Salp Swarm Algorithm Research Articles

One natural metaheuristic optimization technique is the Salp Swarm Algorithm (SSA), which takes its behavior from the swarming and feeding habits of salps in the ocean. With its straightforward structure, few parameters need, efficient exploration/exploitation balance, and adaptability to many optimization domains, SSA has garnered a lot of interest since its introduction. This paper provides a comprehensive review of SSA, highlighting its key advantages and applicability across a wide search space. The study includes its limitations, including sensitivity to problem types and reliance on the No Free Lunch theorem. This review analyzes different adaptations of SSA, such as binary versions, hybrid models, multi-objective extensions, and parameterless approaches, with the goal of enhancing performance and overcoming the limitations of the original algorithm. This paper analyzes the diverse applications of SSA in several domains, such as machine learning (including feature selection and neural network training), engineering optimization (covering scheduling, power systems, and renewable energy), image processing, localization, and additional practical areas. This study evaluates SSA through an analysis of its strengths, weaknesses, and potential areas for improvement. The study demonstrates that SSA is a promising and versatile optimization technique; however, it requires ongoing refinement to effectively address complex, dynamic, and multi-objective problems in future research.

ABSTRACT Accurate prediction of river water quality parameters is essential for environmental protection and sustainable agricultural resource management. This study presents a novel framework for estimating potential salinity in river water in arid and semi‐arid regions by integrating a kernel extreme learning machine (KELM) with a boosted salp swarm algorithm based on differential evolution (KELM‐BSSADE). A dataset of 336 samples, including bicarbonate, calcium, pH, total dissolved solids and sodium adsorption ratio, was collected from the Idenak station in Iran and was used for the modelling. Results demonstrated that KELM‐BSSADE outperformed models such as deep random vector functional link (dRVFL), general regression neural network (GRNN), multivariate adaptive regression spline (MARS), online sequential extreme learning machine (OSELM) and extreme gradient boosting decision tree (XGBoost) when compared with observed river salinity data. Also, the KELM‐BSSADE model effectively identified optimal inputs through the Boruta‐XGBoost (B‐XGB) feature selection method. Four metaheuristic‐based KELM models were developed, utilizing grey wolf optimizer, whale optimization, slime mould algorithm and equilibrium optimizer, further illustrating the capability of KELM‐BSSADE in estimating potential salinity in river water. By accurately estimating potential salinity, KELM‐BSSADE can assist in optimizing irrigation practices, ensuring that agricultural demands are met while minimizing the risk of salinity‐related crop damage.

Modern distribution systems must clearly distinguish between halal and non-halal items, particularly in areas with sizable Muslim populations and rising awareness of halal integrity. Consumer confidence may suffer, halal principles may be broken, and cross-contamination may result from failing to maintain this separation. This research uses the Green Vehicle Routing Problem (GVRP) approach, which is solved with the Salp Swarm Algorithm (SSA), to develop a joint distribution optimization model for halal and non-halal products in an effort to address these issues. With complete separation and adherence to halal logistics regulations, this methodology aims to reduce Total Distribution Cost (TDC), which comprises fuel expenses, carbon emissions, and operating costs. The SSA method is combined with Large Rank Value (LRV) to convert continuous solutions into practical and feasible route sequences. Simulation results using synthetic data from 20 customer locations show that increasing the population size and SSA iterations consistently reduces the TDC value until stable convergence is achieved. The model also proves to be robust to changes in fuel costs, emissions, and vehicles without altering the route structure. Overall, the results of the research show that the SSA-based GVRP model is capable of providing efficient and sustainable halal logistics solutions. The novelty of this research lies in the explicit integration of halal and non-halal segregation with the SSA-based GVRP optimization framework in a single sustainable distribution system.

With the increasing adoption of rooftop photovoltaic (PV) systems, accurate power output forecasting has become essential for effective energy management and grid integration. This study proposes a hybrid Artificial Neural Network (ANN) model optimized using the Salp Swarm Algorithm (SSA) to enhance prediction accuracy for rooftop PV output. SSA was selected for its strong exploration and exploitation capabilities, which complement the ANN’s learning strengths. Historical PV data from two university campuses in Malaysia, representing varied climatic conditions, were used over a one-year period to ensure model robustness. Key input variables influencing PV output were identified through correlation analysis, enabling more focused ANN training. SSA was used to optimize the ANN’s initial weights and biases, accelerating convergence and improving accuracy. Across three test cases, the SSA-ANN model achieved Mean Squared Error (MSE) values as low as 0.0155 and correlation coefficients (R) up to 0.98069, significantly outperforming standalone ANN approaches. These results demonstrate the model's effectiveness in improving PV forecasting accuracy, offering practical benefits for urban energy planning and sustainable power systems.

Accurate copper price forecasting is crucial and challenging due to the uncertainty and complex fluctuations caused by various factors of financial markets. In this area, the single-factor point prediction methods have made significant contributions but do not fully consider the influence of multiple factors and the robustness of the predictions. This study develops a novel hybrid interval prediction framework that combines multi-objective optimization with quantile deep learning for copper price prediction. The framework holistically evaluates the fluctuation range by assessing the distribution of copper prices and incorporates multiple variables chosen through diverse feature selection methods, which are crucial for accurate copper price prediction. The proposed framework encompasses two sub-stages: (1) initial interval prediction and quantile deep learning models of copper price; (2) multi-objective optimization procedure. In the first phase, four probabilistic forecasting algorithms are employed to sharpen prediction accuracy and provide a comprehensive picture of the interpretation of the outcome parameters by evaluating the distribution. The subsequent phase delves deeper to enhance prediction precision. Four multi-objective optimization algorithms are harnessed to refine the predictions, aiming to boost their reliability and resolution. The experiment findings underscore the superior predicted capabilities of the Quantile Regression Long Short-Term Memory (QRLSTM) model when optimized using the Multi-Objective Salp Swarm Algorithm (MOSSA), achieving a Prediction Interval Coverage Probability of 94.5205%, a Prediction Interval Normalized Average Width of 0.0066, and an Average Interval Score of -373.9687 at 95% confidence levels. The probabilistic forecasting framework developed in this research is reliable and comprehensive, considering many factors influencing the copper price.

Meeting the growing global electricity demand in remote and off-grid regions requires cost-effective and reliable power solutions that overcome the intermittency of renewable energy sources. This paper presents a comprehensive techno-economic optimization framework for the design and operation of off-grid hybrid renewable energy systems (HRES) integrating photovoltaic (PV), wind turbine, biomass generator, diesel backup, and a dual-chemistry hybrid battery energy storage system (HBESS) combining lithium-ion and nickel-iron batteries. A detailed mathematical modeling approach is employed to capture the nonlinear dynamics, stochastic renewable behavior, battery degradation, and temperature-adjusted component efficiencies. The system is formulated as a multi-objective mixed-integer nonlinear programming problem targeting the minimization of life cycle cost (LCC), levelized cost of energy (LCOE), and CO 2 emissions while satisfying reliability constraints such as loss of power supply probability (LPSP < 0.01). To solve the optimization problem, advanced metaheuristic algorithms—Particle Swarm Optimization (PSO), Genetic Algorithms (GA), Grey Wolf Optimizer (GWO), and Differential Evolution (DE), and Salp Swarm Algorithm (SSA)—and a Deep Q-Network (DQN)-based reinforcement learning energy management strategy are implemented and benchmarked. The proposed DQN-based controller demonstrates superior performance over conventional rule-based and static dispatch methods by maintaining more stable battery state-of-charge (SOC) profiles, reducing degradation, and enabling intelligent real-time decision-making. Simulation results based on realistic meteorological and demand profiles reveal that the integrated DQN and HBESS strategy reduces total LCC by over 20%, CO 2 emissions by up to 30%, and battery degradation costs by over 10% compared to baseline systems. The Salp Swarm Algorithm (SSA) achieves the fastest convergence and the highest-quality Pareto-optimal solutions among all metaheuristics evaluated. Sensitivity analysis identifies diesel price and interest rate as the most influential parameters on LCOE, while load shifting through aggressive demand-side management further minimizes battery usage, operating costs, and emissions. The proposed framework not only addresses key challenges in off-grid microgrid design but also provides a scalable and robust pathway for sustainable rural electrification using hybrid storage and intelligent control.

Airborne Wind Energy (AWE) systems offer benefits such as high altitude access to stronger and more stable winds, reduced environmental impact, and cost effective infrastructure. However, these systems face several challenges including complex flight trajectory optimization, limited control robustness, and unstable power generation. This paper focuses on optimizing the flight trajectory of a tethered rigid wing AWE system to maximize power generation. A mathematical model of the system is constructed, and a constrained trajectory optimization problem is formulated. The multiple shooting method is employed for discretization, and a Multi-Strategy Improved Salp Swarm Algorithm (MISSA) is proposed to solve the optimization problem. Simulation results indicate that MISSA can generate a closed optimal trajectory, significantly enhance power output, and demonstrate superior performance in addressing complex trajectory optimization challenges.

Robust optimization strategies (effective and feasible viewpoints) are critical in solving complex, high-dimensional problems in science, where traditional algorithms frequently converge prematurely to suboptimal solutions, and sometimes so far to the global optimum. This paper proposes an Ensemble Heuristic Algorithm to improve the performance of Differential Evolution, specifically tested on the Generalized Numerical Benchmark Generator (GNBG) problem test bed. In this research, the effectiveness of the Differential Evolution approach is examined in several stages of the heuristic optimization process. That is to say, it is an ensemble of the Salp Swarm Algorithm (SSA), Multi-Verse Optimizer (MVO), and a hybrid Differential Evolution, into a ensemble framework in a unified way. The proposed algorithm is tested on 24 problem instances from the GNBG set. This method is superior to standard DE, scoring 12 out of 24 on the success metric, compared to 16.19 out of 24 on the success rate metric. The proposed method shows better convergence and solution quality (non-suboptimal solutions) across the whole benchmark set. An initial exploration phase that uses several metaheuristics, like SSA and MVO, is one of the most important parts of this method. These algorithms generate a diverse quantity of candidate solutions. Following this, a stage called “solution combination stage” uses a hybrid Differential Evolution algorithm and improves the overall search capability (exploration). Finally, we continue with a phase called “intensive local search phase”, this part has adaptive step sizes, which it is tuned in order to get the best solutions and improve convergence to optimal results. Consequently, it is emphasized that the algorithms' limitations in the benchmarking problems, and we proposed a flexible scheme in order to reach the optimal point in the complex optimization challenges.

Feature selection (FS) is critical for datasets with multiple variables and features, as it helps eliminate irrelevant elements, thereby improving classification accuracy. Numerous classification strategies are effective in selecting key features from datasets with a high number of variables. In this study, experiments were conducted using three well-known datasets: the Wisconsin Breast Cancer Diagnostic dataset, the Sonar dataset, and the Differentiated Thyroid Cancer dataset. FS is particularly relevant for four key reasons: reducing model complexity by minimizing the number of parameters, decreasing training time, enhancing the generalization capabilities of models, and avoiding the curse of dimensionality. We evaluated the performance of several classification algorithms, including K-Nearest Neighbors (KNN), Random Forest (RF), Multi-Layer Perceptron (MLP), Logistic Regression (LR), and Support Vector Machines (SVM). The most effective classifier was determined based on the highest level of accuracy. Additionally, this research introduces hybrid algorithms such as TMGWO (Two-phase Mutation Grey Wolf Optimization), ISSA (Improved Salp Swarm Algorithm), and BBPSO (Binary Black Particle Swarm Optimization) for identifying significant features for classification. A comparative analysis was conducted to assess the performance of these hybrid FS algorithms from various perspectives. We also compared the performance of classifiers on datasets with and without FS, measuring improvements in accuracy, precision, and recall. Among the algorithms tested, the TMGWO hybrid approach achieved superior results, outperforming the other experimental methods in both feature selection and classification accuracy.

Real-world optimization problems, such as global optimization, cleaner production system, and complex design challenges are inherently complex, involving many variables and constraints. These factors make it challenging for optimizers to determine optimal solutions efficiently. Salp Swarm Algorithm (SSA) adapts easily to complex optimization problems due to its simplicity, multi-search strategy, and few control parameters. However, its search strategy lacks precision in guiding the population toward optimal regions of the solution space, which limits its effectiveness in optimizing cleaner production systems and complex design problems. This study proposes an evolutionary SSA (ESSA) to address complex optimization problems. ESSA proposes distinct innovative search strategies, including two evolutionary search strategies that enhance diversity and adaptive search, as well as an enhanced SSA search strategy that, while less exploratory, ensures steady convergence. ESSA introduces an advanced memory mechanism that stores the best and inferior solutions identified during optimization, enhancing diversity and preventing premature convergence. Moreover, it incorporates a stochastic universal selection method to regulate the archive by selecting individuals according to their fitness values. The performance of ESSA was evaluated using benchmark functions CEC 2017 and CEC 2020, compared to seven leading algorithms. Results show that ESSA outperforms SSA and others in solution quality and convergence speed. Statistical analyses confirm that ESSA ranks first and achieves the best optimization effectiveness, with values of 84.48%, 96.55%, and 89.66% for dimensions 30, 50, and 100, respectively, surpassing other optimizers. Additionally, ESSA's practical applicability is demonstrated through its success in optimizing a cleaner production system and solving complex design problems, highlighting its effectiveness in tackling challenging optimization tasks.

Sentiment recognition is a complex task in natural language processing (NLP), and needs training models to handle bulk volumes of data such as IMDb dataset (English movie reviews) with hesitating linguistic trends, posing some considerable computational difficulties.The proposed framework incorporates Bidirectional Encoder Representations Transformers (BERT) and Salp Swarm Algorithm (SSA) to optimize hyperparameters in sentiment analysis using IMDb dataset.Salp swarm intelligence of (SSA) is conducted to optimize the learning rate, batch size, dropout rate, and number of attention heads.Comparative analysis has been conducted against 4 state-of-the-art algorithms (Grid Search, Particle Swarm Optimization, Improved SSA, WOA-AdaBoost) indicating the effectiveness of the proposed SSA-BERT model.The model shows an accuracy of )99.5% (on IMDb dataset performing better than Grid Search (95.5%), Particle Swarm Optimization (PSO) (96%), and Improved SSA (ISSA) (98.5%) as well as the WOA-AdaBoost (99%). Statistical analysis has been conducted using a T-test to prove the model’s superiority.The proposed model achieved a )99.0% (accuracy on the fifth epoch, and an overall accuracy of (99.5%).

ABSTRACT This research introduces an innovative wind farm layout optimization approach using the Salp Swarm Algorithm (SSA) to minimize wake interference between neighboring wind facilities. Wake effects, caused by turbine-induced wind flow disturbance, significantly impact downstream turbine performance and are a critical factor in optimizing wind farm layouts. The study examines the optimal positioning of turbines for a new 20 MW wind farm adjacent to an existing 30 MW facility, with the primary goal of maximizing the total Annual Energy Production (AEP) of the combined facilities. The optimization framework solves a single-objective problem that aims to maximize net Annual Energy Production (AEP), with wake losses incorporated through the Jensen model and subject to multiple physical and operational constraints, analyzing three distinct turbine capacities (1500 kW, 2000 kW, and 2500 kW). The SSA-optimized configuration demonstrated superior performance, achieving a 0.33% increase in AEP compared to conventional windPRO layouts for 2000 kW turbines. Notably, the 1500 kW turbine arrangement proved the most efficient, with the SSA-optimized design reaching 33.633% efficiency for the new facility and 33.818% for the combined wind farm complex. Validation through WAsP software confirmed the reliability of the SSA approach, showing less than 1% deviation from windPRO calculations. While the results demonstrate SSA’s effectiveness for wind farm layout optimization, future research should focus on enhancing computational efficiency and incorporating additional site-specific constraints for broader applications in onshore wind farm development.

The basic Salp Swarm Algorithm (SSA) offers advantages such as a simple structure and few parameters. However, it is prone to falling into local optima and remains inadequate for seed classification tasks that involve hyperparameter optimization of machine learning classifiers such as Support Vector Machines (SVMs). To overcome these limitations, an Enhanced Knowledge-based Salp Swarm Algorithm (EKSSA) is proposed. The EKSSA incorporates three key strategies: Adaptive adjustment mechanisms for parameters and to better balance exploration and exploitation within the salp population; a Gaussian walk-based position update strategy after the initial update phase, enhancing the global search ability of individuals; and a dynamic mirror learning strategy that expands the search domain through solution mirroring, thereby strengthening local search capability. The proposed algorithm was evaluated on thirty-two CEC benchmark functions, where it demonstrated superior performance compared to eight state-of-the-art algorithms, including Randomized Particle Swarm Optimizer (RPSO), Grey Wolf Optimizer (GWO), Archimedes Optimization Algorithm (AOA), Hybrid Particle Swarm Butterfly Algorithm (HPSBA), Aquila Optimizer (AO), Honey Badger Algorithm (HBA), Salp Swarm Algorithm (SSA), and Sine–Cosine Quantum Salp Swarm Algorithm (SCQSSA). Furthermore, an EKSSA-SVM hybrid classifier was developed for seed classification, achieving higher classification accuracy.

ABSTRACT This research introduces an adaptive energy control strategy for households powered by Sustainable Energy Sources (SES), leveraging Intelligent Grid (IG) technologies. The novelty of this work lies in employing the Intelligent Salp Swarm Algorithm (ISSA), which dynamically adjusts control parameters for real-time optimization, outperforming conventional methods. ISSA efficiently manages appliances, energy sources, and demand prioritization, ensuring optimal energy utilization while addressing SES variability. The proposed model incorporates practical constraints such as customer preferences, energy supply limitations, and ecological unpredictability, ensuring real-world applicability. Three scenarios are analyzed: traditional residences without energy management systems, smart homes with basic SES, and intelligent homes integrating advanced SES. Comparative analysis confirms that ISSA surpasses Genetic Algorithms (GAM) and Particle Swarm Stabilization (PSS) in optimizing energy use. The results highlight ISSA’s ability to enhance system efficiency, reduce costs, and support sustainability goals. This scalable approach effectively addresses modern energy management challenges, contributing to secure, sustainable, and affordable household power systems.

Optimizing biodiesel production from underutilized Garcinia indica oil through empirical, Coati Optimization and Salp Swarm Algorithm

Multiple subpopulation Salp swarm algorithm with Symbiosis theory and Gaussian distribution for optimizing warm-up strategy of fuel cell power system

Problem: Patients with pancreatic ductal adenocarcinoma (PDAC) can have a much better prognosis if they receive an early and precise diagnosis. Numerous studies have created automated techniques that use a variety of medical imaging to forecast the development of PDAC. In this work, we suggest a methodical approach for tracking, categorizing, and identifying pancreatic cancers. The model combines fish optimization methods with Deep Echo State Network (DESN) model technology. Aim: This concept aims to successfully integrate the two technologies. Here, we use a new dataset of diagnostic cases and the Medical Segmentation Decathlon (MSD) dataset to test the effectiveness and dependability of the diagnostic process using deep learning methods. Method: The proposed work employed image processing techniques like Gaussian and median filters, as well as SSA-MLT (Multilevel Thresholding) image segmentation based on Salp Swarm Algorithm (SAL) to enhance CT images of pancreatic cancers. In this paper, we provide a model for PDAC images called VGG16 (VGG16-Backbone Feature Extractor). DESN-CFOA is used in classification processing to extract features. The segmentation and classification performance of pancreatic tumor images were used to assess model training and configuration goals. Result: A thorough comparative data analysis reveals that the suggested approach performs better than existing technology, as demonstrated by extensive simulations. The proposed work is anticipated to produce a better performance in terms of Dice coefficient, Jaccard index, precision, and recall.

Rainfall prediction is considered to be an esteemed research area that impacts the day-to-day life of Indians. The predominant income source of most of the Indian population is agriculture. It helps the farmers to make the appropriate decisions pertaining to cultivation and irrigation. The primary objective of this investigation is to develop a technique for rainfall prediction utilising the MapReduce framework and the convolutional long short-term memory (ConvLSTM) method to circumvent the limitations of higher computational requirements and the inability to process a large number of data points. In this work, an adaptive salp-stochastic-gradientdescent-based ConvLSTM (adaptive S-SGD-based ConvLSTM) system has been developed to predict rainfall accurately to process the long time series data and to eliminate the vanishing problems. To optimize the hyperparameter of the convLSTM model, the S-SGD methodology proposed combine the SGD and the salp swarm algorithm (SSA). The adaptive S-SGD based ConvLSTM has been developed by integrating the adaptive concept in S-SGD. It tunes the weights of ConvLSTM optimally to achieve better prediction accuracy. Assessment measures, such as the percentage root mean square difference (PRD) and mean square error (MSE), were employed to compare the suggested method with previous approaches. The developed system demonstrates high prediction accuracy, achieving minimal values for MSE (0.0042) and PRD (0.8450).

This paper presents a hybrid version of the Salp Swarm Algorithm (SSA) for Economic Load Dispatch (ELD) problems with severe constraints. The Adaptive beta-hill climbing optimizer (AbetaHCO) ais hybridized with a newly developed local search method with SSA as a new operator. This hybridization scheme is known as a memetic algorithm, where SSA serves as a natural selection agent (general refinement) in a genotype environment, while AbetaHCO serves as a culture selection agent (local refinement) in a phenotype environment. In other words, SSA acts as a gene encoding in biology, while AbetaHCO serves as a meme in a cultural context. In an intelligent optimization environment, gene and meme notations from natural biology and cultural selection act as search agents to achieve generality (gene) and problem specificity (meme). ELD is a crucial optimization problem in electrical engineering, and it is non-convex, multi-modal, and severely constrained. The proposed method, called MSSA, evaluates several types of ELD problems that differ in the constraints adopted. The first problem is addressed by considering two types of constraints related to load balance and output. It includes five practical cases of ELD generators that vary in number of units and load requirements: a three-unit generator with a capacity of 850 MW (3UG-850 MW), a thirteen-unit generator with a capacity of 1800 MW (13UG-1800 MW), a thirteen-unit generator with a capacity of 2520 MW (13UG-2520 MW), a forty-unit generator with a capacity of 10500 MW (40UG-10500 MW), and a large-scale generator with a capacity of 80 units of 21000 MW (80UG-21000 MW). Two additional constraints-restricted operating zones and ramp rate limits-are used to address the second problem. A six-unit generator with a capacity of 1,263 MW (6 UG-1,263 MW) and a fifteen-unit generator with a capacity of 2,630 MW (40 UG-2,630 MW) are two real-world cases discussed. Compared with other existing algorithms, the comparative results demonstrate the feasibility and usefulness of the proposed MSSA algorithm.

A feature selection method based on salp swarm algorithm with a multi-round voting mechanism