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Pilot Allocation Optimization Using Enhanced salp swarm algorithm for Sparse Channel Estimation

China Communications 2021年第11期18卷 141-154页

作者： Ning Li Kun Yao Zhongliang Deng Xiaohao Zhao Jianchang Qin School of Electronic Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China

Pilot pattern has a significant effect on the performance of channel estimation based on compressed ***,because of the influence of the number of subcarriers and pilots,the complexity of the enumeration method is computationally *** meta-heuristic algorithm of the salp swarm algorithm(SSA)is employed to address this *** most meta-heuristic algorithms,the SSA algorithm is prone to problems such as local optimal values and slow *** this paper,we proposed the CWSSA to enhance the optimization efficiency and robustness by chaotic opposition-based learning strategy,adaptive weight factor,and increasing local *** show that the test results of the CWSSA on most benchmark functions are better than those of other meta-heuristic ***,the CWSSA algorithm is applied to pilot pattern optimization,and its results are better than other methods in terms of BER and MSE.

关键词： OFDM channel estimation CWSSA compressed sensing salp swarm algorithm pilot allocation

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Size optimization of planar truss systems using the modified salp swarm algorithm

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ENGINEERING OPTIMIZATION 2024年第4期56卷 469-485页

作者： Altay, Onur Cetindemir, Oguzhan Aydogdu, Ibrahim Antalya Bilim Univ Civil Engn Dept Antalya Turkiye Bogazici Univ Kandilli Observ & Earthquake Res Inst Earthquake Engn Dept Istanbul Turkiye Gebze Tech Univ Civil Engn Dept Kocaeli Turkiye Akdeniz Univ Civil Engn Dept Antalya Turkiye

This work evaluates the performance of the salp swarm algorithm (SSA) for truss system optimization problems and presents a novel method called the modified salp swarm algorithm (MSSA). Five truss structures, previously optimized by metaheuristics and containing discrete and continuous variables, were used for the evaluations. Size and size-shape optimization types were considered. Although the SSA performs poorly and has convergence issues in initial random solutions, it reaches comparable solutions to previously published results, particularly in continuous problems. In contrast, the MSSA achieves the best solutions for discrete problems and is relatively close to the best results in the reference literature on continuous problems. Moreover, the MSSA convergence curves exhibit a modest increase in convergence rates, especially for discrete problems. It is envisaged that the findings will contribute to improving solution performance, convergence speed and security for future real-world applications.

关键词： Structural optimization metaheuristic techniques swarm intelligence truss systems salp swarm algorithm

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Discrete salp swarm algorithm for Euclidean Travelling Salesman Problem

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APPLIED INTELLIGENCE 2023年第10期53卷 11420-11438页

作者： Panwar, Karuna Deep, Kusum Indian Inst Technol Roorkee Dept Math Roorkee Uttar Pradesh India

The salp swarm algorithm (SSA) is one of the recently proposed swarm intelligence-based algorithms, which finds its motivation in the swarming behaviour of salps when navigating and foraging in the ocean. The SSA is essentially derived to solve optimization problems that have continuous search space. SSA is a simple to implement and competitive algorithm that has been proven helpful in a variety of real-world applications. It has been explored over several optimization problems so far. In this study, an enhanced discrete version of SSA is proposed to solve the Travelling Salesman Problem (TSP). As TSP is a combinatorial optimization problem, the classical SSA is modified using swap, shift and symmetry operators for global exploration and local exploitation. Also, the 2-opt method has been incorporated with the proposed algorithm to improve the local search ability of the algorithm while addressing discrete problems. The proposed Discrete salp swarm algorithm (DSSA) has evaluated over 45 TSP instances, and the computational results showed that it is a promising algorithm. To assess its performance, the proposed algorithm's results are compared with well-known algorithms such as Genetic algorithm, Artificial Bee Colony, Spider Monkey algorithm, Jaya algorithm, Black Hole, Symbiotic Organism Search etc. The proposed algorithm significantly outperformed these algorithms for a majority of TSP instances.

关键词： salp swarm algorithm Travelling salesman problem Combinatorial optimization problem Metaheuristics 2-opt algorithm

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An enhanced opposition-based salp swarm algorithm for global optimization and engineering problems

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022年第1期13卷 129-150页

作者： Hussien, Abdelazim G. Fayoum Univ Fac Sci Faiyum Egypt

Real-world optimization problems need methods and techniques to explore search space and avoid local optima in order to find the best solutions. salp swarm algorithm (SSA) is a novel algorithm which mimics the behavior of salps during foraging and navigation in the ocean. Unfortunately, like other similar algorithms, SSA may fail in avoiding local optima and have a slow convergence curve. In this work, a novel version of SSA named OBSSA is introduced which is depends on Opposition-Based Learning (OBL) Strategy. The proposed algorithm consists of 2 stages: in the first stage where OBL is used to enhance the initialization stage and in the second stage where OBL is used in the updating process of the population in every iteration. To investigate the performance of OBSSA, 30 functions from IEEE Congress on Evolutionary Computation (IEEE CEC2014) and 6 engineering problems are tested and compared with 8 state-of-art algorithms. Moreover, nonparametric test Wilcoxon's rank-sum is performed at 5% level to test the significant of the obtained results between OBSSA and SSA. The results show that OBSSA can get competitive results with other algorithms.

关键词： salp swarm algorithm SSA Opposition-based Optimization Engineering problems

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Multi-threshold Image Segmentation based on an improved salp swarm algorithm: Case study of breast cancer pathology images

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COMPUTERS IN BIOLOGY AND MEDICINE 2024年 168卷 107769-107769页

作者： Guo, Hongliang Li, Mingyang Liu, Hanbo Chen, Xiao Cheng, Zhiqiang Li, Xiaohua Yu, Helong He, Qiuxiang Jilin Agr Univ Coll Informat Technol Changchun 130118 Peoples R China Jilin Agr Univ Coll Resources & Environm Changchun 130118 Peoples R China Sci & Technol Innovat Ctr Hlth Prod & Med Mat Char Changchun 130000 Peoples R China Wenzhou Univ Library Wenzhou 325035 Zhejiang Peoples R China Wenzhou Med Univ Affiliated Hosp 1 Dept Pathol Wenzhou 325000 Peoples R China

Breast cancer poses a significant risk to women's health, and it is essential to provide proper diagnostic support. Medical image processing technology is a key component of all supporting diagnostic techniques, with Image Segmentation (IS) being one of its primary steps. Among various methods, Multilevel Image Segmentation (MIS) is considered one of the most effective and straightforward approaches. Many researchers have attempted to improve the quality of image segmentation by combining different metaheuristic algorithms with MIS. However, these methods often suffer from issues such as low convergence accuracy and a proclivity for converging towards Local Optima (LO). To overcome these challenges, this study introduces an integrated approach that combines the salp swarm algorithm (SSA), Slime Mould algorithm (SMA) and Differential Evolution (DE) algorithm. In this manuscript, we introduce an innovative hybrid MIS model termed SDSSA, which leverages elements from the SSA, SMA and DE algorithms. The SDSSA model fundamentally relies on non-local means 2D histogram and 2D Kapur's entropy. To evaluate the proposed method effectively, we compare it initially with similar algorithms using the IEEE CEC2014 benchmark functions. The SDSSA showcases enhanced convergence velocity and precision relative to similar algorithms. Furthermore, this paper proposes an excellent MIS method. Subsequently, IS experiments were conducted separately at both low and high threshold levels. The test results demonstrate that the segmentation outcomes of MIS, at both low and high threshold levels, outperform other methods. This validates SDSSA as a superior segmentation technique that provides practical assistance for future research in breast cancer pathology image processing.

关键词： salp swarm algorithm Slime mould algorithm Differential evolution Multi-threshold image segmentation Kapur's entropy

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Passive sonar target classification using multi-layer perceptron trained by salp swarm algorithm

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OCEAN ENGINEERING 2019年 181卷 98-108页

作者： Khishe, Mohammad Mohammadi, Hassan Imam Khomeini Marine Sci Univ Dept Elect Engn Nowshahr Iran

Due to the variability of the radiated signal of the sonar targets, passive sonar target classification is a challenging problem in the real world application. Adaptive Mel-frequency cepstral coefficients (MFCCs) and multi-layer perceptron (MLP) approaches are proposed, including cepstral features to alleviate dataset's dimension and MLP network to adapt the variability in changing condition. In spite of the capabilities of MLP networks, low classification accuracy, and getting stuck in local minima are the main shortcomings of MLP networks. To overcome these shortcomings, this paper proposes the use of the newly introduced salp swarm algorithm (SSA) for training MLP network. In order to investigate the efficiency of the proposed classifier, four high-dimensional benchmark functions, as well as an experimental passive sonar data set, are employed. The designed classifier is compared to gray wolf optimizer (GWO), biogeography-based optimization (BBO), interior search algorithm (ISA), and group method of data handling (GMDH) in terms of classification accuracy, entrapment in local minima, and convergence speed. The results showed that the proposed classifier is more efficient than the other benchmark algorithms;therefore, the SSA classifies sonar data set as much as 0.9017 percent better than GMDH being the best results among the other classifiers.

关键词： Sonar Classification MLP Neural network salp swarm algorithm

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A novel strategy based on salp swarm algorithm for extracting the maximum power of proton exchange membrane fuel cell

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2021年第8期46卷 6087-6099页

作者： Fathy, Ahmed Abdelkareem, Mohammad Ali Olabi, A. G. Rezk, Hegazy Jouf Univ Dept Elect Engn Fac Engn Sakakah Saudi Arabia Zagazig Univ Elect Power & Machine Dept Fac Engn Zagazig Egypt Univ Sharjah Dept Sustainable & Renewable Energy Engn POB 27272 Sharjah U Arab Emirates Univ Sharjah Ctr Adv Mat Res POB 27272 Sharjah U Arab Emirates Menia Univ Dept Chem Engn Fac Engn Al Minya Egypt Aston Univ Sch Engn & Appl Sci Mech Engn & Design Birmingham B4 7ET W Midlands England Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Addawaser Wadi Aldawaser 11991 Saudi Arabia Menia Univ Dept Elect Engn Fac Engn Al Minya 61111 Egypt

Cell temperature and water content of the membrane have a significant effect on the performance of fuel cells. The current-power curve of the fuel cell has a maximum power point (MPP) that is needed to be tracked. This study presents a novel strategy based on a salp swarm algorithm (SSA) for extracting the maximum power of proton-exchange membrane fuel cell (PEMFC). At first, a new formula is derived to estimate the optimal voltage of PEMFC corresponding to MPP. Then the error between the estimated voltage at MPP and the actual terminal voltage of the fuel cell is fed to a proportional-integralderivative controller (PID). The output of the PID controller tunes the duty cycle of a boost converter to maximize the harvested power from the PEMFC. SSA determines the optimal gains of PID. Sensitivity analysis is performed with the operating fuel cell at different cell temperature and water content of the membrane. The obtained results through the proposed strategy are compared with other programmed approaches of incremental resistance method, Fuzzy-Logic, grey antlion optimizer, wolf optimizer, and mine-blast algorithm. The obtained results demonstrated high reliability and efficiency of the proposed strategy in extracting the maximum power of the PEMFC. (c) 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： PEMFC MPPT salp swarm algorithm Energy efficiency

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Effective Epileptic Seizure Detection Using Enhanced salp swarm algorithm-based Long Short-Term Memory Network

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IETE JOURNAL OF RESEARCH 2024年第2期70卷 1538-1555页

作者： Rani, T. Jhansi Kavitha, D. Jawaharlal Nehru Technol Univ Dept Comp Sci & Engn Anantapur Andhra Pradesh India G Pulla Reddy Engn Coll Dept Comp Sci & Engn Kurnool Andhra Pradesh India

In this manuscript, a deep learning method is implemented for automatic detection of abnormal and normal Electroencephalogram (EEG) signals, especially for early diagnosis of Epileptic Seizure (ES) disease. Firstly, 8th order Butter Worth Filter (BWF) is applied for removing artifacts from Bern Barcelona (BB), Bonn University (BU), and Temple University Hospital (TUH) datasets. Next, the swarm decomposition method is employed for signal decomposition where it provides a significant frequency localization by separating the essential signal components from the denoised EEG signals. Then, the semantic feature extraction is performed for extracting feature values from the decomposed signals. The feature extraction includes the benefits like reduction in overfitting risk, accuracy improvement, and speed up the training process. Further, the extracted multidimensional feature values are reduced by proposing enhanced salp swarm algorithm (SSA), where it selects optimum feature values for classification. The enhanced SSA uses Whale Optimization algorithm (WOA) for identifying the potential feature values from the multi-dimensional feature space. Lastly, the selected features are fed to the Long Short Term Memory (LSTM) network for classifying abnormality and normality of seizures. The experimental results show that the enhanced SSA based LSTM network classifies abnormality and normality of seizure with classification accuracy of 97.84%, 99.32%, and 99.20% on the BU, BB, and TUH datasets.

关键词： Epileptic seizure long short-term memory network salp swarm algorithm swarm decomposition method 8th-order butter worth filter

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Optimum design of both linear and planar sparse arrays with sidelobe level reduction using salp swarm algorithm

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JOURNAL OF ELECTROMAGNETIC WAVES AND APPLICATIONS 2021年第5期35卷 690-704页

作者： Luo, Zailei Liu, Feng Zou, Zhibin Guo, Shaojun Shen, Tongsheng Natl Innovat Inst Def Technol Adv Interdisciplinary Technol Res Ctr Beijing 100071 Peoples R China Amry Engn Univ PLA Natl Def Engn Coll Nanjing Peoples R China

Sparse arrays play an important role in the widely used acoustic and electromagnetic applications such as sonar, radar, and satellite. In this paper, a recently proposed swarm intelligence algorithm, salp swarm algorithm (SSA), is introduced for design of both linear and planar sparse antenna arrays with optimum sidelobe level (SLL). The design of sparse antenna arrays can be regarded as a nonlinear optimization problem, which the main optimization goal is finding optimum excitations and positions for antenna elements to achieve antenna array beam pattern with desired SLL. The SSA is a new kind of swarm intelligence algorithm which inspired by the swarming behavior of salps. Compared to other evolutionary algorithms, the SSA requires few algorithm-specified tuning parameters, which makes it simple and easy to use for engineering. Illustrative examples of both linear and planar arrays are given to verify the validity of the SSA for SLL reduction of sparse arrays.

关键词： salp swarm algorithm sparse arrays low sidelobe level swarm intelligence

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Emended salp swarm algorithm for multiobjective electric power dispatch problem

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APPLIED SOFT COMPUTING 2020年 90卷 106172-000页

作者： Kansal, Veenus Dhillon, J. S. St Longowal Inst Engn & Technol Longowal Elect & Instrumentat Engn Dept Sangrur India

This paper presents an emended salp swarm algorithm (ESSA) which is basically the extension of the basic salp swarm algorithm (SSA) to solve multiobjective electric power load dispatch problem. The main inspiration behind ESSA is the swarming behavior and reproduction cycle of salps. salps, in the chain, move across a multi-dimensional search space to aim for the food source (global solution). Owing to the searching behavior of SSA that makes the algorithm prone to premature convergence, the solitary and colonial reproduction phase of salp has been introduced to improve the convergence behavior along with their swarming behavior The multiobjective optimization problem is firstly converted into scalar objective exploiting fuzzy set theory and the conflicting nature of objectives is resolved by cardinal priority ranking. The variable elimination method with exterior penalty is used to handle the physical and operational constraints of generating units. The validation of the proposed ESSA has been examined on the standard benchmark functions and seven EcLD test systems including both scalar and multiple objectives. The statistical analysis based on Wilcoxon sign rank test, supports that results achieved by the algorithm are superior to the other competing algorithms. So, the proposed algorithm (ESSA) is found a promising algorithm. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Exploration Economic-emission load dispatch salp swarm algorithm Exploitation

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