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salp swarm algorithm using lens opposition-based learning and local search for constrained optimization problems

Iran Journal of Computer Science 2025年 1-28页

作者： Majumdar, Parijata Mitra, Sanjoy Department of Computer Science and Engineering Indian Institute of Information Technology Agartala Jirania 799046 India Department of Computer Science and Engineering Tripura Institute of Technology Agartala Narsingarh 799009 India

The salp swarm algorithm (SSA) simulates how salps forage and travel in the ocean. SSA suffers from low initial population diversity, improper balancing of exploration and exploitation, and slow convergence speed. Thus, we have proposed a novel variant of SSA (LOLSSA) by integrating lens opposition learning with the fundamental evolutionary techniques of SSA to increase the initial population variety. During exploration, the information about the local best position for each individual is effectively shared with the follower, using the salp position update process to avoid getting stuck at the local optimum. At the end of each iteration, the local search algorithm is utilized to increase exploitation. Additionally, the leader’s salp position incorporates inertia weight to speed up the convergence speed. swarm intelligence algorithms have proven their efficacy in feature selection, since they search for an optimal set of features in a large search area that will have the best impact on the accuracy of the training system. LOLSSA is thus employed for feature selection to select informative features in 11 high-dimensional datasets, where it outperforms well-known metaheuristics in average fitness, accuracy, and average number of feature reductions. In the software-as-a-service cloud computing model, a task scheduling problem is solved to satisfy the QoS requirement of both the service provider and the user. Experiments show that LOLSSA optimizes this problem in terms of different performance metrics. LOLSSA also outperforms other metaheuristics regarding 23 well-known benchmark functions, IEEE CEC-C06 2019, and CEC-06 2020 benchmark functions, and in solving engineering design problems. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Convergence speed Feature selection Global optimization Lens opposition learning salp swarm algorithm Task scheduling problem

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Anti-Interference Sliding Mode Control of PMSM Based on Improved salp swarm algorithm

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2025年第4期20卷 614-627页

作者： Yao, Guozhong Yuan, Peng Han, Shaojun Wang, Guiyong Kunming Univ Sci & Technol Fac Transportat Engn Kunming Peoples R China

Aiming at the problem that the control performance of permanent magnet synchronous motor is easily affected by load disturbance during operation, a fractional-order sliding mode control method combining fractal-order sliding mode controller (FOSMC) and fractional-order sliding mode load torque observer (FOSMO) is proposed. The parameters of this method are tuned using the improved salp swarm algorithm (ISSA). Initially, the fractional-order sliding mode controller is designed by applying fractional-order calculus theory to enhance the system's response speed. Subsequently, a fractional-order sliding mode load torque observer is developed to improve the system's anti-interference capability and robustness due to the slow response and low identification accuracy of traditional load torque observers. Lastly, the control parameters are self-tuned using the improved salp swarm algorithm due to the difficulty of adjusting them manually. Simulation and experimental results demonstrate that the proposed control method exhibits good convergence, no overshoot, minimal buffeting, and strong robustness against uncertain factors like load disturbances. (c) 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

关键词： PMSM sliding mode control salp swarm algorithm performance optimization load torque observer

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Maximum power point tracking of solar photovoltaic under partial shading conditions based on improved salp swarm algorithm

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ELECTRIC POWER SYSTEMS RESEARCH 2025年 241卷

作者： Hou, Guolian Guo, Zhiqiang North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China

Owing to the nonlinear voltammetric characteristics of Photovoltaic(PV) cells, multi-peaks emerge on the voltammetric curve under partial shading conditions (PSCs). This phenomenon complicates the attainment of maximum power through maximum power point tracking (MPPT), thereby jeopardizing the stability and reliability of the PV cells' operation. To address the challenges posed by PSCs and enhance the precision and expeditiousness of MPPT in PV cells. This paper proposes a PV-MPPT method based on the improved salp swarm algorithm (ISSA). Firstly, to bolster the algorithm's exploratory capacity, tent chaotic initialization is employed to refine the initial search scope, thereby swiftly homing in on the proximity of the maximum power point(MPP). Secondly, during the algorithm's iterative phase, the tracking accuracy and convergence velocity are harmonized through an integration of Levy flight and lion swarm algorithm, and counteract the tendency of PV-MPPT to become ensnared at local MPP under PSCs. Ultimately, simulations conducted under different PSCs scenarios, along with comparison results with other methods, demonstrate that the PV system using ISSA achieves the highest MPPT tracking accuracy and the shortest tracking time. The proposed ISSA is adept at affording superior MPPT performance within PV systems.

关键词： Photovoltaic Maximum power point tracking Partial shading conditions salp swarm algorithm

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A novel fractional Bessel grey system model optimized by salp swarm algorithm for renewable energy generation forecasting in developed countries of Europe and North America

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COMPUTATIONAL & APPLIED MATHEMATICS 2025年第1期44卷 1-47页

作者： Ma, Xin Yuan, Hong Ma, Minda Wu, Lifeng Southwest Univ Sci & Technol Sch Math & Sci Mianyang 621010 Peoples R China Chongqing Univ Sch Management Sci & Real Estate Chongqing 400045 Peoples R China Southwest Univ Sci & Technol Ctr Informat Management & Serv Studies Sichuan Mianyang Peoples R China Chongqing Univ Sch Architecture & Urban Planning Chongqing 400045 Peoples R China Nanchang Inst Technol Sch Hydraul & Ecol Engn Nanchang 330099 Peoples R China

Renewable energy is believed to be one of the most low-carbon energy sources. Accurate forecasts of renewable energy generation can help the government make correct energy decisions. However, the sequence of renewable energy generation is irregular, nonlinear, and complex. And the existing techniques has problems such as being too linear or requiring a large amount of modeling data. Therefore, a new method is needed to solve these problems. A novel fractional grey model with Bessel function of the first kind as the grey input is proposed, and the discrete convolution solution is utilized to make the model viable in operation. The introduction of Bessel function of the first kind and fractional accumulation makes the model more adaptable and stable, and has better nonlinear fitting ability. The salp swarm algorithm is used to determine the optimal nonlinear parameters of the proposed model. Compared with the existing 15 models in 5 developed countries in Europe and North America, the minimum forecasting MAPE of the proposed model just reaches 0.58%, which is better than all comparison models. Since the model performs well in actual situations, it has high potential in accurately forecasting renewable energy generation. Hence, it can be considered as a reliable forecasting tool in the future.

关键词： Grey system model salp swarm algorithm Renewable energy Bessel function of the first kind Low-carbon energy Fractional Bessel grey system model

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A hybrid Improved salp swarm algorithm and Harris Hawk Optimizer for energy planning in microgrids with minimum operating cost

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INTERNATIONAL JOURNAL OF GREEN ENERGY 2025年第1期22卷 72-89页

作者： Seddaoui, Naoual Boulouma, Sabri Rahmani, Lazhar CDER Ctr Dev Energies Renouvelables Unite Dev Equipements Solaires UDES Tipasa Algeria Univ Ferhat ABBAS Fac Technol Elect Engn Dept Automat Lab Setif Algeria

Achieving optimal energy planning in Microgrids (MGs) is pivotal for addressing complex challenges associated with cost-effective and reliable energy supplies. This paper proposes a novel hybrid metaheuristic algorithm for optimal energy planning in microgrids using an Improved salp swarm algorithm with Harris Hawk Foraging (ISSAHF). This technique is based on an improved multi-leader salp swarm algorithm with an elite leader following strategy combined with Harris Hawks foraging. A simulation study is conducted on a low-voltage microgrid in off-grid and grid-connected modes. The optimization algorithm resulted in a daily average cost of 28.3370 in off-grid mode compared to 19.2676 in grid-connected one. Furthermore, the statistical study shows that the proposed algorithm outperforms well-established metaheuristic techniques regarding search capability and robustness. It yields mean optimal cost of 623.5248 in off-grid and 404.7475 for the grid-connected one, compared to other optimization techniques that vary from 667.2141 to 959.5747 in off-grid mode, and from 424.5841 to 813.932 in grid-connected mode. For robustness, the proposed technique performs well with a standard deviation of 20.765 compared to the best (17.024) and the worst (47.2423) cases in off-grid mode, while in grid-connected mode, it is 28.8771 compared to the best (21.6316) and the worst (45.3774) values.

关键词： Microgrid planning cost reduction emission reduction Harris Hawk Optimization multi-leader strategy salp swarm algorithm

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An amalgamation of deep neural networks optimized with salp swarm algorithm for cervical cancer detection

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COMPUTERS & ELECTRICAL ENGINEERING 2025年 123卷

作者： Bilal, Omair Asif, Sohaib Zhao, Ming Khan, Saif Ur Rehman Li, Yangfan Cent South Univ Sch Comp Sci & Engn 932 Lushan S Rd Changsha Hunan Peoples R China

Cervical cancer is a critical global health issue by affecting millions of women each year and causing high mortality rates if not diagnosed early. Early detection of cervical cancer significantly improves patient outcomes and survival rates. Traditional diagnostic approaches are frequently susceptible to errors, which can result in inaccurate diagnoses and being time-consuming. However, the emergence of machine learning and artificial intelligence provides innovative approaches to improve both diagnostic accuracy and efficiency. Individual deep learning models often face challenges in extracting the critical information essential for accurate prediction of disease in complex datasets. To tackle this, we propose a novel ensemble model that leverages the salp swarm algorithm (SSA) to enhance cervical cancer diagnosis. This approach employs three highly effective pre-trained models like DenseNet169, DenseNet201, and Xception for feature extraction. To improve feature attention, we integrated the Convolutional Block Attention Module into each of these models to make them our base models. Subsequently, the predictions generated from each base model within the ensemble are aggregated through a weighted aggregation approach and further optimized the ensemble model by intelligently assigning weights to each model through the SSA. We evaluated our model using two datasets including the Mendeley LBC dataset with four classes and the BloodMNIST Benchmark dataset with eight classes. This approach ensures the robustness and generalizability of the ensemble model by demonstrating its effectiveness on diverse datasets. Our proposed ensemble model demonstrates superior performance compared to existing state-of-the-art methods by attaining an impressive accuracy rates of 99.48% on the 4-class Mendeley LBC dataset and 95.23% on the 8-class BloodMNIST dataset. This work marks a significant advancement in the field of cervical cancer diagnosis. We evaluate our optimized ensemble model

关键词： Cervical cancer disease Deep learning Ensemble learning salp swarm algorithm Cancer cells classification

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Synergistic Integration of Renewable Energy and HVDC Technology for Enhanced Multi-objective Economic Emission Dispatch Using the salp swarm algorithm 5th

Synergistic Integration of Renewable Energy and HVDC Technol...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Gbadega, Peter Anuoluwapo Sun, Yanxia Univ Johannesburg Dept Elect & Elect Engn Sci ZA-2006 Johannesburg South Africa

ISBN: (纸本)9789819770038;9789819770045

This paper proposes a salp swarm algorithm (SSA), a unique optimization technique for the synergistic integration of renewable energy and High Voltage Direct Current (HVDC) technology to enhance the performance of multi-objective economic emission dispatch (MODED). The primary aim is to optimize both the economic and environmental aspects of power systems. A mathematical model for MODED based on Wind-Solar-Thermal integrated energy has been carefully constructed, considering variables like the valve point effect, equality constraints, and inequality constraints. The study determines optimal generation levels and associated costs for six thermal generating units under various power demands, exploring diverse scenarios such as Economic Dispatch for High Voltage-Alternating Current (HVAC) with Losses, Economic Dispatch for HVDC with Losses, Economic Dispatch for HVDC addressing challenges related to voltage instability, protection difficulties and losses in DC systems, Economic Dispatch HVAC & HVDC with Losses and Economic Dispatch for HVAC & HVDC with Renewable Energy (RE). To validate the model, tests have been conducted on the IEEE 30 Bus System with a substantial presence of renewable energy.

关键词： HVDC technology HVAC Renewable energy salp swarm algorithm Multi-objective dynamic economic dispatch

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Improve the efficiency of salp swarm algorithm for sewer system network design optimization problems

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OPSEARCH 2025年 1-32页

作者： Pham, Vu Hong Son Dau, Thuy Dung Vo, My Nguyet Ho Chi Minh City Univ Technol HCMUT Fac Civil Engn Dept Construct Engn & Management Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Vietnam

Sewer network construction is a significant investment, and designing a cost-effective network that meets minimum constraint requirements is a complex challenge. This study aims to find an optimal cost solution to reduce investment capital while ensuring efficient operation and high performance of the sewer network. By optimizing technical parameters that directly affect construction costs, the research introduces a novel hybrid algorithm based on the salp swarm algorithm (SSA) and the dragonfly algorithm (DA). This SSA-DA hybrid algorithm addresses the limitations of the individual SSA and DA by effectively balancing exploration and exploitation processes. The feasibility and effectiveness of the proposed algorithm were validated through two case studies from previous research on sewer networks. Quantitative analysis showed that the SSA-DA algorithm consistently outperformed other methods, including SSA and DA alone, achieving lower construction costs. Qualitative analysis confirmed the algorithm's reliability, as evidenced by consistent results across 10 independent runs. This research significantly contributes to the field by providing a robust and efficient optimization tool for cost-effective sewer network design.

关键词： salp swarm algorithm Dragonfly algorithm Optimization Domestic sewer network Hybrid method Metaheuristic

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salp swarm algorithm with crossover scheme and Levy flight for global optimization

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第5期40卷 9277-9288页

作者： Jia, Heming Lang, Chunbo Sanming Univ Coll Informat Engn Sanming Fujian Peoples R China Northwestern Polytech Univ Coll Automat Xian Shaanxi Peoples R China

salp swarm algorithm (SSA) is a meta-heuristic algorithm proposed in recent years, which shows certain advantages in solving some optimization tasks. However, with the increasing difficulty of solving the problem (e.g. multi-modal, high-dimensional), the convergence accuracy and stability of SSA algorithm decrease. In order to overcome the drawbacks, salp swarm algorithm with crossover scheme and Levy flight (SSACL) is proposed. The crossover scheme and Levy flight strategy are used to improve the movement patterns of salp leader and followers, respectively. Experiments have been conducted on various test functions, including unimodal, multimodal, and composite functions. The experimental results indicate that the proposed SSACL algorithm outperforms other advanced algorithms in terms of precision, stability, and efficiency. Furthermore, the Wilcoxon's rank sum test illustrates the advantages of proposed method in a statistical and meaningful way.

关键词： salp swarm algorithm crossover scheme Levy flight functions optimization

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salp swarm algorithm: a comprehensive survey

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NEURAL COMPUTING & APPLICATIONS 2020年第15期32卷 11195-11215页

作者： Abualigah, Laith Shehab, Mohammad Alshinwan, Mohammad Alabool, Hamzeh Amman Arab Univ Fac Comp Sci & Informat Amman Jordan Aqaba Univ Technol Dept Comp Sci Aqaba Jordan Saudi Elect Univ Coll Comp & Informat Abha Saudi Arabia

This paper completely introduces an exhaustive and a comprehensive review of the so-called salp swarm algorithm (SSA) and discussions its main characteristics. SSA is one of the efficient recent meta-heuristic optimization algorithms, where it has been successfully utilized in a wide range of optimization problems in different fields, such as machine learning, engineering design, wireless networking, image processing, and power energy. This review shows the available literature on SSA, including its variants, like binary, modifications and multi-objective. Followed by its applications, assessment and evaluation, and finally the conclusions, which focus on the current works on SSA, suggest possible future research directions.

关键词： salp swarm algorithm Meta-heuristic optimization algorithms Optimization problems Bio-inspired algorithms

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