Charged System Search Algorithm Research Articles

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.

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.

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.

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.

Tribe-charged system search for global optimization

In this paper, an improved metaheuristic algorithm called Tribe-Charged System Search is proposed for global optimization. In this improved algorithm, the main searching loop of the standard Charged System Search algorithm is modified to achieve a better convergence performance. In this algorithm, the searching phase of the algorithm is divided into three distinct phases called "Tribes" in which the searching process is conducted differently in each phase based on the communication allowance between tribes. These changes cause the algorithm focuses on high global searching for the early iterations while the concentrating in local search process is considered through the last iterations. In order to validate the performance of the new algorithm, 4 mathematical benchmark functions alongside the 3 well-known constrained problems and 2 engineering design problems are utilized. The performance of the new algorithm is compared to six other metaheuristic algorithms. The results of the mathematical, constrained and engineering problems prove that the new method is capable of providing very competitive results among the other metaheuristics.

Shear wall layout optimization of tall buildings using Quantum Charged System Search

This paper presents a developed meta-heuristic algorithm to optimize the shear walls of tall reinforced concrete buildings. These types of walls are considered as lateral resistant elements. In this paper, Quantum Charged System Search (QCSS) algorithm is presented as a new optimization method and used to improve the convergence capability of the original Charged System Search. The cost of tall building is taken as the objective function. Since the design of the lateral system plays a major role in the performance of the tall buildings, this paper proposes a unique computational technique that, unlike available works, focuses on structural efficiency or architectural design. This technique considers both structural and architectural requirements such as minimum structural costs, torsional effects, flexural and shear resistance, lateral deflection, openings and accessibility. The robustness of the new algorithm is demonstrated by comparing the outcomes of the QCSS with those of its standard algorithm.

The estimation of low and high-pass active filter parameters with opposite charged system search algorithm

Algorithms are frequently used to solve problems that have a large search space and take a long time to be mathematically solved. They can later be improved with different improvement methods based on the structure and the type of the problem. In this study, the charged system search algorithm (CSS), which has been successfully implemented in the solutions of numerous engineering problems studied within the literature, was improved by introducing opposition-based learning method (OBL) to it in two different methods. With these improved algorithms, solutions were developed for 30-dimensional multimodal test functions in the first place and the results were discussed. In the second place, the parameters of active filters given below were determined from E24 standard series with developed approaches.Filters are electronic circuits that enhance the wanted frequency components of electric signals applied on their inputs and remove harmonics and interferences from these signals. They are divided into two types; active and passive filters. Active filters are produced with transistors or op-amps. They are financially more advantageous compared to passive filters. These filters are preferred especially in low frequencies due to their low costs. Adjustable for a large frequency domain, active filters are very convenient in terms of size and weight and their designs are highly simple. They can easily be connected successively without affecting one another. In this study, the parameter values of Sallen-Key topology Butterworth low and high-pass active filters, which have an extensive area of use, were determined through improved algorithms.My suggestions for the future studies are these: the effects of the opposite position learning approach on other heuristic algorithms can be analysed solving test functions and different engineering problems; different approaches other than the two approaches proposed in this study, can be developed for the opposite position learning concept; LPF and HPF designs solved in the study can be solved for different degrees and stages and finally new designs can be done for different filter types and different resistor and capacitor series that haven’t been handled in this study.

Optimal Design of Magnetorheological Damper Based on Tuning Bouc-Wen Model Parameters Using Hybrid Algorithms

This paper presents a useful approach to optimally design magnetorheological (MR) dampers used in structural buildings. To fulfill this aim, damper parameters are regarded as the design variables whose values can be obtained through an optimization process. To improve the quality of searching for the optimum parameters of MR dampers, charged system search (CSS) and grey wolf (GW) algorithms, two of the most widely utilized meta-heuristic algorithms, are used together, and hybrid CSS-GW is presented. To show the authenticity and robustness of the new algorithm in solving optimization problems, some benchmark test functions are tested, at first. Then, an eleven-story benchmark building equipped with 3 MR dampers is considered to get the optimum design of the MR damper using the hybrid CSS-GW. Results show that the developed hybrid algorithm can successfully figure out the optimum parameters of the MR dampers.

Optimal design of large-scale frames with an advanced charged system search algorithm using box-shaped sections

In the present article, an advanced charged system search (ACSS) algorithm is designed for optimizing the large-scale frame structures using box-shaped sections for columns. The proposed ACSS is an extended version of the charged system search (CSS) which is a metaheuristic algorithm that uses the electrostatics and Newtonian laws of mechanics under the conditions of the Coulomb law. Two large-scale 3D frames with 1026 and 1935 components are optimized using AISC-LRFD to show the efficiency of using the box-shaped sections. Overall performance of the ACSS algorithm with box-shaped sections is compared to those of the upper bound strategy for integrated versions of the standard Big Bang Big Church algorithm and two of its newly developed variants. The results show the successful performance of using steel box-shaped columns in comparison to the frames with I-shaped sections. The numerical results show that the ACSS is efficient and robust compared to its standard version.

Optimal Design of Reinforced Concrete Cantilever Retaining Walls Utilizing Eleven Meta-Heuristic Algorithms: A Comparative Study

In this paper, optimum design of reinforced concrete cantilever retaining walls is performed under static and dynamic loading conditions utilizing eleven population-based meta-heuristic algorithms. These algorithms consist of Artificial Bee Colony algorithm, Big Bang-Big Crunch algorithm, Teaching-Learning-Based Optimization algorithm, Imperialist Competitive Algorithm, Cuckoo Search algorithm, Charged System Search algorithm, Ray Optimization algorithm, Tug of War Optimization algorithm, Water Evaporation Optimization algorithm, Vibrating Particles System algorithm, and Cyclical Parthenogenesis Algorithm. Two well-known methods consisting of the Rankine and Coulomb methods are used to determine lateral earth pressures acting on cantilever retaining wall under static loading condition. In addition, Mononobe-Okabe method is employed for dynamic loading condition. The design is based on ACI 318-05 and the goal of optimization is to minimize the cost function of the cantilever retaining wall. The performance of the utilized algorithms is investigated through an optimization example of cantilever retaining wall. In addition, convergence histories of the algorithms are provided for better understanding of their performance.

Proposed variants of charged system search algorithm for location area optimisation in mobile wireless communication networks

Location area optimisation is used to diminish the location update cost and paging cost in mobile wireless communication networks. Retaining heuristic optimisation technique, helps to diminish the location and paging cost, the problem occurs in this technique is combinational optimisation in nature. Handiness of mobile users growing day by day and many users will be allocated to various mobile subscribers and thus forecasting the ideal area is always a big job. Charged system search algorithm (CSSA) is employed to overwhelm the local and global minima that happened often during the peers of the run process. The variants introduced into the CSSA include the wavelet models and the gravitational search algorithm (GSA) models. The prominent features of both wavelet model and GSA model are obtained and are shared with the charged system search algorithm to minimise the total cost experienced for location area optimisation in mobile wireless communication networks (MWCN).

Proposed Variants of Charged System Search Algorithm for Location Area Optimization in Mobile Wireless Communication Networks

Location area optimisation is used to diminish the location update cost and paging cost in mobile wireless communication networks. Retaining heuristic optimisation technique, helps to diminish the location and paging cost, the problem occurs in this technique is combinational optimisation in nature. Handiness of mobile users growing day by day and many users will be allocated to various mobile subscribers and thus forecasting the ideal area is always a big job. Charged system search algorithm (CSSA) is employed to overwhelm the local and global minima that happened often during the peers of the run process. The variants introduced into the CSSA include the wavelet models and the gravitational search algorithm (GSA) models. The prominent features of both wavelet model and GSA model are obtained and are shared with the charged system search algorithm to minimise the total cost experienced for location area optimisation in mobile wireless communication networks (MWCN).