Salp Swarm Optimization Algorithm Research Articles

Enhancement of dynamic stability by optimal location and capacity of UPFC: A hybrid approach

In the paper, the modified salp swarm optimization algorithm (MSSA) and moth-flame optimization algorithm (MFO) based optimal location and capacity of Unified Power Flow Controller (UPFC) to improve the dynamic stability of the power system is proposed. The novelty of the proposed method is exemplified in the improved searching ability, random reduction and reduced complexity. Here, the MSSA is used to optimize the location of the UPFC while the generator fault occurs. The MSSA selects the maximum power loss line as the optimum location to place UPFC as per the objective function, since the generator fault violates the system equality and inequality constraints from the secure limit. From the UPFC control parameters, the minimum voltage deviation is optimized using the MFO algorithm. The minimum voltage deviation has been used to find the optimum capacity of the UPFC. Then the optimum UPFC capacity is applied in the optimum location, which enhances the dynamic stability of the system. The proposed method is implemented in the MATLAB/Simulink platform and the performance is evaluated by means of comparison with the different techniques like GSA-BAT, MFO-FF, SSO-FF, BAT-FF and CS-FF algorithms. The comparison results demonstrate the superiority of the proposed approach and confirm its potential to solve the problem.

Optimizing performance attributes of electric power systems using chaotic salp swarm optimizer

ABSTRACT This paper investigates the performance of a chaotic salp swarm optimization (CSSO) algorithm for solving optimal power flow (OPF) problems. The proposed CSSO-based method is applied on five different types of objective functions (OFs) which include generation costs minimization, environmental pollution/emission reduction, minimizing the transmission active power losses, enhancing the voltage profile, and upgrading system stability. Single and multi-objective frameworks are considered to attain various operational, economic, environmental and technical benefits. Initially, single OFs are used to formulate the optimization problem, and at later stage, simultaneous multiple objectives are optimized subject to a set of equality and inequality constraints. To prove the viability of the proposed CSSO-based OPF, standard IEEE 30-bus and 57-bus test systems via 16 studied cases are investigated. In addition, the subsequent cropped results are compared with other competing recent optimization methods in the literature. It can be reported that the cropped best fuel costs when they are optimized using the CSSO are 798.93 $/h and 41,666.66 $/h for the IEEE 30-bus and 57-bus test cases, respectively. The numerical results and performance tests along with comprehensive comparisons clearly indicate the superiority of the CSSO in achieving the given objectives.

A Novel Approach for Detection of Fake News on Social Media Using Metaheuristic Optimization Algorithms

Deceptive content is becoming increasingly dangerous, such as fake news created by social media users. Individuals and society have been affected negatively by the spread of low-quality news on social media. The fake and real news needs to be detected to eliminate the disadvantages of social media. This paper proposes a novel approach for fake news detection (FND) problem on social media. Applying this approach, FND problem has been considered as an optimization problem for the first time and two metaheuristic algorithms, the Grey Wolf Optimization (GWO) and Salp Swarm Optimization (SSO) have been adapted to the FND problem for the first time as well. The proposed FND approach consists of three stages. The first stage is data preprocessing. The second stage is adapting GWO and SSO for construction of a novel FND model. The last stage consists of using proposed FND model for testing. The proposed approach has been evaluated using three different real-world datasets. The results have been compared with seven supervised artificial intelligence algorithms. The results show GWO algorithm has the best performance in comparison with SSO algorithm and the other artificial intelligence algorithms. GWO seems to be efficiently used for solving different types of social media problems.

Hybrid Moth-Flame and Salp Swarm Optimization Algorithm

Hybrid Moth-Flame and Salp Swarm Optimization Algorithm

Side lobe pattern synthesis using hybrid SSWOA algorithm for conformal antenna array

In this paper, the pattern synthesis of conformal antenna array using Salp Swarm Whale Optimization Algorithm (SSWOA) algorithm is proposed. The proposed hybrid algorithm is the combination of Salp Swarm Optimization (SSA) and Whale Optimization Algorithm (WOA). In the proposed algorithm, the SSA algorithm plays the role of guiding the evolution and the WOA algorithm works as an assistant. Moreover, after updating the salps position, with WOA the best known position are evolved to enhance the optimum search ability. When applied to antenna array pattern synthesis by taking advantages of SSA and WOA, the proposed optimization has high convergence accuracy and faster convergence speed. Several typical test functions and optimization examples of a linear array pattern synthesis are illustrated to show the performance of the hybrid algorithm. Experimental results show that the proposed hybrid algorithm is superior to SSA and WOA when applied to Sphere, Griewank, Rosenbrock, and Rastrigin test functions.

A Novel Salp Swarm Optimization MPP Tracking Algorithm for the Solar Photovoltaic Systems under Partial Shading Conditions

To extract the maximum solar power from the photovoltaic (PV) panel/array with the high conversion efficiency under partial shading condition (PSC), this paper discusses a new and an efficient maximum power point (MPP) tracking algorithm. The proposed algorithm is based on the bio-inspired salp swarm optimization (SSO), and the algorithm forecasts the global MPP (GMPP) with the fast convergence to GMPP and high tracking efficiency. The SSO algorithm thus reduces the computational burden as encountered in whale optimization algorithm (WOA), and gray wolf optimization (GWO) algorithm discussed in the various literatures. The modeling and simulation of the proposed SSO algorithm are done with the help of Matlab/Simulink software to validate the effectiveness to locate the MPP during PSCs. The simulation results prove that the proposed SSO algorithm exhibits a high PV power output with the tracking efficiency of more than 95% at the faster convergence rate to GMPP. The SSO algorithm is experimentally verified on the conventional boost converter under different shading conditions.

Multi-objective-based robust unit commitment using hydro-thermal-wind: a hybrid technique

PurposeThe purpose of this paper is to solve economic emission dispatch problem in connection of wind with hydro-thermal units.Design/methodology/approachThe proposed hybrid methodology is the joined execution of both the modified salp swarm optimization algorithm (MSSA) with artificial intelligence technique aided with particle swarm optimization (PSO) technique.FindingsThe proposed approach is introduced to figure out the optimal power generated power from the thermal, wind farms and hydro units by minimizing the emission level and cost of generation simultaneously. The best compromise solution of the generation power outputs and related gas emission are subject to the equality and inequality constraints of the system. Here, MSSA is used to generate the optimal combination of thermal generator with the objective of minimum fuel and emission objective function. The proposed method also considers wind speed probability factor via PSO-artificial neural network (ANN) technique and hydro power generation at peak load demand condition to ensure economic utilization.Originality/valueTo validate the advantage of the proposed approach, six- and ten-units thermal systems are studied with fuel and emission cost. For minimizing the fuel and emission cost of the thermal system with the predicted wind speed factor, the proposed approach is used. The proposed approach is actualized in MATLAB/Simulink, and the results are examined with considering generation units and compared with various solution techniques. The comparison reveals the closeness of the proposed approach and proclaims its capability for handling multi-objective optimization problems of power systems.

A novel non-isolated positive output voltage buck-boost converter

Purpose A two switches non-isolated DC-DC novel buck-boost converter for charging the battery of electric vehicle is projected in this paper. The performance of the converter is compared with conventional buck-boost and transformer-less P/O buck-boost converter by Shan and Faqiang. The detail operation and performance analysis of the proposed converter is described both in continuous conduction mode and discontinuous conduction mode. A state space model and simulation model is designed in MATLAB. The PID controller parameters are tuned using Single-objective Salp swarm optimization algorithm using MATLAB. The controller is implemented using DSP board. The hardware and simulation results are projected in the paper to validate the effectiveness of the proposed buck-boost converter. A comparison analysis is projected among conventional converter and Shan & Faqiang converter. Design/methodology/approach The converter state space model is designed and simulation model is also developed in MATALAB. The controller is implemented using DSP board. The parameters are obtained using optimization technique using SSA algorithm. The hardware design is also implemented, and the result is compared with the Shan and Faqiang converter. The efficiency of the converter is also tested. Findings The converter is providing a higher efficiency. The inductor current is also positive in both buck and boost mode. The robustness of the controller is better for a wide range of variation of input voltage because the output voltage remains almost constant. Therefore, this is very suitable for battery charging and PV module application. Practical implications For battery charging from PV module where voltage fluctuation is frequent. Social implications The authors can use household applications to charge the battery using PV module. Originality/value The converter design concept is new. Optimization is used to find the parameters of the controllers and is implemented in hardware design. The parameters obtained provide robustness in the converter performance.

Chaotic salp swarm algorithm for SDN multi-controller networks

Software-defined networking (SDN) is a novel network paradigm that enables flexible management for networks. However, with the increase in network capacity, a single controller of SDN has many limitations on both performance and scalability. Distributed multi-controller deployment is a promising method to satisfy fault tolerant and scalability. There are still open research issues related to controllers placement, and the optimal number of deployed controllers. In this paper, a dynamic optimization algorithm that is based on the Salp Swarm Optimization Algorithm (SSOA) is developed with the introduction of chaotic maps for enhancing the optimizer’s performance. The algorithm dynamically evaluates the optimum number of controllers and the optimal connections between switches and controllers in large scale SDN networks. In order to evaluate the proposed algorithm, several experiments were conducted and implemented in various scenarios. Moreover, the algorithm was compared to the linear and meta-heuristic algorithms. Simulation results show that the proposed algorithm outperforms meta-heuristic algorithms and a game theory based algorithm in terms of execution time and reliability.