We experimented by evaluating QCSSO on a well-known generalized dynamic benchmark problem (GDBG) provided for CEC 2009, followed by a comparative numerical study with well-regarded algorithms. As promised, the introduced QCSSO is discovered, and a rival algorithm for DOPs.

An Intelligent Moving Object Segmentation Using Hybrid IFCM-CSS Clustering Model

In this study, a novel hybrid deep clustering approach is proposed for the effective moving object segmentation. Initially, the data is collected, and the keyframe selection is performed using the threshold-based Kennard–Stone method. Then, the preprocessing step involves noise filtering using bilateral wavelet thresholding and binary color conversion. The blob detection is performed using normalized Laplacian of Gaussian. Finally, the segmentation of moving objects is performed using a hybrid clustering approach called improved fuzzy C-mean (IFCM) clustering with chaotic salp swarm (CSS) optimization algorithm (Hybrid IFCM-CSS). The overall evaluation is done in MATLAB. The performance of the hybrid IFCM-CSS is compared to other approaches based on some measures. The proposed Hybrid IFCM-CSS achieves the highest precision of 0.971, using the SBM-RGBD dataset.

Enhancing security in smart healthcare systems: Using intelligent edge computing with a novel Salp Swarm Optimization and radial basis neural network algorithm

A smart healthcare system (SHS) is a health service system that employs advanced technologies such as wearable devices, the Internet of Things (IoT), and mobile internet to dynamically access information and connect people and institutions related to healthcare, thereby actively managing and responding to medical ecosystem needs. Edge computing (EC) plays a significant role in SHS as it enables real-time data processing and analysis at the data source, which reduces latency and improves medical intervention speed. However, the integration of patient information, including electronic health records (EHRs), into the SHS framework induces security and privacy concerns. To address these issues, an intelligent EC framework was proposed in this study. The objective of this study is to accurately identify security threats and ensure secure data transmission in the SHS environment. The proposed EC framework leverages the effectiveness of Salp Swarm Optimization and Radial Basis Functional Neural Network (SS-RBFN) for enhancing security and data privacy. The proposed methodology commences with the collection of healthcare information, which is then pre-processed to ensure the consistency and quality of the database for further analysis. Subsequently, the SS-RBFN algorithm was trained using the pre-processed database to distinguish between normal and malicious data streams accurately, offering continuous monitoring in the SHS environment. Additionally, a Rivest-Shamir-Adelman (RSA) approach was applied to safeguard data against security threats during transmission to cloud storage. The proposed model was trained and validated using the IoT-based healthcare database available at Kaggle, and the experimental results demonstrated that it achieved 99.87 % accuracy, 99.76 % precision, 99.49 % f-measure, 98.99 % recall, 97.37 % throughput, and 1.2s latency. Furthermore, the results achieved by the proposed model were compared with the existing models to validate its effectiveness in enhancing security.

Hybrid Salp Swarm and Improved Whale Optimization Algorithm‐based clustering scheme for improving network lifespan in wireless sensor networks

SummaryWireless sensor networks (WSNs) represent the collection of restricted energy sensor nodes that are deployed in an area of target for gathering potential environment data for decision‐making with respect to their objective of application. However, the implementation of energy‐effective data gathering strategies in large‐scale WSNs is the most challenging due to the limited energy resources. Clustering‐based data gathering strategies are identified to be quite effective for energy saving that directly attributes to extended network lifetime. Moreover, optimal path amid the cluster head (CH) and sink node needs to be selected for sustaining energy efficiency and improving network lifespan. In this article, Hybrid Salp Swarm and Improved Whale Optimization Algorithm (HSSIWOA)‐based clustering scheme is proposed for improving the network lifetime and routing optimization with maximized energy efficacy. It integrated the exploration capability of Salp Swarm Optimization Algorithm (SSOA) with exploitation benefits of Improved Whale Optimization Algorithm (IWOA) for balancing the trade‐off between the rate of exploration and exploitation during CH selection process. It utilized the parameters of residual energy, load balance, intra‐cluster distance, inter‐cluster distance, and node centrality into account during the process of fitness evaluation. It performed well by constructing an optimized number of clusters, such that energy stability and network lifetime are maintained in the network. The experimental results of the proposed HSSIWOA scheme confirmed extended network lifetime of 21.64%, minimized energy utilization of 23.42%, and maximized throughput of 18.56%, better than the baseline approaches.

CGFSSO: the co-operative guidance factor based Salp Swarm Optimization algorithm for MPPT under partial shading conditions in photovoltaic systems

CGFSSO: the co-operative guidance factor based Salp Swarm Optimization algorithm for MPPT under partial shading conditions in photovoltaic systems

A hybrid solar-driven vacuum thermionic generator and looped multi-stage thermoacoustically driven cryocooler system: Exergy- and emergy-based analysis and optimization

Significant high-quality heat is wasted in the vacuum thermionic generator (VTIG), which can be efficiently utilized as a prime mover of a bottoming system for cogeneration applications. For this purpose, a new environmental-friendly hybrid system composed of a heliostat solar field, VTIG, and looped multi-stage thermoacoustically driven cryocooler (LMTC) is established, in which the high-temperature heat source of the solar receiver runs the VTIG to generate power, and the LMTC recovers the waste heat of the VTIG to produce a cooling load. Thermodynamic, economic, and environmental analyses of the system are carried out based on exergy and emergy concepts. Moreover, a parametric study is performed to assess the effect of design parameters on the system's thermodynamic, economic, and environmental criteria. Finally, the multi-criteria salp swarm optimization algorithm and decision-making procedures are conducted to improve the exergetic performance and decrease the system's cost and monetary emergy rates along with the environmental impact and ecological emergy rate. Findings depict that at the reliable, optimal operation of the system, the exergetic efficiency can reach 29.36% with a maximum power of 17.2 MW and cooling load of 0.260 MW. The system's cost and monetary emergy rate can be reduced to 0.059 $/s and 5.94 × 1010 seJ/s, with 10.6% and 10% reductions, respectively. Moreover, the environmental impact and ecological emergy rates decline by 6% and 7.4%, respectively. The theoretical findings may offer guidance for the optimum designing and practical running of such a solar solid-state cogeneration system.

Designing and Application of Modified SCSO-Based LADRC Controller for Dicing Saw Chuck Table Systems

In this paper, the linear active disturbance rejection control (LADRC) is applied to the position control of a dicing saw chuck table, which is used to improve the steady-state accuracy and anti-interference ability of the control system. Aiming at the problem that LADRC parameters are difficult to adjust, a parameter adjustment method based on an improved sand cat swarm algorithm is proposed. The nonlinear convergence factor strategy and multiple chaotic map optimization strategy are used to improve the algorithm. In order to verify the performance of the proposed method, the improved sand cat swarm optimization algorithm is compared with the standard sand cat swarm optimization algorithm, grey wolf optimization algorithm and salp swarm optimization algorithm. The final experimental results show that using the improved sand cat swarm optimization algorithm to optimize the LADRC controller has higher steady-state accuracy, stronger anti-interference ability, and better optimization results.

Developing an optimized routing protocol with rumor riding technique for detection of Sybil attack in VANET environment

SummaryAn attack on VANET known as a Sybil attack involves the attacker claiming or stealing multiple identities with the intent of causing havoc on the network's operation by disseminating false information. To protect the VANET network from Sybil attack, a variety of methods have been proposed. In order to compensate for the Sybil attack on VANETs, a rumor riding with salp swarm optimization (SSO) based Routing protocol is developed in this research work. The primary goal of this investigation is to discover how VANETs are vulnerable to the Sybil attack in order to improve their security. Rumor riding will be used to identify the Sybil attack in the suggested strategy. VANETs are protected by the Blowfish Algorithm, which uses SSO to select the best key for each network. The collective intelligence and dynamic adaptation of SALP swarm optimization algorithm enable efficient identification of rumor sources in complex networks. The algorithm is scalable, flexible, and adept at reducing false positives and negatives, making it suitable for large‐scale monitoring. Its parallel processing capabilities and robustness ensure rapid and reliable detection. Using the NS2 platform, the proposed method is compared to existing benchmark in terms of delay, packet delivery ratio (PDR), packet loss, throughput, and attack detection rate. The proposed SSO has reduced the packet loss by nearly 5% and delay by nearly 7%, when compared with existing techniques. The delivery ratio of packet for proposed model is increased up to 8% of PDR, when compared with ACO, PSO, and FA. According to this evidence, it is clear that the proposed SSO outperformed existing approaches in a variety of performance metrics.

Multiprocessor Task Scheduling Optimization for Cyber-Physical System Using an Improved Salp Swarm Optimization Algorithm

Multiprocessor Task Scheduling Optimization for Cyber-Physical System Using an Improved Salp Swarm Optimization Algorithm

SSEPC cloud: Carbon footprint aware power efficient virtual machine placement in cloud milieu

The consumption of energy and carbon emission in cloud datacenters are the alarming issues in recent times, while optimizing the average response time and service level agreement (SLA) violations. Handful of researches have been conducted in these domains during virtual machine placement (VMP) in cloud milieu. Moreover it is hard to find researches on VMP considering the cloud regions and the availability zones along with the datacenters, although both of them play significant roles in VMP. Hence, we have worked on a novel approach to propose a hybrid metaheuristic technique combining the salp swarm optimization and emperor penguins colony algorithm, i.e. SSEPC to place the virtual machines in the most suitable regions, availability zones, datacenters, and servers in a cloud environment, while optimizing the mentioned quality of service parameters. Our suggested technique is compared with some of the contemporary hybrid algorithms in this direction like Sine Cosine Algorithm and Salp Swarm Algorithm (SCA-SSA), Genetic Algorithm and Tabu-search Algorithm (GATA), and Order Exchange & Migration algorithm and Ant Colony System algorithm (OEMACS) to test its efficacy. It is found that the proposed SSEPC is consuming 4.4%, 8.2%, and 16.6% less energy and emitting 28.8%, 32.83%, and 37.45% less carbon, whereas reducing the average response time by 11.43%, 18.57%, and 26% as compared to its counterparts GATA, OEMACS, and SCA-SSA respectively. In case of SLA violations, SSEPC has shown its effectiveness by lessening the value of this parameter by 0.4%, 1.2%, and 2.8% as compared to SCA-SSA, GATA, and OEMACS respectively.

Estimation of mechanical properties of the modified high-performance concrete by novel regression models

Using support vector regression (SVR) analytics, a novel method for evaluating the high-performance concrete (HPC) compressive strength (CS) containing fly ash (FA) and blast furnace slag (BFS) has been developed. Both Salp swarm optimization (SSA) and Grasshoppers optimization algorithm (GOA) were used in this research to look for critical SVR method variables that may be tweaked for better performance. The suggested approaches were created using 1030 trials, eight inputs (the primary component of admixtures, mix designs, curing age, and aggregates), and the CS as the forecasting goal. After that, the findings were compared to those found elsewhere in the literature. Combined SSA-SVR and GOA-SVR analysis could work exceptionally well when it comes to estimating, according to the estimation findings. The root means square error (RMSE) value for the GOA-SVR faces a remarkable increment in comparison with the SSA-SVR. The comparison resulted that the GOA-SVR delivered a higher rate of accuracy than any previous published research. At the outset, the developed GOA-SVR model might be considered a practical predictive system for the CS prediction of HPC admixed with FA and BFS.

AT-densenet with salp swarm optimization for outlier prediction

In the field of data mining, outlier prediction detects objects that behave differently from normal objects. The conventional density and distance based outlier detection cannot identify the outliers present generally in the stream data. Transfer learning applies the knowledge of Artificial Intelligence to the task of outlier prediction. It makes training faster than earlier stages of the deep learning model. However, the correlations between spatial points are not detected with the Transfer learning based approach. It can be resolved with attention based approach, which focuses on the most informative data points. Hence, this work introduces Transfer learning with an attention mechanism based deep learning model for outlier detection. The attention transfer-DenseNet model is utilized for the outlier prediction. The attention mechanism highlights outlier information in feature maps and enhances the prediction performance. Further, the attention transfer-DenseNet weights are optimized by the metaheuristic algorithm salp swarm optimization algorithm. The performance of the proposed outlier prediction is compared over the conventional models and attained better mean average precision of 0.98 (Glass dataset) and 0.986 (Abalone dataset), respectively.

An improved hybrid salp swarm optimization and African vulture optimization algorithm for global optimization problems and its applications in stock market prediction

An improved hybrid salp swarm optimization and African vulture optimization algorithm for global optimization problems and its applications in stock market prediction

Techno-economic and environmental assessment of renewable energy sources, virtual synchronous generators, and electric vehicle charging stations in microgrids

Optimum design for microgrids that include renewable energy sources (RESs) is a complex process that requires optimization across a wide range of factors, including economic, technological, and environmental effectiveness. The inverter-connected RES units lack the rotational mass required to maintain the system’s inertia and can result in significant frequency instability during disturbances. The battery energy storage system-based virtual synchronous generator (BESS-VSG) is a unique approach to address this challenge since it mimics a conventional synchronous generator (SG) using the inverter regulation concept. Furthermore, given the recent rise in electric vehicles (EVs), EV owners’ charging patterns impact the microgrid’s operational, financial, and technological aspects. Therefore, this research offers a novel Modified Multi-objective Salp Swarm Optimization Algorithm (MMOSSA)-based technique for optimal photovoltaic (PV), wind turbine (WT), BESS-VSG, and EV charging station (EVCS) allocation on microgrids. The objective function is designed to optimize the total net present cost (TNPC), levelized cost of electricity (LCOE), energy loss, frequency deviation, voltage stability indicator (VSI), and carbon emissions. The proposed strategy is evaluated on two real-world grid networks: Masirah Island in Oman and Ankara in Turkey, where wind prospects, solar potential, and EV charging patterns are conflicting. This study investigated PV/BESS-VSG, WT/BESS-VSG, and PV/WT/BESS-VSG configurations regarding the weather and load variability for the two test networks. The obtained results demonstrate that the PV/WT/BESS-VSG is the best choice for both the test networks, with TNPC values of 261030.9 $ and 303840.5 $, respectively, and LCOE values of 0.0383 $/kWh and 0.0469 $/kWh, respectively. The carbon emissions are reduced by 88.8% and 87.3% for the two test networks, respectively. In addition, with the accurate PV/WT/VSG design and appropriate EVCS placements, the microgrids’ renewable fraction, the quantity of sold energy, and technical characteristics improved significantly. Moreover, employing numerical analysis, different loss of power supply probability (LPSP) values, spacing index, and hyper-volume index, the suggested MMOSSA method is compared to five relevant multi-objective optimization strategies. The findings demonstrate that the MMOSSA Pareto fronts offer better solutions across all the assessments.

An Amplified Salp Swarm Optimization Algorithm for Maximum Power Point Tracking Control of Proton Exchange Membrane Fuel Cells

The proton exchange membrane fuel cell (PEMFC), one of the many fuel cell varieties on the market, is distinguished by its low temperatures of operation, high reliability, and extended lifespan. The fuel cell system's maximum output can only be achieved at one specific operating point under varying operating circumstances. Hence, it is essential to get the most power possible from the PEMFC for improved functioning and optimal exploitation of grid systems. Thus, the design of a maximum power point tracking (MPPT) controller for a PEMFC power system using an amplified salp swarm optimization (AS2O) algorithm is focused on. Here, an interleaved Luo converter is also used to produce a regulated output voltage with less switching stress and frequency. Moreover, the proposed work intends to effectively satisfy the energy demand of grid systems by obtaining the maximum electrical energy from the PEMFCs with less harmonics and low system complexity. The simulation and performance results of the proposed AS2O‐MPPT controlling model are validated and compared using several parameters using MATLAB/Simulink tool. Using the proposed controlling technique, the total harmonics distortion is reduced up to 1.16%, with the settling time of 0.0062 s, rise time of 0.0010 s, and peak overshoot of 0.255 W.

Synthesis of Elliptical Antenna Array using Hybrid SSWOA Algorithm

In terms of research, the elliptical antenna arrays (EAA) synthesis is relatively novel. As it does not have to be circular in construction, this novel synthesis can maneuver the primary beam in the right direction, making it easier to realize. The amplitude and angular location of the ellipse, as well as the eccentricity of the ellipse, are all taken into account in the optimization process. The proposed hybrid algorithm is the SSWOA (Salp Swarm Whale Optimization Algorithm), which combines the Salp Swarm Optimization Algorithm (SSA) with the Whale Optimization Algorithm (WOA). The SSA algorithm serves as a guide, while the WOA algorithm serves as a helper in this method. We discover that optimization has a faster convergence time and high convergence accuracy when considering the benefits of SSA and WOA and applying them to the synthesis of antenna array layouts. If Griewank, Rosenbrock, Sphere, and Rastrigin test functions are used, it’s worth noting that the hybrid method outperforms both WOA and SSA.

Salp swarm optimization algorithm based MPPT design for PV-TEG hybrid system under partial shading conditions

This paper proposes an innovative strategy to integrate thermoelectric generator (TEG) and photovoltaic (PV) systems, aiming to enhance energy production efficiency by addressing the significant waste heat generated during traditional PV system operation. Additionally, photovoltaic-thermoelectric generator (PV-TEG) hybrid system encounters the dual challenge of partial shading conditions (PSC) and non-uniform temperature distribution (NTD). Thus, salp swarm optimization (SSA) is introduced to simultaneously tackle the negative impacts of PSC and NTD. In contrast to alternative meta-heuristic algorithms (MhAs) and conventional mathematical approaches, the streamlined and effective optimization mechanism inherent to SSA affords a shorter optimization time, while mitigating the risk of the PV-TEG hybrid system’s optimization outcomes being confined to local maximum power points (LMPP). Furthermore, the optimization performance of SSA for PV-TEG hybrid systems is assessed via four case studies, including start-up test, stepwise variations in solar irradiation at constant temperature, stochastic change in solar irradiation, and field measured data for typical days in Hong Kong, in which simulation results show that SSA evinces unparalleled global exploration and local search capabilities, yielding heightened energy output (up to 43.75%) and effectively suppressing power fluctuations in the PV-TEG hybrid system (as evidenced by ΔVavg and ΔVmax).