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

ENERGY CONVERSION AND MANAGEMENT 2023年第1期292卷

作者： Yang, Bo Wu, Shaocong Huang, Jianxiang Guo, Zhengxun Wang, Jiarong Zhang, Zijian Xie, Rui Shu, Hongchun Jiang, Lin Kunming Univ Sci & Technol Fac Elect Power Engn Kunming 650500 Peoples R China CSG EHV Transmiss Co Kunming Bur Kunming 650217 Peoples R China Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ England

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 distri-bution (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 optimiza-tion 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 tem-perature, 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 Delta Vavg and Delta Vmax).

关键词： salp swarm optimization algorithm PV-TEG hybrid system Maximum power point tracking Partial shading conditions Non-uniform temperature distribution

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Investigation of 180W separation by transient single withdrawal cascade using salp swarm optimization algorithm

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NUCLEAR ENGINEERING AND TECHNOLOGY 2023年第4期55卷 1225-1232页

作者： Imani, Morteza Aghaie, Mahdi Shahid Beheshti Univ Engn Dept GC POB *** Tehran Iran

The 180W is the lightest isotope of Tungsten with small abundance ratio. It is slightly radioactive (a decay), with an extremely long half-life. Its separation is possible by non-conventional single withdrawal cascades. The 180W is used in radioisotopes production and study of metals through gamma-ray spec-troscopy. In this paper, single withdrawal cascade model is developed to evaluate multicomponent separation in non-conventional transient cascades, and available experimental results are used for validation. Numerical studies for separation of 180W in a transient single withdrawal cascade are per-formed. Parameters affecting the separation and equilibrium time of cascade such as number of stages, cascade arrangements, feed location and flow rate for a fixed number of gas centrifuges (GC) are investigated. The salp swarm algorithm (SSA) as a bio-inspired optimization algorithm is applied as a novel method to minimize the feed consumption to obtain desired concentration in the collection tank. Examining different cascade arrangements, it is observed in arrangements with more stages, the sepa-ration is further efficient. Based on the obtained results, with increasing feed flow rate, for fixed product concentration, the cascade equilibrium time decreases. Also, it is shown while the feed location is the farthest stage from the collection tank, the separation and cascade equilibrium time are well-organized. Finally, using SSA optimal parameters of the cascade is calculated, and optimal arrangement to produce 5 gr of 180W with 90% concentration in the tank, is proposed.(c) 2022 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Single withdrawal cascades 180 W isotope Transient condition Simulation q iteration method salp swarm optimization algorithm

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Peak-to-average power ratio reduction of orthogonal frequency division multiplexing signals using improved salp swarm optimization-based partial transmit sequence model

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ETRI JOURNAL 2025年第2期47卷 256-269页

作者： Tripathi, Vandana Patel, Prabhat Jain, Prashant Kumar Shukla, Shailja Jabalpur Engn Coll Dept Elect & Commun Jabalpur Madhya Pradesh India Rajiv Gandhi Proudyogiki Vishwavidyalaya Dept Elect & Commun Bhopal Madhya Pradesh India Jabalpur Engn Coll Dept Elect Engn Jabalpur Madhya Pradesh India

Several peak-to-average power ratio (PAPR) reduction methods have been used in orthogonal frequency division multiplexing (OFDM) applications. Among the available methods, partial transmit sequence (PTS) is an efficient PAPR reduction method but can be computationally expensive while determining optimal phase factors (OPFs). Therefore, an optimization algorithm, namely, the improved salp swarm optimization algorithm (ISSA), is incorporated with the PTS to reduce the PAPR of the OFDM signals with limited computational cost. The ISSA includes a dynamic weight element and L & eacute;vy flight process to improve the global exploration ability of the optimization algorithm and to control the global and local search ability of the population with a better convergence rate. Three evaluation measures, namely, the complementary cumulative distribution function (CCDF), bit error rate (BER), and symbol error rate (SER), demonstrate the efficacy of the PTS-ISSA model, which achieves a lower PAPR of 3.47 dB and is superior to other optimization algorithms using the PTS method.

关键词： communication system orthogonal frequency division multiplexing partial transmit sequence peak-to-average power ratio salp swarm optimization algorithm

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optimization of Load Sharing in Compressor Station Based on Improved salp swarm algorithm

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ENERGIES 2022年第15期15卷 5720页

作者： Zhang, Jiawei Li, Lin Zhang, Qizhi Wu, Yanbin Xian Shiyou Univ Sch Elect Engn Xian 710065 Peoples R China Shaanxi Key Lab Measurement & Control Technol Oil Xian 710065 Peoples R China Changqing Oilfield Co Oil Prod Plant 1 Yanan 716000 Peoples R China

In long-distance gas transmission pipelines, there are many booster compressor stations consisting of parallel compressors that provide pressure for the delivery of natural gas. So, it is economically important to optimize the operation of the booster compressor station. The booster compressor station optimization problem is a typical mixed integer nonlinear programming (MINLP) problem, and solving it accurately and stably is a challenge. In this paper, we propose an improved salp swarm algorithm based on good point set, adaptive population division and adaptive inertia weight (GASSA) to solve this problem. In GASSA, three improvement strategies are utilized to enhance the global search capability of the algorithm and help the algorithm jump out of the local optimum. We also propose a constraint handling approach. By using semi-continuous variables, we directly describe the on or off state of the compressor instead of using auxiliary binary variables to reduce the number of variables and the difficulty of solving. The effectiveness of GASSA is firstly verified using eight standard benchmark functions, and the results show that GASSA has better performance than other selected algorithms. Then, GASSA is applied to optimize the booster compressor station load distribution model and compared with some well-known meta-heuristic algorithms. The results show that GASSA outperforms other algorithms in terms of accuracy and reliability.

关键词： compressor station optimization semi-continuous variable salp swarm optimization algorithm load sharing

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swarm-Based Extreme Learning Machine Models for Global optimization

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Computers, Materials & Continua 2022年第3期70卷 6339-6363页

作者： Mustafa Abdul Salam Ahmad Taher Azar Rana Hussien Artificial Intelligence Department Faculty of Computers and Artificial IntelligenceBenha UniversityBenha13518Egypt Scientific Computing Department Faculty of Computers and Artificial IntelligenceBenha UniversityBenha13518Egypt

Extreme Learning Machine(ELM)is popular in batch learning,sequential learning,and progressive learning,due to its speed,easy integration,and generalization ***,Traditional ELM cannot train massive data rapidly and efficiently due to its memory residence,high time and space *** ELM,the hidden layer typically necessitates a huge number of ***,there is no certainty that the arrangement of weights and biases within the hidden layer is *** solve this problem,the traditional ELM has been hybridized with swarm intelligence optimization *** paper displays five proposed hybrid algorithms“salp swarm algorithm(SSA-ELM),Grasshopper algorithm(GOA-ELM),Grey Wolf algorithm(GWO-ELM),Whale optimizationalgorithm(WOA-ELM)andMoth Flame optimization(MFO-ELM)”.These five optimizers are hybridized with standard ELM methodology for resolving the tumor type classification using gene expression *** proposed models applied to the predication of electricity loading data,that describes the energy use of a single residence over a fouryear *** the hidden layer,swarm algorithms are used to pick a smaller number of nodes to speed up the execution of *** best weights and preferences were calculated by these algorithms for the hidden *** results demonstrated that the proposed MFO-ELM achieved 98.13%accuracy and this is the highest model in accuracy in tumor type classification gene expression *** in predication,the proposed GOA-ELM achieved 0.397which is least RMSE compared to the other models.

关键词： Extreme learning machine salp swarm optimization algorithm grasshopper optimization algorithm grey wolf optimization algorithm moth flame optimization algorithm bio-inspired optimization classification model and whale optimization algorithm

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Optimal knee osteoarthritis diagnosis using hybrid deep belief network based on salp swarm optimization method

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第13期34卷 e6913-e6913页

作者： Balasubramanian, Kishore Kishore, Ramya Krishnamoorthy, Gayathri Devi Dr Mahalingam Coll Engn & Technol Pollachi India PA Coll Engn & Technol Pollachi India Dr NGP Inst Technol Coimbatore Tamil Nadu India

Osteoarthritis (OA) damages the articular cartilage of the knee and is a severe degenerative joint condition. Currently, OA diagnosis is carried out by symptom analysis and progressive evaluation of the radiographs, although the method is subjective. This work presents a novel computer aided diagnostic system to diagnose the severity of the disease in its early stages. The proposed method comprises of stages that include image pre-processing, extraction of features based on discrete wavelet decomposition, histogram, GLCM and texture features, and classification by stacked up-RBM Deep Belief Networks (Hybrid DBN) finally. The performance of HDBN is improved by optimizing the hyper parameters with the salp swarm optimization algorithm (SSA). Experiments conducted on the Kaggle database consisting of 7503 images demonstrated an overall accuracy of 99.45% with 100% sensitivity and specificity. The impact on isolated and combined feature contributions is also analyzed using 10-fold cross validation (CV). The robustness of the algorithm is tested on degraded images of salt-pepper, Gaussian, and Poisson noise, which proved the effectiveness of the method. Comparison of the method proposed with similar techniques is done by employing different optimization algorithms and classifiers.

关键词： classification deep belief network feature extraction knee osteoarthritis KL grades salp swarm optimization algorithm

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An energy efficient data fault prediction based clustering and routing protocol using hybrid ASSO with MERNN in wireless sensor network

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TELECOMMUNICATION SYSTEMS 2024年第1期86卷 61-82页

作者： Mahalakshmi, G. Ramalingam, S. Manikandan, A. Govt Coll Engn Dept CSE Amravati India Sri Eshwar Coll Engn Dept ECE Coimbatore India SRM Inst Sci & Technol Dept ECE Chennai India

Wireless sensor networks (WSNs) and Internet of Things (IoT) are essential for numerous applications. WSN nodes often operate on limited battery capacity, so energy efficiency is a significant problem for clustering and routing. In addition to these limitations, one of the primary issues of WSNs is achieving reliability and security of transmitted data in vulnerable environments to prevent malicious node attacks. This work aims to develop a secure and energy-efficient routing protocol for fault data prediction to enhance WSNs network lifespan and data reliability. The proposed technique has three major phases: cluster construction, optimal route selection, and intrusion detection. The adaptive shark smell optimization (ASSO) technique was initially used with three input parameters for CH selection. These parameters are the residual energy, the distance to the BS, and the node density. After clustering, salp swarm optimization (SSO) is used to select the optimum path for data transmission between clusters, resulting in an energy-efficient WSN. Finally, to ensure the security of cluster-based WSNs, an effective intrusion detection system based on a modified Elman recurrent neural network (MERNN) is implemented to detect the presence of intrusions in the network. The experimental results show that it outperforms the competing methods in various performance metrics. The performance results of quality of service (QoS) parameters are expressed as dispersion value (0.8072), packet delivery rate (98%), average delay (160 ms), network lifetime (3200 rounds), and the accuracy of this method is 99.2%. Compared to the SVM, ELM, HMM, and MK-ELM protocols, the proposed protocol increases network lifetime by 77%, 60%, 45.4%, and 14.2%, respectively.

关键词： Wireless sensor network Fault detection Adaptive shark smell optimization algorithm salp swarm optimization algorithm Modified Elman recurrent neural network

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Modeling and design optimization of master manipulator for robot-assisted minimally invasive surgery

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2023年第7期237卷 1537-1549页

作者： Gao Chao Pan Bo Niu Guojun Fu Yili Harbin Inst Technol State Key Lab Robot & Syst Room 410Cl Bldg2 Yikuang St Harbin 150080 Heilongjiang Peoples R China Zhejiang Sci Tech Univ Sch Mech Engn & Automat Hangzhou Zhejiang Peoples R China

In the minimally invasive surgical robot system, the surgeon controls the movement of instruments with master manipulators, which is a typical physical human-robot interaction system. In this paper, the surgeon is fully taken into consideration for structural design optimization. The kinematics model of the surgeon's arm was established to describe the irregular shape of surgeon's operation space;The surgeon console model was established to describe the relative position between the master manipulators and the surgeon's arm. To make master manipulator could behavior great dexterity in surgery, the dexterity index of surgeon operation space was proposed. Combined with the minimum gravity torque index and the redundancy index, this multi-objective optimization problem was solved by salp swarm optimization algorithm. Compared with the pre-optimization master manipulator, the performances of the optimized manipulator are overall improved by 10.3%, 23.0%, and 41.2% respectively in animal experiment.

关键词： Multi-objective design optimization robot-assisted minimally invasive surgery master manipulator salp swarm optimization algorithm

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Enhancing Brain Tumor Diagnosis with Substructure Aware Graph Neural Networks and Fuzzy Linguistic Segmentation 2

Enhancing Brain Tumor Diagnosis with Substructure Aware Grap...

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2nd International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)

作者： Srinivasan, P. Santhosh Regan, M. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Math Chennai Tamil Nadu India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Math Chennai Tamil Nadu India

ISBN: (纸本)9798331540661;9798331540678

Brain tumors, which are characterized by uncontrolled cell proliferation, pose significant diagnostic and treatment challenges that require precise medical intervention. Current methods for brain tumor detection often suffer from low precision, accuracy, and high error rates. To address these issues, this study proposes a novel approach utilizing a Random-Coupled Neural Network with Substructure Aware Graph Neural Network Attention optimized by the salp swarm optimization algorithm (RCNN-SAGNN-SSOA) for accurate brain tumor detection. The proposed approach starts with pre-processing and feature extraction using a Contextual Attention Network with Convolutional Auto Encoder (CANCAE) to remove noise and enhance feature extraction efficiency. Image segmentation is performed using Adaptively Regularized Kernel-based Fuzzy C Means (ARKFCM). The classification is then carried out using the RCNN-SAGNN, with optimization provided by the salp swarm optimization algorithm (SSOA) to accurately differentiate between normal and abnormal brain conditions. Evaluations on the Brats 2021 and MRI datasets demonstrate that the proposed RCNN-SAGNN-SSOA achieves an accuracy of 99.78% and a recall of 97.34%, significantly outperforming existing methods. This high level of accuracy highlights the model's potential to significantly improve the speed and effectiveness of brain tumor diagnosis, offering substantial benefits for patient treatment.

关键词： Brain Tumor Detection Adaptively Regularized Kernel-based Fuzzy C Means Random-coupled Neural Network salp swarm optimization algorithm

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Low complexity multiobjective finite impulse response filter design using salp swarm algorithm and its improved version

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INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 2021年第6期34卷 e2914-e2914页

作者： Srivastava, Savita Dwivedi, Atul Kumar Nagaria, Deepak Bundelkhand Inst Engn & Technol Jhansi Uttar Pradesh India

Recent trend indicates that the assessment of low complexity is crucial for an effectual design of digital filter. Earlier the existing algorithms solely focused on reducing the pass band ripples and the stop band ripples in spite of the requirement of low implementation complexity in finite impulse response (FIR) filter design. In this regard, the optimization-based FIR filter using multiobjective improved salp swarm optimization algorithm have been designed. The proposed method not only minimizes the ripples of pass band and stop band but also minimizes the transition band, number of sparsity, which is used to implement a FIR filter that abides the specified frequency. The nondominated solution of filter design has a set of an optimum solution as pareto front that maintains the trade-off between the various specifications. The designed FIR filter has been compared with other reported method to validate the applicability of the proposed method. The evaluation and analysis have been performed regrading minimum pass band ripples, minimum stop band ripple, minimum transition band, and implementation complexity. To prove the practical applicability of the proposed method, the designed FIR filters have been implemented in Xilinx ISE14.7 (Virtex-VII) environment. The comparison result shows that the proposed method performs better than all other reported method in achieving minimum ripples and implementation complexity.

关键词： evolutionary optimization FIR filter low-complexity design salp swarm optimization algorithm sparse designs

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