Salp Swarm Algorithm Algorithm Research Articles

Optimized Fixed-Time Synergetic Controller via a modified Salp Swarm Algorithm for Acute and Chronic HBV Transmission System

In this paper, we propose a Salp Swarm Algorithm (SSA) Optimized Fixed-Time Synergetic Control (FTSC) strategy for the spread of hepatitis B infection. The utilization of the SSA optimization algorithm for optimizing the Synergetic Control (SC) fraction parameters presents a non-trivial challenge due to the restriction that only odd numbers can be used for the fractional power. Therefore, an enhanced and adapted version of the SSA algorithm is proposed to effectively address this specific scenario. Our strategic approach centers on the reduction of susceptible, acutely infected, and chronically infected individuals by employing control parameters like isolation, treatment, and vaccination. The objective is to drive these target state variables to their smallest values in a fixed-time, thereby effectively controlling the epidemic. We support our proposal with numerical simulations to demonstrate the feasibility and effectiveness of the control strategy. A comparison is conducted between FTSC and SC in scenarios with and without optimization. The results indicated that FTSC holds a distinct advantage, consistently demonstrating significant progress, with up to 30\% reduction in the total convergence time to zero, outperforming SC in each case.

Advanced Intelligent Approach for Solar PV Power Forecasting Using Meteorological Parameters for Qassim Region, Saudi Arabia

Solar photovoltaic (SPV) power penetration in dispersed generation systems is constantly rising. Due to the elevated SPV penetration causing a lot of problems to power system stability, sustainability, reliable electricity production, and power quality, it is critical to forecast SPV power using climatic parameters. The suggested model is built with meteorological conditions as input parameters, and the effects of such variables on predicted SPV power have been studied. The primary goal of this study is to examine the effectiveness of optimization-based SPV power forecasting models based on meteorological conditions using the novel salp swarm algorithm due to its excellent ability for exploration and exploitation. To forecast SPV power, a recently designed approach that is based on the salp swarm algorithm (SSA) is used. The performance of the suggested optimization model is estimated in terms of statistical parameters which include Root Mean Square Error (RMSE), Mean Square Error (MSE), and Training Time (TT). To test the reliability and validity, the proposed algorithm is compared to grey wolf optimization (GWO) and the Levenberg–Marquardt-based artificial neural network algorithm. The values of RMSE and MSE obtained using the proposed SSA algorithm come out as 1.45% and 2.12% which are lesser when compared with other algorithms. Likewise, the TT for SSA is 12.46 s which is less than that of GWO by 8.15 s. The proposed model outperforms other intelligent techniques in terms of performance and robustness. The suggested method is applicable for load management operations in a microgrid environment. Moreover, the proposed study may serve as a road map for the Saudi government’s Vision 2030.

Frequency Control Strategy for Grid-tied Virtual Power Plant Using SSA-tuned Fractional Order PID Controller

This paper investigates the frequency control strategy of the Virtual Power Plant (VPP), with the structure of an independent control area that includes diverse Distributed Energy Resources. The proposed VPP system comprises generating units such as Solar PV, Archimedes Wave Energy Conversion, Biodiesel driven Generator, and Plug-in Hybrid Electric Vehicle, which are interconnected to conventional thermal power plant. Centralized control strategy is adopted for frequency control studies, by considering the communication time delays. Fractional order Proportional Integral Derivative (FOPID), Proportional Integral Derivative (PID), and Proportional Integral (PI) controllers are employed to perform the control action on generating units to perform the frequency control. Tuning of the controllers is done using optimization algorithms such as Particle Swarm Optimization, Firefly Algorithm, Grasshopper Optimization Algorithm, Ant Lion Optimizer, Sine Cosine Algorithm, and Salp Swarm Algorithm (SSA). By comparative analysis, SSA algorithm is proved to outperform the other algorithms in terms of minimizing the objective function of Integral of Squared Error of the deviations in system frequencies and tie-line power. The system is also investigated under various scenarios of generation and load disturbances to study the comparative performance of the FOPID controller over PID and PI controllers. Results prove the superiority of SSA-tuned FOPID controller over PID and PI controllers in terms of providing better system responses. Sensitivity test has been conducted on the system without resetting nominal controller gain values and the proposed SSA-tuned FOPID controller is proved to be robust against the uncertainties in the load and parameters of our system.

Large-Scale Competitive Learning-Based Salp Swarm for Global Optimization and Solving Constrained Mechanical and Engineering Design Problems

The Competitive Swarm Optimizer (CSO) has emerged as a prominent technique for solving intricate optimization problems by updating only half of the population in each iteration. Despite its effectiveness, the CSO algorithm often exhibits a slow convergence rate and a tendency to become trapped in local optimal solutions, as is common among metaheuristic algorithms. To address these challenges, this paper proposes a hybrid approach combining the CSO with the Salp Swarm algorithm (SSA), CL-SSA, to increase the convergence rate and enhance search space exploration. The proposed approach involves a two-step process. In the first step, a pairwise competition mechanism is introduced to segregate the solutions into winners and losers. The winning population is updated through strong exploitation using the SSA algorithm. In the second step, non-winning solutions learn from the winners, achieving a balance between exploration and exploitation. The performance of the CL-SSA is evaluated on various benchmark functions, including the CEC2017 benchmark with dimensions 50 and 100, the CEC2008lsgo benchmark with dimensions 200, 500 and 1000, as well as a set of seven well-known constrained design challenges in various engineering domains defined in the CEC2020 conference. The CL-SSA is compared to other metaheuristics and advanced algorithms, and its results are analyzed through statistical tests such as the Friedman and Wilcoxon rank-sum tests. The statistical analysis demonstrates that the CL-SSA algorithm exhibits improved exploitation, exploration, and convergence patterns compared to other algorithms, including SSA and CSO, as well as popular algorithms. Furthermore, the proposed hybrid approach performs better in solving most test functions.

Discrete Salp Swarm Algorithm for symmetric traveling salesman problem.

In the Salp Swarm Algorithm (SSA), the update mechanism is inspired by the unique chain movement of the salp swarm. Numerous versions of SSA were already put forward to deal with various optimization problems, but there are very few discrete versions among them. d-opt is improved based on the 2-opt algorithm: a decreasing factor d is introduced to control the range of neighborhood search; TPALS are modified by Problem Aware Local Search (PALS) based on the characteristics of Travelling Salesman Problem (TSP); The second leader mechanism increases the randomness of the algorithm and avoids falling into the local optimal solution to a certain extent. We also select six classical crossover operators to experiment and select Subtour Exchange Crossover (SEC) and the above three mechanisms to integrate them into the SSA algorithm framework to form Discrete Salp Swarm Algorithm (DSSA). In addition, DSSA was tested on 23 known TSP instances to verify its performance. Comparative simulation studies with other advanced algorithms are conducted and from the results, it is observed that DSSA satisfactorily solves TSP.

Addressing constrained engineering problems and feature selection with a time-based leadership salp-based algorithm with competitive learning

Abstract Like most metaheuristic algorithms, salp swarm algorithm (SSA) suffers from slow convergence and stagnation in the local optima. The study develops a novel Time-Based Leadership Salp-Based Competitive Learning (TBLSBCL) to address the SSA’s flaws. The TBLSBCL presents a novel search technique to address population diversity, an imbalance between exploitation and exploration, and the SSA algorithm’s premature convergence. Hybridization consists of two stages: First, a time-varying dynamic structure represents the SSA hierarchy of leaders and followers. This approach increases the number of leaders while decreasing the number of salp’s followers linearly. Utilizing the effective exploitation of the SSA, the position of the population’s leader is updated. Second, the competitive learning strategy is used to update the status of the followers by teaching them from the leaders. The goal of adjusting the salp swarm optimizer algorithm is to help the basic approach avoid premature convergence and quickly steer the search to the most promising likely search space. The proposed TBLSBCL method is tested using the CEC 2017 benchmark, feature selection problems for 19 datasets (including three high-dimensional datasets). The TBLSBCL was then evaluated using a benchmark set of seven well-known constrained design challenges in diverse engineering fields defined in the benchmark set of real-world problems presented at the CEC 2020 conference (CEC 2020). In each experiment, TBLSBCL is compared with seven other state-of-the-art metaheuristics and other advanced algorithms that include seven variants of the salp swarm. Friedman and Wilcoxon rank-sum statistical tests are also used to examine the results. According to the experimental data and statistical tests, the TBLSBCL algorithm is very competitive and often superior to the algorithms employed in the studies. The implementation code of the proposed algorithm is available at: https://github.com/MohammedQaraad/TBLSBCL-Optimizer.

Comparing SSALEO as a Scalable Large Scale Global Optimization Algorithm to High-Performance Algorithms for Real-World Constrained Optimization Benchmark

The Salp Swarm Algorithm (SSA) outperforms well-known algorithms such as particle swarm optimizers and grey wolf optimizers in complex optimization challenges. However, like most meta-heuristic algorithms, SSA suffers from slow convergence and stagnation in the best local solution. In this study, a Salp swarm algorithm (SSA) is combined with a local escaping operator (LEO) to overcome some inherent limitations of the original SSA. SSALEO is a novel search technique that accounts for population diversity, the imbalance between exploitation and exploration, and the SSA algorithm’s premature convergence. By implementing LEO in SSALEO, the search slowdown in SSA is eliminated, and the local search efficiency of swarm agents is improved. The proposed SSALEO method is tested using the CEC 2017 benchmark with 50 and 100 decision variables, seven CEC2008lsgo test functions with 200, 500, and 1000 decision variables, and its performance was compared to other metaheuristic algorithms (MAs) and advanced algorithms, including seven Salp swarm variants. The comparisons show that SSA greatly benefits from LEO by enhancing the quality and accelerating its solutions’ convergence rate. The SSALEO was then assessed using a benchmark set of seven well-known constrained design challenges in various engineering domains defined in the CEC 2020 conference benchmark. Friedman and Wilcoxon rank-sum statistical tests are also used to examine the results. ACCORDING TO EXPERIMENTAL DATA AND STATISTICAL TESTS, the SSALEO algorithm is very competitive and often superior to the algorithms used in the studies. Further, the proposed approach can be viewed as a special LSGO optimizer whose performance exceeds that of specialized state-of-the-art algorithms like CMA-ES and SHADE.

A Salp Swarm Optimization for Dynamic Resource Management to Improve Quality of Service in Cloud Computing and IoT Environment

Background: Cloud Computing can process and utilize efficient resources within a metered premise. This feature is a significant research problem, giving great Quality-of-Services (QoS) to the clients in cloud. Objective: The objective of this study is to confirm QoS with minimum resource utilization time and costs, cloud service providers ought to receive self-versatile resource provisioning at each level. Various guidelines and model-based methodologies have been proposed for the management of resources in cloud services. Method: In this research article, resource allocation is done using the Salp Swarm Algorithm (SSA), which combines various VMs on a constrained Data Center with SLA and QoS factors. Result: Different existing optimization algorithms are available such as First Fit, Greedy Crow Search (GCS) and Hybrid Crow Search algorithm (TSPCS). The combination of the Travelling Salesman Problem (TSP) and Crow Search Algorithm (CSA) is more efficient than the Fist Fit, GCS, and TSPCS in terms of the parameters such as resource utilization and response time. It is clearly shown that a user’s request takes minimum time and maximum QoS when employing the SSA algorithm in cloud computing. Conclusion: The proposed mechanism is simulated on Cloudsim Simulator. The simulation results show less migration time that improve the QoS and minimizes the energy consumption in a cloud and IoT environment.

Automated technique for carotid plaque characterisation and classification using RDWT in ultrasound images

ABSTRACT In this manuscript, a novel method of rational-dilation wavelet transform (RDWT) is proposed for carotid plaque characterisation and classification in Ultrasound images. RDWT is mainly utilised for image acquisition, pre-processing, feature extraction and ensemble classification in automated plaque classification. Here, the transition bands are constructed from the transition function. The statistical features, viz mean, standard deviation, skewness, Renyi entropy, energy are extracted from the sub-bands of RDWT. The Salp Swarm Algorithm (SSA) is mainly used for selecting the optimum features. In this for selecting optimum features using SSA algorithm two conditions are satisfied such as, in the first approach, mean, standard deviation, skewness are selected and then utilised for converting the continual version of salp swarm algorithm to binary. Subsequently, the crossover operator is utilised to select the Renyi entropy, energy features including transfer functions for replacing the average operator and enhancing the characteristics of research method. Plaque Classification uses K Nearest Neighbour (KNN), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM) classifiers. Experimental outcomes show the efficiency of the proposed method depending on accuracy, specificity and sensitivity. The proposed method attains accuracy of 93%, sensitivity of 90% and specificity of 94% when likened to the existing techniques, such as KNN, PNN and SVM.

Pilot allocation optimization using enhanced salp swarm algorithm for sparse channel estimation

Pilot pattern has a significant effect on the performance of channel estimation based on compressed sensing. However, because of the influence of the number of subcarriers and pilots, the complexity of the enumeration method is computationally impractical. The meta-heuristic algorithm of the salp swarm algorithm (SSA) is employed to address this issue. Like most meta-heuristic algorithms, the SSA algorithm is prone to problems such as local optimal values and slow convergence. In 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 search. Experiments show that the test results of the CWSSA on most benchmark functions are better than those of other meta-heuristic algorithms. Besides, the CWSSA algorithm is applied to pilot pattern optimization, and its results are better than other methods in terms of BER and MSE.

PI Parameters Tuning for Sub-Synchronous Interactions in DFIG Based on GSSA Algorithm

The interaction between the control of power electronic devices and the series compensating capacitors is called sub-synchronous control interaction (SSCI). Because SSCI seriously affects the stability of the power grid, it has attracted much attention. In this paper, the initial population of salp swarm algorithm (SSA) is optimized based on good point theory and an improved SSA algorithm (GSSA) is proposed. Then, based on the improved GSSA algorithm, the PI parameters are adjusted to minimize the oscillation. Finally, the validity of the method is verified by simulation analysis. In addition, the robustness of the optimized parameters under different wind speeds and series compensation levels is studied. The results show that the optimized parameters can not only effectively suppress SSCI, but also have strong robustness in different working conditions.

Modified salp swarm algorithm for global optimisation

Salp swarm algorithm (SSA) is a newly swarm-based metaheuristic algorithm that simulate the swimming and foraging behaviour of salps in oceans so to search for global optimum solution. Similarly to other metaheuristic algorithms, SSA suffers from poor convergence rate and stagnation in local optima. In this paper, three different improvements to the original population update process are proposed in order to enhance its exploitation and exploration capabilities. The first modification (MSSA1) introduces the concept of local best information to the followers salps update process allowing a better exploration of local search neighbourhood. The second improvement (MSSA2) provide two followers update process. The first is based on a differential evolution combined with a randomly selected local best position, and the second uses a local search in the global best neighbourhood which is triggered by a non-improvement in the corresponding local best. A third modification to the SSA algorithm (MSSA3) penalises a non-improvement of the local best solution by computing a new corresponding follower’s position based on a local jump in the local best neighbourhood for better exploitation. The performances of the proposed algorithms are tested on 27 CEC’15 test suite, and two real-world optimisation problems. A comparative study using nonparametric statistical tests of the obtained results is conducted against those of eight well-known metaheuristics, including the original SSA. The results indicate an overall distinctive performance of all three modification compared to the remaining algorithms, while MSSA1 scored generally better than MSSA2 and MSSA3.

A multiprocess Salp swarm optimization with a heuristic based on crossing partial solutions

Abstract The Salp swarm algorithm (SSA) is one of the most recent metaheuristic optimization algorithms. SSA has been used successfully to solve optimization problems in different research areas such as machine learning, engineering design, wireless networks, image processing, mobile robotics, and energy. In this article, we present a multi-threaded implementation of the SSA algorithm. Each thread executes an SSA algorithm that shares information among the swarms to get a better solution. The best partial solutions of each swarm intersect in a similar way of genetic algorithms. The experiments with nineteen benchmark functions (unimodal, multimodal, and composite) show the results obtained with this new algorithm are better than those achieved with the original algorithm.

An Effective Salp Swarm Based MPPT for Photovoltaic Systems Under Dynamic and Partial Shading Conditions

This study proposes a duty cycle-based direct search method that capitalizes on a bioinspired optimization algorithm known as the salp swarm algorithm (SSA). The goal is to improve the tracking capability of the maximum power point (MPP) controller for optimum power extraction from a photovoltaic system under dynamic environmental conditions. The performance of the proposed SSA is tested under a transition between uniform irradiances and a transition between partial shading (PS) conditions with a focus on convergence speed, fast and accurate tracking, reduce high initial exploration oscillation, and low steady-state oscillation at MPP. Simulation results demonstrate the superiority of the proposed SSA algorithm in terms of tracking performance. The performance of the SSA method is better than the conventional (hill-climbing) and among other popular metaheuristic methods. Further validation of the SSA performance is conducted via experimental studies involving a DC-DC buck-boost converter driven by TMS320F28335 DSP on the Texas Instruments Experimenter Kit platform. Hardware results show that the proposed SSA method aligns with the simulation in terms of fast-tracking, convergence speed, and satisfactory accuracy under PS and dynamic conditions. The proposed SSA method tracks maximum power with high efficiency through its superficial structures and concepts, as well as its easy implementation. Moreover, the SSA maintains a steady-state oscillation at a minimum level to improve the overall energy yield. It thus compensates for the shortcomings of other existing methods.

Salp swarm algorithm with crossover scheme and Lévy flight for global optimization

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 Lévy flight (SSACL) is proposed. The crossover scheme and Lévy 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.

Circular Antenna Array Synthesis Using Salp Swarm Optimization

Salp Swarm Algorithm (SSA) is used to design an optimal non uniform circular antenna arrays. Salp Swarm Algorithm which mimics the swarming behavior of salps in oceans is a nature-inspired optimization method. As it is simple and easy to apply, it has been applied to many different problems in the literature. SSA method optimally determined the positions and amplitudes of the circular antenna array elements to obtain radiation patterns with a low maximum sidelobe level (MSL) and narrow half power beam width (HPBW). Different sizes of antennas with 8, 10 and 12 elements are discussed to demonstrate the capability of the SSA algorithm. The results of MSL and HPBW obtained by SSA for the synthesis of circular antenna arrays are better than other compared optimization methods.

A novel salp swarm assisted hybrid maximum power point tracking algorithm for the solar photovoltaic power generation systems

The photovoltaic (PV) systems must work at the maximum power point (MPP) to derive the highest possible power with the higher performance during a change in operating conditions. The primary objective is to implement a novel hybrid tracking algorithm to extract the maximum output power from the solar PV panel or array under partial shading conditions (PSCs). This hybrid MPP tracking algorithm is based on the salp swarm algorithm (SSA), which finds the initial global peak (GP) operating point and is followed by the perturb and observation (P&O) algorithm in the last stage to realize a faster convergence rate. Thus, the computational burden met by the conventional methods such as standalone P&O, hybrid grey-wolf-optimization (HGWO), and hybrid whale-optimization algorithm (HWOA) algorithm reported in the literature is overcome by the proposed hybrid SSA algorithm called HSSA. The P&O algorithm searches the MPP in the projected search space by the SSA algorithm. The proposed hybrid algorithm is simulated using MATLAB/Simulink simulation tool to validate the effectiveness of tracking the MPP. The hybrid SSA is compared with the standalone P&O, hybrid WOA, and hybrid GWO, and from the simulation results, it is proved that the hybrid tracking algorithm exhibits a high tracking performance.

Skeletal scintigraphy image enhancement based neutrosophic sets and salp swarm algorithm.

Recently, several schemes are proposed for enhancing the dark regions of the skeletal scintigraphy image. Nevertheless, most of them are flawed by some performance problems. This paper presents an adaptive scheme based on Salp Swarm algorithm (SSA) and a neutrosophic set (NS) under multi-criteria to enhance the dark regions of the skeletal scintigraphy image efficiently. Enhancing the dark regions is first converted into an optimization problem. The SSA algorithm is used to find the best improvement for each image separately, and then the neutrosophic algorithm is used to find similarity score to each image with adaptive weight coefficients obtained by the SSA algorithm. The proposed algorithm is applied to an Egyptian medical dataset collected from Menoufia University Hospital and it is a no-reference image. The experiments are done using 3 different resolutions 512*512, 256*256, and 128*128 and compared with Gamma Correction, the NS algorithm and the local enhance algorithm. The results demonstrate that the proposed algorithm achieves superior performance in almost criteria fitness function, entropy, eumber of edges, nNaturalness image quality Evaluator, sharpness, sharpness index, and contrast-distorted images using contrast enhancement. The results showed the idea of integration between the falsity membership of the neutrosophic set and the Salp swarm algorithm can be used to Skeletal Scintigraphy enhancement. This paper proved that it can depend on falsity membership of the neutrosophic set in the Image Enhancement field.

Dynamic Salp swarm algorithm for feature selection

Recently, many optimization algorithms have been applied for Feature selection (FS) problems and show a clear outperformance in comparison with traditional FS methods. Therefore, this has motivated our study to apply the new Salp swarm algorithm (SSA) on the FS problem. However, SSA, like other optimizations algorithms, suffer from the problem of population diversity and fall into local optima. To solve these problems, this study presents an enhanced version of SSA which is known as the Dynamic Salp swarm algorithm (DSSA). Two main improvements were included in SSA to solve its problems. The first improvement includes the development of a new equation for salps’ position update. The use of this new equation is controlled by using Singer's chaotic map. The purpose of the first improvement is to enhance SSA solutions' diversity. The second improvement includes the development of a new local search algorithm (LSA) to improve SSA exploitation. The proposed DSSA was combined with the K-nearest neighbor (KNN) classifier in a wrapper mode. 20 benchmark datasets were selected from the UCI repository and 3 Hadith datasets to test and evaluate the effectiveness of the proposed DSSA algorithm. The DSSA results were compared with the original SSA and four well-known optimization algorithms including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Lion Optimizer (ALO), and Grasshopper Optimization Algorithm (GOA). From the obtained results, DSSA outperformed the original SSA and the other well-known optimization algorithms over the 23 datasets in terms of classification accuracy, fitness function values, the number of selected features, and convergence speed. Also, DSSA accuracy results were compared with the most recent variants of the SSA algorithm. DSSA showed a significant improvement over the competing algorithms in statistical analysis. These results confirm the capability of the proposed DSSA to simultaneously improve the classification accuracy while selecting the minimal number of the most informative features.

Feature Selection of BOF Steelmaking Process Data Based on Denary Salp Swarm Algorithm

In order to eliminate irrelevant or redundant data and improve the accuracy of basic oxygen furnace (BOF) steelmaking endpoint prediction model, a novel denary version of the salp swarm algorithm (SSA) is proposed in this paper and applied for feature selection of BOF steelmaking process data in wrapper mode. SSA is one of the recently proposed algorithms, which is inspired by the swarming behavior of salps in deep water. Firstly, the proposed denary SSA presets the dimension of solutions instead of the strategy of indeterminate number that will lead to different results over various runs. Then the native and binary versions of SSA are applied to generate candidates for leader salp; meanwhile, a probability function is utilized in DSSA to replace each element of leader salp. Finally, an update strategy for follower salps is used to enhance the exploitation of the SSA algorithm. The proposed method is employed to find the optimal solution that maximizes the regression accuracy and minimizes the non-repeatability of the feature selection on BOF steelmaking process data. The performance of the proposed approach is compared with various state-of-the-art approaches in terms of different assessment criteria. Results show that the proposed denary SSA approach of feature selection provides the repeatable results and obtains higher regression accuracy.