Charged System Search Algorithm Research Articles

A hybrid CSS-GW algorithm for finding optimum location of multi semi-active MR dampers in buildings

Selecting optimum locations to install the dampers has a significant importance to achieve the best performance of them in reducing dynamic responses. Different methods can be used to find the optimum location of dampers in structures. One of the most widely used methods is the meta-heuristic algorithms. In this paper, charged system search (CSS) and grey wolf (GW) algorithms are hybridised (HCSS-GW) to improve the searching abilities in finding optimum locations of the multi-magnetorheological (MR) dampers in buildings. To illuminate the validity of the proposed HCSS-GW algorithm in solving the other optimisation problems, some benchmark test functions are selected to compare the performance of it with both standard CSS and GW algorithms in evolving best solution. The obtained results indicate that the proposed algorithm is highly robust and accurate in comparison to its constitutive algorithms and can be utilised successfully in finding optimum locations of dampers in different buildings.

A hybrid CSS-GW algorithm for finding optimum location of multi semi-active MR dampers in buildings

Selecting optimum locations to install the dampers has a significant importance to achieve the best performance of them in reducing dynamic responses. Different methods can be used to find the optimum location of dampers in structures. One of the most widely used methods is the meta-heuristic algorithms. In this paper, charged system search (CSS) and grey wolf (GW) algorithms are hybridised (HCSS-GW) to improve the searching abilities in finding optimum locations of the multi-magnetorheological (MR) dampers in buildings. To illuminate the validity of the proposed HCSS-GW algorithm in solving the other optimisation problems, some benchmark test functions are selected to compare the performance of it with both standard CSS and GW algorithms in evolving best solution. The obtained results indicate that the proposed algorithm is highly robust and accurate in comparison to its constitutive algorithms and can be utilised successfully in finding optimum locations of dampers in different buildings.

Tribe–charged system search for parameter configuration of nonlinear systems with large search domains

In most cases, the standard optimization algorithms are capable of identifying the required number of parameters for a specific design problem; however, this process is difficult and inefficient in dealing with some specific complex situations such as confronting large initial search domains. In this article, the tribe–charged system search algorithm is proposed and utilized for parameter identification of magnetorheological fluid dampers. The new method is partitioned into three different phases, called the isolated, communing and united phases, and is developed to overcome the likely early convergence of the standard charged system search algorithm. Two different kinds of Bouc–Wen hysteretic model which simulate the nonlinear behaviour of dampers are used to represent the efficiency and capability of the proposed metaheuristic method. The results show that the new algorithm can be successfully used as a robust method for finding solutions to difficult problems with a large search domain.

Optimization of Module Parameters for PV Power Estimation Using a Hybrid Algorithm

This article proposes a novel method to estimate the optimal parameters and power outputs for photovoltaic (PV) power generation. Accurate estimation for PV power generation allows efficient scheduling to meet the load demand and reduces the effect of uncertainty for a microgrid. The parameters that are provided by the PV manufacturer have a nonlinear relationship with power output and may vary with the aging of the PV cells. To allow finer and more accurate estimation for PV power output, the parameters of the single-diode $R_{p}$ model are transformed into 13 parameters under various weather conditions. The principal component analysis (PCA) and an assessment index are used to delete the parameters that have little effect on the output. Using the actual input/output data, a hybrid charged system search (HCSS) algorithm is then used to estimate the optimal parameters. When the parameters are optimized, the estimation for PV power output can be produced as long as the inputs are given. The proposed method is tested on two different PV power generation systems. To verify the performance of the proposed method, the results are compared with the results for the application of the traditional differential evolution (DE) and particle swarm optimization (PSO) methods.

Variant of the charged system search algorithm for the design of optimal linear phase finite impulse response filters

ABSTRACT Digital signal filtering is one of the prime area which is frequently used in practical applications. In the class of digital filters, the prominent filters include – filters with finite impulse response (FIR) and filters with infinite impulse response (IIR). Low pass, high pass, band pass and band stop filters are the different types of filters that are currently employed for carrying out filtering actions. Filters are used for practical applications to reduce the noise incurred while processing the signals received, whether it may be an audio signal, video signal, bio-medical signal and so on. The key features for the design of filters include the optimization of coefficients and in turn the design of coefficients is based on attaining maximum stop band attenuation with less ripple rates. This paper proposes the soft computing based wavelet concept being introduced in the charged system search algorithm at the updation process. The scaling factor in the updation equation is implemented with a wavelet introduced to improve the exploration and exploitation capability of the algorithm. This introduction of wavelet into the algorithm results in faster convergence of the algorithm and proves its effectiveness in comparison with that of the other approaches as available in the literature.

Optimization of Water-Supply and Hydropower Reservoir Operation Using the Charged System Search Algorithm

The Charged System Search (CSS) metaheuristic algorithm is introduced to the field of water resources management and applied to derive water-supply and hydro-power operating policies for a large-scale real-world reservoir system. The optimum algorithm parameters for each reservoir operation problems are also obtained via a tuning procedure. The CSS algorithm is a metaheuristic optimization method inspired by the governing laws of electrostatics in physics and motion from the Newtonian mechanics. In this study, the CSS algorithm’s performance has been tested with benchmark problems, consisting of highly non-linear constrained and/or unconstrained real-valued mathematical models, such as the Ackley’s function and Fletcher–Powell function. The CSS algorithm is then used to optimally solve the water-supply and hydropower operation of “Dez” reservoir in southern Iran over three different operation periods of 60, 240, and 480 months, and the results are presented and compared with those obtained by other available optimization approaches including Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and Constrained Big Bang–Big Crunch (CBB–BC) algorithm, as well as those obtained by gradient-based Non-Linear Programming (NLP) approach. The results demonstrate the robustness and superiority of the CSS algorithm in solving long term reservoir operation problems, compared to alternative methods. The CSS algorithm is used for the first time in the field of water resources management, and proves to be a robust, accurate, and fast convergent method in handling complex problems in this filed. The application of this approach in other water management problems such as multi-reservoir operation and conjunctive surface/ground water resources management remains to be studied.

Economic dispatch of power systems using an adaptive charged system search algorithm

In this article, an adaptive charged system search (ACSS) algorithm is developed for the solution of the economic dispatch problems. The proposed ACSS is based on the charged system search (CSS) which is a meta-heuristic algorithm utilizing the governing Coulomb law from electrostatics and the Newtonian laws of mechanics. Here, two effective strategies are considered to present the new ACSS. The first one is an improved initialization based on opposite based learning and subspacing techniques. The second one is Levy flight random walk for enriching updating process of the algorithm. Many types of economic dispatch cases comprising 6, 13, 15, 40, 160 and 640 units generation systems are testified as benchmarks ranging from small to large scale problems. These problems entail different constraints consisting of power balance, ramp rate limits, prohibited operating zones and valve point load effects. Additionally, multiple fuel options and transmission losses are included for some test cases. Moreover, simple constraint handling functions are developed in terms of penalty approach which can readily be incorporated into any other meta-heuristic algorithm. Results indicate that the ACSS either outperform or perform well in comparison to the CSS and other optimizers in finding optimized fuel costs.

Automated layout design of multi-span reinforced concrete beams using charged system search algorithm

PurposeThe preliminary layout design of structures impacts upon the entire design process and, consequently, the total cost. The purpose of this paper is to select the most economical layouts that satisfy structural and architectural requirements, while considering the reciprocal effects of cost factors and layout variables at the preliminary stages of design.Design/methodology/approachThis paper presents an automated method for cost optimization of geometric layout design of multi-span reinforced concrete (RC) beams subjected to dynamic loading by using the charged system search (CSS) algorithm. First, a novel cost optimization approach for geometric layout problems is introduced, in which both cost parameters and dynamic responses are considered in the preliminary layout design of beams. The proposed structural optimization problem with constraints on the static and dynamic equilibrium, architectural dimensions and structural action effects is solved using the CSS algorithm.FindingsThe proposed CSS algorithm for solving the cost optimization problem can be easily used for optimizing the span lengths and is also capable of working with various cost functions. The presented examples show that the proposed algorithm using the new cost optimization function provides satisfactory results and can result in over 7 per cent cost saving.Originality/valueThis is an original paper proposing a novel methodology for preliminary layout design of concrete beams.

Charged System Search and Magnetic Charged System Search Algorithms for Construction Site Layout Planning Optimization

Construction site layout planning can be considered as an effort to place different temporary facilities in available site locations such that multiple objectives are satisfied as much as possible. With the extension of high-rise building construction and construction activities besides the lack of available spaces in construction sites, proper utilization of this resource has been highlighted because of its significant positive influences on direct cost, safety, and security of the site which consequently affects the total cost and schedule of the project. Thus the construction site layout planning is considered as one of the essential and important phases in construction projects. Site layout planning problem is an NP-Hard problem from the viewpoint of complexity. In this research, two prominent meta-heuristic algorithms, namely Charged System Search (CSS) and Magnetic Charged System Search (MCSS) are utilized to optimize the site layout planning problem. The obtained results of implementing these two algorithms for two different types of site space modeling are compared with the results of the Particle Swarm Optimization (PSO) algorithm and also those of the previous studies. The results illustrate the capability of the CSS and MCSS algorithms in solving the present problem.

Combined PID and LQR controller using optimized fuzzy rules

In this paper, a combination of PID controller and linear quadratic regulator is proposed. A fuzzy switching module is applied to optimally fuse both controllers. A new adaptive version of charged system search algorithm optimizes the membership functions of the fuzzy module. By the time, the algorithm changes itself to find a proper solution faster. To show the efficiency of the designed intelligent controller, the results of a simulated unicycle robot under disturbances are presented.

Web Page Interface Optimization Based on Nature-Inspired Algorithms

This article describes how the conversion rate of a web page depends on the interface usability degree. Optimization of existing interfaces as the matter of improving their usability faces a number of difficulties. In the first place, the unified objective function selection method for such optimization is not set up; that is resulting in necessity of qualified experts' participation for its implementation. In the second place, the corresponding optimization problem will have a high dimension, which makes the classical optimization methods unsuitable for the problem solution. Nature-inspired algorithms have undeniable advantages in comparison with classical optimization algorithms for solving high-dimensional problems, such as for example the optimization of web interfaces by their usability criterion. In this article, new web page interface optimization methods based on nature-inspired algorithms are proposed. In particular, genetic algorithms (GAs), artificial bee colony algorithms (ABC), and charged system search algorithms (CSSs) were analyzed. The conducted experiments revealed the advantages of these algorithms for posed problem solutions and showed research prospects in this direction.

Capacity optimisation for an SAMS considering LCOE and reliability objectives

This study proposes a novel method to optimally allocate capacity for a stand-alone microgrid system (SAMS). An SAMS is usually found on offshore islands or in the areas, where electricity cannot be delivered by utility companies. With the continuing development of renewable energy, the photovoltaic (PV) plant, wind turbine generator and battery energy storage system are integrated into an SAMS to reduce generation cost, mitigate environmental damage and increase generation efficiency. In terms of the uncertainty of renewable generation and to allow optimal capacity allocation for an SAMS, a combination of a Monte Carlo simulation, an enhanced charged system search algorithm and a Pareto-based fuzzy decision-making method is used in this study. The objectives considered are the minimisation of both the levelised cost of energy (LCOE) and the expected energy not supplied. An efficient scheme that reduces the convergence time is also developed. The proposed method is tested using an SAMS that is located on an offshore island of Taiwan. Testing results show that the proposed method produces more stable convergence, lower LCOE value, smaller PV capacity and lower power curtailment than those for the differential evolution and particle swarm optimisation methods.

Application of Multi-objective Charged System Search Algorithm for Optimization Problems

The charged system search algorithm is a relatively new optimization algorithm developed based on some principles from physics and mechanics. This paper presents an approach in which Pareto dominance is incorporated into the charged system search in order to allow this algorithm to handle problems with some multi-objective functions; the proposed algorithm will be called Multi-Objective Charged System Search (MOCSS). Well-known mathematical and engineering benchmarks are used to evaluate the proposed algorithm and the results have been compared with other new approaches. The results of implementing the new algorithm on some test problems show that the proposed algorithm outperforms the other algorithms in terms of Generational Distance, Maximum Spread, Spacing, Coverage of two Set and Hypervolume Indicator. Results of well-known mathematical examples indicate that the new approach is highly competitive and can be considered as a viable alternative to solve multi-objective optimization problems. These results encourage the application of the proposed method to more complex and real-world multi-objective optimization problems. The proposed method can deal with highly nonlinear problems with complex constraints and diverse Pareto optimal sets.

Economics- and Reliability-Based Design for an Offshore Wind Farm

This study examines the optimal layout of an offshore wind farm for a fixed number of wind turbines (WTs) and a fixed area of coverage. After the optimal layout of the turbines is determined, the topology of the cable connections is optimized. The wind decay that is associated with the wake effect is considered and the charged system search algorithm is utilized to obtain the optimal WT layout that maximizes the power output of the wind farm. The Wind Atlas Analysis and Application Program software is utilized to verify the optimality of the layout. Mixed integer linear programming is utilized to optimize the topology of radial cable connections. The system reliabilities of the proposed layout and the traditional regular layout are considered. Finally, the optimal wind-farm layout that maximizes wind power output, minimizes cable cost, and maximizes reliability is obtained.

REDUCTION OF ACTIVE POWER LOSS BY ADAPTIVE CHARGED SYSTEM SEARCH ALGORITHM

This paper presents a new optimization algorithm called Adaptive Charged System Search Algorithm (ACA) for solving optimal power problem. Coulomb law from electrostatics and the Newtonian laws of mechanics are forming the basics of the proposed algorithm. Adaptive Charged System Search Algorithm (ACA) is a multi-agent approach in which each agent is a Charged Particle (CP) & they affect each other based on their fitness values, separation of distances. The quantity of the resultant force is determined by using the electrostatics laws and the quality of the movement is determined using Newtonian mechanics laws. Proposed Adaptive Charged System Search Algorithm (ACA) has been tested in Standard IEEE 57,118 bus systems & real power loss has been comparatively reduced with voltage profiles are within the limits.

Optimization of motion estimation hardware using charged system optimization with low power multipliers for H.264 encoders

Motion estimation has a critical role to play in the transmission and compression of digital video. As it has a complexity in terms of computation that is inherent it is a big challenge for the implementation of real time codes. Recently there have been studies on block matching algorithms (BMA) for bringing down the complexity in the computation and has also got better attention in many algorithms that are very effective for very large scale integration (VLSI) systems for bringing down the complexity. These BMA algorithms are relatively easy to implement which can give solutions that are suboptimal even if the entire search has not been performed. The entire search is high in terms of cost of computation that prevents it from being applied in the systems of VLSI. A novel and fast motion estimation based algorithm known as charged system search (CSS) and full search algorithm (FSA) for multipliers of low power and for H.264 encoders is proposed. The CSS is used in all fields of optimization and is well suited for most domains and does not require any gradient information. The results have proved that proposed FSA has better performance.

Optimum Design of Truss Structures Under Frequency Constraints using Hybrid CSS-MBLS Algorithm

Optimum design of truss structures under frequency constraints is a complicated highly non-linear optimization problem with nonconvex solution space. In this paper, a hybrid Charged System Search (CSS) algorithm with Migration-based Local Search (MBLS) is proposed for resolving this problem. The CSS algorithm as a developed metaheuristic optimization algorithm is inspired by the governing laws of electrostatics in physics and the governing laws of motion from the Newtonian mechanics. In the proposed hybrid CSS-MBLS algorithm, the convergence speed of the standard CSS algorithm is enhanced by the MBLS mechanism. Numerical results obtained from some design examples reveal the successfulness and effectiveness of the proposed algorithm in solving truss optimum design problem under frequency constraints.

Hybrid fuzzy charged system search algorithm based state estimation in distribution networks

This paper proposes a new hybrid charged system search (CSS) algorithm based state estimation in radial distribution networks in fuzzy framework. The objective of the optimization problem is to minimize the weighted square of the difference between the measured and the estimated quantity. The proposed method of state estimation considers bus voltage magnitude and phase angle as state variable along with some equality and inequality constraints for state estimation in distribution networks. A rule based fuzzy inference system has been designed to control the parameters of the CSS algorithm to achieve better balance between the exploration and exploitation capability of the algorithm. The efficiency of the proposed fuzzy adaptive charged system search (FACSS) algorithm has been tested on standard IEEE 33-bus system and Indian 85-bus practical radial distribution system. The obtained results have been compared with the conventional CSS algorithm, weighted least square (WLS) algorithm and particle swarm optimization (PSO) for feasibility of the algorithm.

Efficient force method for tetrahedron finite element analysis using mathematical optimization technique

PurposeThe efficiency of the finite element analysis via force method depends on the overall flexibility matrix of the structure, while this matrix is directly affected from null bases vectors. As the null bases for an indeterminate structure are not unique, for an optimal analysis, the selected null bases should be sparse and banded corresponding to sparse, banded and well-conditioned flexibility matrix. This paper aims to present an efficient method for the formation of optimal flexibility matrix of finite element models comprising tetrahedron elements via mathematical optimization technique.Design/methodology/approachFor this purpose, a linear mixed integer programming model is presented for finding sparse solution of underdetermined linear system, which is correspond to sparse null vector. The charged system search algorithm is improved and used to find the best generator for formation of null bases.FindingsThe efficiency of the present method is illustrated through some examples. The proposed method leads to highly sparse, banded and accurate null basis matrices. It makes an efficient force method feasible for the analysis of finite element model comprising tetrahedron elements.Originality/valueThe force method, in which the member forces are used as unknowns, can be appealing to engineers. The main problem in the application of the force method is the formation of a self-stress matrix corresponding to a sparse flexibility matrix. In this paper, the highly sparse, banded and accurate null basis matrices gains by using mathematical optimization technique.

Optimum two-dimensional crack modeling in discrete least-squares meshless method by charged system search algorithm

In this paper, a node adaptive rearrangement is presented based on the estimated error in various domains for some problems in the fracture mechanics by Discrete Least-Squares Meshless method (DLSM). This method is one of the approximate methods recently introduced and used in the various elds. The method is based on minimization of the least-squares functional with respect to the nodal parameters, and it uses moving least-squares method for calculating the shape functions. Due to the natural process of problem solving, after calculating the shape functions, the residuals are calculated and their values are considered as an objective function for rearrangement of the nodes. There are three popular methods for constructing shape functions in discontinuous domains, and here, the transparency method is utilized. Similar to other numerical methods, there are di erent procedures for re nement and improvement of the results; however, adaptive rearrangement can be employed without increasing the computational cost. In this paper, the Charged System Search (CSS) algorithm is used as a tool for adaptive rearrangement or repositioning process. Eciency and e ectiveness of the proposed adaptive rearrangement technique is tested by some benchmark two-dimensional crack examples with available analytical solution around crack tips.