Charged System Search Research Articles

A charge system search based watermarking technique for gray-scale images using DWT–SVD

A charge system search based watermarking technique for gray-scale images using DWT–SVD

Attitude control of a quadrotor using PID controller based on differential evolution algorithm

In this study, an energy efficiency study has been carried out through the moment values of a quadrotor applied in the position control with the differential evolution algorithm (DE), which is under the evolutionary algorithms among meta-heuristic algorithms particle swarm optimization (PSO) which is among swarm-based algorithms and gravity search algorithm (GSA) among physics-based algorithms and charged system search (CSS) algorithm, which are all well-known in the literature. The efficient use of energy in electrically powered flying mechanisms is important in terms of completing its task. The aim of this study is to obtain proportional–integral–derivative (PID) coefficients that minimize the energy consumption caused by quadrotor moments. First of all, 12 PID coefficients calculated with traditional Ziegler Nicholes, and trying-error methods for the control of the quadrotor have been trained with DE, PSO, GSA and CSS algorithms and their cost values have been found for this problem. In the study, optimum PID coefficients have been found separately with each algorithm and torque data giving energy efficiency have been obtained by applying them to the state equations. Although the cost minimization convergence value obtained from DE does not look good compared to other algorithms, it has been observed that the moment values of the obtained PID coefficients in the control program are lower. In the study, it has been shown that the PID coefficients obtained from DE reach the target with approximately less energy than the maximum-moment value of the PSO, which provides the closest moment value. It can be thought that this value contributes to energy consumption in each orbital movement of the quadrotor. The simulation results show that the coefficients obtained from DE provide better energy efficiency and are superior to other algorithms compared in terms of energy efficiency.

An effective sizing and sensitivity analysis of a hybrid renewable energy system for household, multi-media and rural healthcare centres power supply: A case study of Kaele, Cameroon

The vast majority of rural Africans lack access to clean, reliable, and sustainable electrical energy. This has deprived rural inhabitants of the opportunity to receive advanced healthcare delivery and modern education. In this study, an off-grid hybrid system composed of solar panels, wind turbines, battery banks and diesel-powered generators has been designed to fulfil the electrical loads requirements of a household, a multi-media and healthcare centres situated at Kaele, Cameroon. Initially, the wind and solar potential of Kaele were investigated and four meta-heuristics algorithms, namely, the colliding bodies’ optimization, charged system search, teaching-learning-based optimization (TLBO) and the water evaporation optimization algorithms are applied to the hybrid system and the results are compared with regards to the system net present cost (NPC). Thereafter, three off-grid hybrid systems: PV/Wind/Battery/Diesel, PV/Battery/Diesel, and Wind/Battery/Diesel were compared based on a techno-economic and environmental analysis. The results showed that, the mean yearly wind speed and solar radiation of Kaele are 6.7 m/s and 7.20 kWh/m2/day, respectively. Comparing the search power of the aforementioned algorithms on the sizing problem, it has been found that the TLBO algorithm produces better results than the other algorithms. The results also showed that, the PV/Wind/Battery/Diesel configuration is the cost-effective option for supplying the household, multi-media and healthcare centres. The obtained optimal combination and the NPC of the PV/Wind/Battery/Diesel system are as follows: For household, the most appropriate configuration combines 9 solar panels, 2 wind turbines, 33 battery banks, 1 diesel generator and the obtained NPC corresponds to 26111.2$; for the multi-media centre, 52 solar panels, 2 wind turbines, 96 battery banks and 1 diesel generator which resulted to a NPC of 55511.2$; and for the healthcare centre, the ideal configuration consists of 13 solar panels, 1 wind turbine, 13 battery banks and 1 diesel generator with a NPC of 19008.7$. The cost of energy of the optimal system was found to be 0.2419$/kWh, 0.2195$/kWh and 0.2158$/kWh for household, multi-media and healthcare establishments, respectively. The results also revealed that for household and healthcare centre the obtained break-even grid extension distance was 0.443 km and 1.23 km, respectively for the PV/Wind/Battery/Diesel system. Since this separation is smaller than the nearest grid extension distance of 5 km, it is concluded that the proposed hybrid energy system is suitable for powering the household, multi-media and healthcare centres in Kaele, Cameroon. Finally, a sensitivity analysis of each relevant component cost, resources availability and microeconomic variables of the PV/Wind/Battery/Diesel system is carried out to acquire a more suitable configuration.

Optimum design of real-size reinforced concrete bridge via charged system search algorithm trained by Nelder-Mead simplex

In this paper, an optimization method based on the combination of Nelder-Mead (NM) simplex concepts and charged system search algorithm (CSS) is proposed and called NM-CSS. NM, as a search technique, involves the iterative formation and transformation of a geometrically arranged set of points known as a simplex to uncover the optimal solution. By incorporating NM into metaheuristic algorithms, the convergence speed and solution quality can be notably boosted. Throughout each iteration, the solutions undergo various operations, including reflection, contraction, and expansion, to enhance the effectiveness of the algorithm. In this algorithm, produced solutions by the CSS are sorted and divided into two groups of good and bad solutions, and then the NM operators are applied to bad solutions to generate the possible best ones. The proposed algorithm is verified through some mathematical benchmark functions. Then, it is implemented on a real-size reinforced concrete bridge to reach an optimum design. To recognize the effective parameters in the design of structural components of reinforced concrete bridges, a sensitivity analysis is carried out. The total cost of materials in piers and the deck is defined as an objective function, and the cross-section area of structural elements and longitudinal reinforcements are chosen as design variables. The results of simulations represent the stability and robustness of the proposed NM-CSS method compared to standard CSS. In other words, utilizing the proposed NM-CSS algorithm in an optimum design of the piers and deck results in saving %3.685 and %2.084 of costs, respectively, in comparison with the results of the standard CSS.

Reliability-based design optimization of post-tensioned self-centering rocking steel frame structures

This paper reports the Reliability-Based Design Optimization (RBDO) of post-tensioned self-centering rocking steel frame structures with buckling-restrained bracing systems. The objective was to investigate the reliability index, optimization of self-centering structures to reach minimum weight using metaheuristic algorithms, and their application in linear and nonlinear reliability problems. For this purpose, three steel structures (10-, 15-, and 20-storey structures) were each evaluated with three steel framing systems (buckling-restrained bracing, post-tensioned self-centering multiple rocking steel frame with buckling-restrained bracing, and post-tensioned self-centering single rocking steel frame with buckling-restrained bracing), making a total of nine models. Subsequently, specific 2D frames of the structural models were numerically simulated using nonlinear dynamic analysis (NDA) in OpenSees software. Optimization was carried out in two stages: with and without considering the uncertainties. Finally, for calculating the failure probability and structural reliability index, Monte-Carlo Simulation (MCS) was employed. Results indicate that, although considering the reliability index leads to heavier structures, structural safety was increased and failure probability was reduced to even lower specified levels. Furthermore, among the incorporated algorithms, the Colliding Bodies Optimization (CBO) and Charged System Search (CSS) algorithms proved to have superior performances in terms of accuracy and converging time. On average, using these two algorithms for cases without uncertainty resulted in maximum weight reductions of 36%, 30%, and 32% for 10-, 15-, and 20-storey structures. In addition, for cases with uncertainty, these two algorithms with reliability index (β) of 2 respectively reduced the weight of 10-, 15-, and 20-storey structures by 21%. Also, it was shown that not considering the uncertainties can increase the failure probability by up to 23%.

Highly sensitive multifunction protection coordination scheme for improved reliability of power systems with distributed generation (PVs)

AbstractThe high penetration of distribution generators (DGs), such as photovoltaic (PV), has made optimal overcurrent coordination a major concern for power protection. In the literature, the conventional single or multi‐objective function (OF) for phase overcurrent relays (OCRs) scheme faces challenges in terms of stability, sensitivity, and selectivity to handle the integration of DGs and ground fault scenarios. In this work, a new optimal OCR coordination scheme has been developed as a multifunction scheme for phase and ground events using standard and non‐standard tripping characteristics. This research introduces and validates a coordinated optimum strategy based on two new optimization approaches, the Tug of War Optimization algorithm (TWO) and the Charged System Search algorithm (CSS), to mitigate the effects of DGs on fault currents and locations across the power network. Industrial software is used to create a case study of a CIGRE power network equipped with two 10 MW PV systems, and the results of the proposed new optimum coordination scheme are compared to traditional schemes. The findings show that the proposed multifunction OCR scheme is able to reduce the tripping time of OCRs over different fault and grid operation scenarios and increase the sensitivity of the relays in islanding operation mode.

Optimal Design of Planar Steel Frames Using the Hybrid Teaching–Learning and Charged System Search Algorithm

In this study, a novel effective algorithm called HTC which results from a hybridization process between the teaching–learning-based optimization (TLBO) and charged system search (CSS) algorithms is proposed and utilized to optimize planar steel frames. Among the features of the proposed algorithm, simplicity and low number of setting parameters can be mentioned. The main goals of this hybridization process are to establish a balance between exploration and exploitation, increase the rate of convergence, and avoid getting trapped in the local optimal point. Moreover, the purpose of optimizing steel frames is to calculate their minimum weight by choosing suitable sections as well as considering the stress and displacement constraints in accordance with the requirements of the AISC design regulations using the load and resistance factor design (LRFD) method. To evaluate the performance of the HTC algorithm, standard benchmark steel frames are examined, and the results of the proposed algorithm are compared with other metaheuristic optimization methods. According to the outcomes of the comparison, the proposed algorithm has better performance than the other methods.

Metaheuristic-based crack detection in beam-type structures using peridynamics theory: A comparative study

This study is a first attempt to provide an effective tool for crack prediction in beam-type structures using the peridynamics (PD) theory and metaheuristic-based optimization. The norm of the difference between the response of the tested beam and that of the trial model (analyzed using PD), is minimized to find the position and depth of crack. Model calibration is discussed thoroughly. Five well-known metaheuristics as well as an upgraded charged system search algorithm are considered. Results reveal that the proposed procedure can effectively localize and detect the crack severity, even in noise contaminated cases.

Modelling and Analysis of the Cone Coupling Problem Using Optimization

A coupling is a mechanism that transmits operative power between two shafts that are revolving at different speeds. A coupling connects two shafts at their ends and can slip or fail depending on the torque limit. It is an essential component of any power transmission system and may survive for a very long time if properly designed and maintained. This study's current research, a newly developed optimization algorithms are used to minimize the volume of cone coupling. The current study presented here compares modern metaheuristic methods for optimizing the design of the cone coupling problem. The algorithms used are particle swarm optimization (PSO), crow search algorithm (CSA), enhanced honeybee mating optimization (EHBMO), Harmony search algorithm (HSA), Krill heard algorithm (KHA), Pattern search algorithm (PSA), Charged system search algorithm (CSSA), Salp swarm algorithm (SSA), Big bang big crunch optimization (B-BBBCO), Gradient based Algorithm (GBA). The performance of these algorithms is assessed both statistically and subjectively. The algorithms' performance is evaluated quantitatively and qualitatively using consistency, simplicity and quality. The experimental results on the cone clutch problem shows that PSO produces greater results than EHBMO, whereas CSA and BBCO produce approximately identical results.

An efficient hybrid-based charged system search algorithm for active filter design

An efficient hybrid-based charged system search algorithm for active filter design

Design and evaluation of hysteresis models for structural systems using a fuzzy adaptive charged system search

Many hysteresis models have been proposed for the simulation of the nonlinear behavior of structures each of which has certain advantages depending on specific applications and desired objectives. The Bouc–Wen–Baber–Noori model is one of the hysteresis models that has been utilized for a wide range of applications. However, the parameter tuning of this model has been conducted based on expert knowledge, which has not led to the development of a precise nonlinear model. The main contribution of this paper is to propose a metaheuristic-based parametric identification process for the design of the Bouc–Wen–Baber–Noori hysteresis model and evaluate the results by using some established experimental investigation methods. To fulfill this aim, the Fuzzy Adaptive Charged System Search (F-CSS) is proposed for optimization in which a fuzzy-logic-based parameter tuning process is utilized to achieve better performance in comparison with the standard Charged System Search algorithm (CSS). For nonlinear dynamic analysis, an Iterative Hysteretic Analysis (IHA) process is also introduced for conducting the precise analysis of the structure with exact solutions. Comparing the metaheuristic-based results to the experimental findings demonstrates that the proposed algorithm is capable of providing very competitive results. Besides, the proposed adaptive method is capable of producing very competitive results in comparison with different optimization algorithms.

Hybrid teaching–learning-based optimization for solving engineering and mathematical problems

In this work, a new and effective algorithm called hybrid teaching–learning-based optimization (TLBO) and charged system search (CSS) algorithms (HTC) are proposed to solve engineering and mathematical problems. The CSS is inspired by Coulomb and Gauss’s electrostatic laws of physics as well as the Newtonian mechanic laws of motion. The TLBO is inspired by the interaction between teacher and student in a classroom. Usually, the TLBO gets trapped in the local optimal due to the lack of a system for measuring the distance between the student and the optimal point. In order to solve this problem, the CSS algorithm, which is based on the electrical physics laws, is utilized. In the CSS algorithm, each factor is stored under the influence of the best local and global positions, and it is used in subsequent iterations as the possible optimal answers. In fact, this leads to a better balance between exploration and exploitation. In order to validate the proposed method, the CEC2021 and CEC2005 mathematical functions are optimized. Additionally, to show the applicability of the proposed algorithm and to evaluate its performance and convergence rate, several benchmark truss structures are optimized. The weight of the structural elements is taken into account as the objective function, which is optimized under displacement and stress constraints. The results of the proposed algorithm are compared with some other well-known meta-heuristic methods. The results show that the hybrid HTC algorithm improved the convergence rate and quickly obtained the optimal and desired design. The hybrid HTC algorithm can be adapted to solve other complex mathematical and optimization problems.

Seismic loss optimum design of steel structures using learning‐based charged system search

SummaryOne of the structural engineers' challenges on a regular basis is balancing the expense of initial construction with the cost of future structural loss. At first appearance, employing the optimization method seems to be a viable option. However, since both the structure's analysis and design, as well as the computation of the cost of future loss, are time‐consuming and expensive, combining these processes with the costly optimization progression is prohibitively expensive. The purpose of this study is to present a methodology for the risk‐based optimum design of steel frame structures, as well as to enhance a previously published metaheuristic algorithm for a better optimization approach. The methodology given here allows structural designers to account for seismic risks in the design optimization process without incurring expensive computing expenditures. This approach may be appealing for practical work since it minimizes time‐consuming charges and provides designers with a structural impression. Furthermore, as compared to the standard version, the new optimization algorithm improves performance while decreasing computing costs. Bayesian linear regression is used in conjunction with a parameter identification challenge to derive probabilistic models for estimating structural analysis demand responses. The minimum amount of total initial cost and seismic loss cost is regarded as the objective function of the design of three chosen mid‐ to high‐rise moment frames for the optimization purpose. The results demonstrated enhanced optimization performance as well as a decreased loss cost for employed structures.

Optimization of Large-Scale Frame Structures Using Fuzzy Adaptive Quantum Inspired Charged System Search

Optimization of Large-Scale Frame Structures Using Fuzzy Adaptive Quantum Inspired Charged System Search

A semi-active control algorithm for nonlinear structures based on uniform distribution of deformation theory

Semi-active control is a promising method for vibration control of civil structures. Magneto-rheological (MR) damper is one of the most common and practical devices among other semi-active control devices. The command voltage is the only controllable property of an MR damper. There are several algorithms for determining the MR damper command voltage such as active control-based algorithms. On active-based algorithms, an initial reference control force is achieved based on a reference active control algorithm, then this force converts to applied voltage. Uniform Distribution of Deformations (UDD) theory is a concept which intends to use the maximum capacity of elemental and structural ductility. When deformations are uniform throughout the entire structure, damages are mostly uniform and the maximum of damage is reduced. This point can be used for designing a new active-based semi-active algorithm. The innovative Uniform Deformation Control (UDC) is an active-based control algorithm. Reference active control forces are achieved using a massless Virtual Added frame with Pinned connections (VAP). This virtual frame makes deformation of the structure more uniform. The axial forces of the VAP beams are the reference active control forces. Afterward, based on these forces, the command voltages of semi-active MR damper are determined. The VAP characteristics are optimized using Charged System Search (CSS) optimization method for making the structural deformations more uniform. Therefore, this new innovative proposed algorithm focuses on reducing damages contrary to common algorithms which focus on reducing responses. The contribution of the present paper is to introduce the new damage-based algorithm. Then, this algorithm will be investigated in comparison with two other common algorithms. The effectiveness of the new proposed algorithm is proved via nonlinear numerical investigations on an office building. The new algorithm can reduce displacements and accelerations with relatively low control forces while making deformations more uniform.

Innovative Adaptive Protection Approach to Maximize the Security and Performance of Phase/Earth Overcurrent Relay for Microgrid Considering Earth Fault Scenarios.

Microgrids are increasingly paying attention to the emerging of distributed energy resources. However, microgrid protection has challenged engineers because of the varying nature of fault current distribution during grid-connected and islanded operations. This paper suggests a novel approach of coordination between phase and earth fault overcurrent relays to ensure stability and security for the performance of protection schemes in active distribution networks. In this study, a combination of phase and earth overcurrent relays is coordinated to avoid mis-coordination in the overcurrent protection schemes. In the proposed approach, phase overcurrent function was employed as backup protection for earth fault function locally in the same relay instead of the traditional remote backup earth fault protection coordination approach. The suggested coordination approach provides a fast and reliable protection performance in different earth faults scenarios. The coordination was tested on the IEC Microgrid Benchmark network. Also, the objective function was modified to reduce the overall operational time using two recently developed metaheuristic optimization algorithms known as the charged system search (CSS) and Teaching-Learning-Based Optimization Algorithm (TLBO) in the MATLAB package. Finally, the time multipler setting values were adapted and compared to verify the speed and reliability of the proposed coordination approach using ETAP.

Metaheuristically optimized nano-MgO additive in freeze-thaw resistant concrete: a charged system search-based approach

With progressive advances in the synthesis, characterization, and commercialization of nanoparticles and nanomaterials, these modern engineered materials are becoming an ingredient of innovative structural materials for various applications in civil and construction engineering. In this research, MgO nanoparticles were systematically added to normal concrete samples in order to investigate the effect of these nanomaterials on the durability of the samples under freeze and thaw conditions. The compressive and tensile strengths as well as the permeability of concrete samples containing nanoparticles were measured and compared with the corresponding values of control samples without nanoparticles. The curing time of the concrete samples, the amount of nanoparticles, and the water-cement ratios (w/c) were the variables of the experiments. Moreover, data clustering and the Charged System Search (CSS) algorithm were utilized as the numerical analysis and optimization methods. The regression analysis before clustering and after clustering proved the process of clustering is a prerequisite of regression analysis. Furthermore, the CSS optimization method showed that the optimum amount of nano MgO is 1% of the weight of cement, which can increase the compressive strength of concrete by 9.12% more than plain samples over 34 days.

Optimizing the Operation Release Policy Using Charged System Search Algorithm: A Case Study of Klang Gates Dam, Malaysia

In planning and managing water resources, the implementation of optimization techniques in the operation of reservoirs has become an important focus. An optimal reservoir operating policy should take into consideration the uncertainty associated with uncontrolled reservoir inflows. The charged system search (CSS) algorithm model is developed in the present study to achieve optimum operating policy for the current reservoir. The aim of the model is to minimize the cost of system performance, which is the sum of square deviations from the distinction between the release of the target and the actual demand. The decision variable is the release of a reservoir with an initial volume of storage, reservoir inflow, and final volume of storage for a given period. Historical rainfall data is used to approximate the inflow volume. The charged system search (CSS) is developed by utilizing a spreadsheet model to simulate and perform optimization. The model gives the steady-state probabilities of reservoir storage as output. The model is applied to the reservoir of Klang Gates for the development of an optimal reservoir operating policy. The steady-state optimal operating system is used in this model.

Fuzzy Adaptive Charged System Search for global optimization

This study proposes a new fuzzy adaptive Charged System Search (CSS) for global optimization. The suggested algorithm includes a parameter tuning process based on fuzzy logic with the aim of improving its performance. In this regard, four linguistic variables are defined which configures a fuzzy system for parameter identification of the standard CSS algorithm. This process provides a focus for the algorithm on higher levels of global searching in the initial iterations while the local search is considered in the last iterations. Twenty mathematical benchmark functions, the Competitions on Evolutionary Computation (CEC) regarding CEC 2020 benchmark, three well-known constrained, and two engineering problems are utilized to validate the new algorithm. Moreover, the performance of the new algorithm is compared and contrasted with other metaheuristic algorithms. The obtained results reveal the superiority of the proposed approach in dealing with different unconstraint, constrained, and engineering design problems.

Stochastic Remote Sensing Event Classification over Adaptive Posture Estimation via Multifused Data and Deep Belief Network

Advances in video capturing devices enable adaptive posture estimation (APE) and event classification of multiple human-based videos for smart systems. Accurate event classification and adaptive posture estimation are still challenging domains, although researchers work hard to find solutions. In this research article, we propose a novel method to classify stochastic remote sensing events and to perform adaptive posture estimation. We performed human silhouette extraction using the Gaussian Mixture Model (GMM) and saliency map. After that, we performed human body part detection and used a unified pseudo-2D stick model for adaptive posture estimation. Multifused data that include energy, 3D Cartesian view, angular geometric, skeleton zigzag and moveable body parts were applied. Using a charged system search, we optimized our feature vector and deep belief network. We classified complex events, which were performed over sports videos in the wild (SVW), Olympic sports, UCF aerial action dataset and UT-interaction datasets. The mean accuracy of human body part detection was 83.57% over the UT-interaction, 83.00% for the Olympic sports and 83.78% for the SVW dataset. The mean event classification accuracy was 91.67% over the UT-interaction, 92.50% for Olympic sports and 89.47% for SVW dataset. These results are superior compared to existing state-of-the-art methods.