Gravitational Search Algorithm Method Research Articles

Adaptation of Number of Filters in the Convolution Layer of a Convolutional Neural Network Using the Fuzzy Gravitational Search Algorithm Method and Type-1 Fuzzy Logic

This paper presents a model of the search for adaptation of parameters and the creation of the membership functions of various fuzzy systems created using the fuzzy gravitational algorithm (FGSA). These fuzzy systems were created to find the optimal number of filters to enter a convolutional neural network (CNN) with an architecture of two convolution layers, as well as two pooling layers respectively and a classification layer, which is responsible for recognizing images. With this model, the results obtained by optimizing this CNN with the FGSA algorithm and the adaptation of parameters using this same algorithm are compared to form the membership functions of fuzzy systems. Both methods and their results are comparing with each other.

The gravitational search algorithm for incorporating TCSC devices into the system for optimum power flow

<span lang="EN-US">This paper proposes a gravitational search algorithm (GSA) to allocate the thyristor-controlled series compensator (TCSC) incorporation with the issue of reactive power management. The aim of using TCSC units in this study is to minimize active and reactive power losses. Reserve beyond the thermal border, enhance the voltage profile and increase transmission-lines flow while continuing the whole generation cost of the system a little increase compared with its single goal base case. The optimal power flow (OPF) described is a consideration for finding the best size and location of the TCSCs devices seeing techno-economic subjects for minimizing fuel cost of generation units and the costs of installing TCSCs devices. The GSA algorithm's high ability in solving the proposed multi-objective problem is tested on two 9 and 30 bus test systems. For each test system, four case studies are considered to represent both normal and emergency operating conditions. The proposed GSA method's simulation results show that GSA offers a practical and robust high-quality solution for the problem and improves system performance.</span>

Hypersonic reentry trajectory planning by using hybrid fractional-order particle swarm optimization and gravitational search algorithm

This paper proposes a novel hybrid algorithm called Fractional-order Particle Swarm optimization Gravitational Search Algorithm (FPSOGSA) and applies it to the trajectory planning of the hypersonic lifting reentry flight vehicles. The proposed method is used to calculate the control profiles to achieve the two objectives, namely a smoother trajectory and enforcement of the path constraints with terminal accuracy. The smoothness of the trajectory is achieved by scheduling the bank angle with the aid of a modified scheme known as a Quasi-Equilibrium Glide (QEG) scheme. The aerodynamic load factor and the dynamic pressure path constraints are enforced by further planning of the bank angle with the help of a constraint enforcement scheme. The maximum heating rate path constraint is enforced through the angle of attack parameterization. The Common Aero Vehicle (CAV) flight vehicle is used for the simulation purpose to test and compare the proposed method with that of the standard Particle Swarm Optimization (PSO) method and the standard Gravitational Search Algorithm (GSA). The simulation results confirm the efficiency of the proposed FPSOGSA method over the standard PSO and the GSA methods by showing its better convergence and computation efficiency.

A novel model for digital predistortion based on a gravitational search algorithm for linearization of transmitters in LTE networks

A high-performance model with a gravitational search algorithm (GSA)-based generalized parallel two-box (GPTB) structure is suggested for digital predistortion in modern transmitters exhibiting memory effects, where the GSA is applied to identify the minimum dimension for the GPTB model. An indirect learning structure in conjunction with the GSA method is employed to identify the coefficients of the GSA-based GPTB model. The GPTB–GSA method is verified using simulations of a transmitter excited by quadrature amplitude modulation (QAM) signals in ADS software and simulations of the GSA in MATLAB software. The MATLAB results demonstrate the ability of the GSA to determine the dimension of the GPTB model efficiently. Also, the adjacent channel power ratio (ACPR) measure is decreased by about 16 dB according to the simulation. The proposed model and algorithm can reduce the number of coefficients by approximately 25% in comparison with the memory polynomial model.

A comparative evaluation of Gravitational Search Algorithm (GSA) against Artificial Bee Colony (ABC) for thermodynamic performance of a geothermal power plant

Optimizing a complex system/problem under real working conditions with optimization methods means ensuring that they operate more efficiently, economical, and eco-friendly. For this purpose, in order to maximize the exergy efficiency of a thermodynamic model of a real operated geothermal power plant (GPP), two optimization methods, namely Gravitational Search Algorithm (GSA) and Artificial Bee Colony (ABC), have been comparatively evaluated in this study. The selected thermodynamic model is a problem that is highly complex, non-linear and unsolvable through mathematical methods. In order to solve the problem, 17 optimization parameters have been selected on the model. In addition, the selected parameters have been divided into 11 groups according to the system equipment specifications to reduce time loss. The results of the study reported that GSA and ABC maximized the exergy efficiency of the real system from 14.52% to 26.31% and 23.92% respectively. The effects of the optimized parameters on the model are observed, and it has been verified by GPP operators, engineers and researchers that no contrariety to logic and engineering discipline existed. Hence, the results of GSA method for the engineering problem addressed in this study are better than those of ABC method and they responded in a much shorter time. The most effective group in both methods is the G3 group related to the turbines. Besides, the most effective optimization parameters on the system performance are the pressure differences in evaporators and mass flow of the geothermal fluid.

Comparison of stability service cost in power grids considering different types of WTs

This study presents a comparative study on dynamic behaviour of two different types of wind turbines (WTs) that are widely used in power systems. Mathematical modelling of two types of WTs in the multi-machine power system is presented, and a suitable framework for the study is provided. To compare the cost of provision of stability service by each of two different WTs, a technical–economical two-objective optimisation problem considering damping indices and generation cost will be formulated and solved by multi-objective gravitational search algorithm method. The presented strategy is carried out for each WT and Pareto-optimal curves are extracted. It is shown that in a system with poor dynamic situation, expensive production units are used that this phenomenon will increase generation cost. Generation costs are analysed by considering two WTs, doubly fed asynchronous generator and permanent magnet synchronous generator and dynamic condition for each type are evaluated. Two scenarios are assumed for evaluation; the first one without retuning of stabilisers and the second one with stabilisers retuned due to system generation situation. The proposed scheme is tested on modified 16-machine 5-area network and simulation results are compared.

P.1.j.024 - Association of sleep quality and neurocognitive dysfunction in patients with depression

Optimizing a complex system/problem under real working conditions with optimization methods means ensuring that they operate more efficiently, economical, and eco-friendly. For this purpose, in order to maximize the exergy efficiency of a thermodynamic model of a real operated geothermal power plant (GPP), two optimization methods, namely Gravitational Search Algorithm (GSA) and Artificial Bee Colony (ABC), have been comparatively evaluated in this study. The selected thermodynamic model is a problem that is highly complex, non-linear and unsolvable through mathematical methods. In order to solve the problem, 17 optimization parameters have been selected on the model. In addition, the selected parameters have been divided into 11 groups according to the system equipment specifications to reduce time loss. The results of the study reported that GSA and ABC maximized the exergy efficiency of the real system from 14.52% to 26.31% and 23.92% respectively. The effects of the optimized parameters on the model are observed, and it has been verified by GPP operators, engineers and researchers that no contrariety to logic and engineering discipline existed. Hence, the results of GSA method for the engineering problem addressed in this study are better than those of ABC method and they responded in a much shorter time. The most effective group in both methods is the G3 group related to the turbines. Besides, the most effective optimization parameters on the system performance are the pressure differences in evaporators and mass flow of the geothermal fluid.

Multi-Objective Network Reconfiguration with Optimal DG Output Using Meta-Heuristic Search Algorithms

Network reconfiguration is a well-known technique for the distribution system to reduce power losses. However, the reconfiguration technique, by itself, could only minimize power losses up to a certain point. Further power losses reduction could be realized via the application of distributed generation (DG). However, the integration of DG to the distribution system at a non-optimal value could instead increase power losses and voltage fluctuation. Therefore, it is vital to develop an effective optimization strategy to determine the optimal output of the DG and simultaneously ensure optimal configuration. This paper presents a simultaneous optimal network reconfiguration with optimal DG output to minimize power losses and improve the voltage profile. Different objectives are discussed in this paper: (1) to minimize power losses, (2) to improve voltage profile index, (3) to maximize DG output. Evolutionary programming, particle swarm optimization, firefly, and gravitational search algorithm methods have been applied for optimal distribution network reconfiguration with optimal DG output. To evaluate the possibilities of the suggested method, simulations using MATLAB software are carried out on an IEEE 33-bus radial distribution system. The obtained outcomes prove the efficiency of the proposed strategy to find an optimal network configuration and optimal output of DG units.

A niche GSA method with nearest neighbor scheme for multimodal optimization

Abstract In this paper, a new niching method based on Gravitational Search Algorithm (GSA) is proposed in which species are formed within the population (swarm) based on a nearest neighbor (NN) scheme. Also, we suggest a scheme to detect the niches inside the population by using the hill valley algorithm without the need of a pairwise comparison between any pair of solutions inside the population. In order to improve the exploitation capability of the proposed niching method, the formed species are balanced such that they are forced to have almost equal number of members. This mechanism enables the species to explore more optima via diversity conservation in the swarm. Experimental results of using several multimodal benchmark functions confirm the effectiveness of the proposed niching scheme compared to well-known existing niching methods.

A novel multi objective Loading Pattern Optimization by Gravitational Search Algorithm (GSA) for WWER1000 core

In this paper, a novel multi objective optimization algorithm, Gravitational Search Algorithm (GSA), is developed in order to implement in the Loading Pattern Optimization (LPO) of a nuclear reactor core. In recent decades several metaheuristic algorithms or computational intelligence methods have been expanded to optimize reactor core loading pattern. Regarding the coupled behavior of Neutronic and Thermal-Hydraulic (NTH) dynamics in a nuclear reactor core, proper loading pattern of fuel assemblies (FAs) depends on NTH aspects, simultaneously. Thus, obtaining optimal arrangement of FAs, in a core to meet special objective functions is a complex problem. Gravitational Search Algorithm (GSA) is constructed based on the law of Gravity and the notion of mass interactions, using the theory of Newtonian physics and searcher agents are the collection of masses. In this work, for multi objective optimization, the NTH aspects are included in fitness function. Neutronic goals include increasing multiplication factor (Keff), decreasing of power picking factor (PPF) and flattening of the power density, also thermal–hydraulic (TH) goals include increasing critical heat flux (CHF) and decreasing average of fuel centers temperature. Therefore, at the first step, GSA method is compared with other metaheuristic algorithms on Shekel's Foxholes problem. In the second step for finding the best core pattern and implementation of the objectives listed, GSA algorithm has been performed for case of WWER1000 reactor. For the NTH calculations, PARCS (Purdue advanced reactor core simulator) and COBRA-EN codes are implemented, respectively. The results demonstrate that GSA algorithm have promising performance and can propose for other optimization problems of nuclear engineering field.

Enhanced Secured Optimal Power Flow by TCSC Parameter Optimization using RBFNN based GSA

Attainment of enhanced secured optimal power flow (OPF) has been established as one of the vital requirement of power system operation in a deregulated structure. This paper establishes a novel hybrid technique which is yielded by combining Radial Basic Function Neural Network (RBFNN) and Gravitational Search Algorithm (GSA). The new velocity and position of agents are less deviated by implementing RBFNN based GSA leading to realization of optimized Thyristor controlled series compensator (TCSC) parameters wherein the results are also compared with traditional GSA method and Fuzzy based GSA algorithm. The superiority of performance is established by comparing the proposed hybrid technique with the previously existing method. The Hybrid method proposed is implemented in MATLAB working platform and results are tested on standard IEEE 30 bus transmission system. The total generated power, power loss and cost of generation are evaluated with variation of system load and reduction in line power limit evaluates the system response to contingency.

A novel optimization method, Gravitational Search Algorithm (GSA), for PWR core optimization

In-core fuel management optimization (ICFMO) is one of the most challenging concepts of nuclear engineering. In recent decades several meta-heuristic algorithms or computational intelligence methods have been expanded to optimize reactor core loading pattern. This paper presents a new method of using Gravitational Search Algorithm (GSA) for in-core fuel management optimization. The GSA is constructed based on the law of gravity and the notion of mass interactions. It uses the theory of Newtonian physics and searcher agents are the collection of masses. In this work, at the first step, GSA method is compared with other meta-heuristic algorithms on Shekel’s Foxholes problem. In the second step for finding the best core, the GSA algorithm has been performed for three PWR test cases including WWER-1000 and WWER-440 reactors. In these cases, Multi objective optimizations with the following goals are considered, increment of multiplication factor (Keff), decrement of power peaking factor (PPF) and power density flattening. It is notable that for neutronic calculation, PARCS (Purdue Advanced Reactor Core Simulator) code is used. The results demonstrate that GSA algorithm have promising performance and could be proposed for other optimization problems of nuclear engineering field.

GRAVITATIONAL SEARCH ALGORITHM WITH CHAOTIC MAP (GSA-CM) FOR SOLVING OPTIMIZATION PROBLEMS

Gravitational Search Algorithm (GSA) is a newly heuristic algorithm inspired by nature which utilizes Newtonian gravity law and mass interactions. It has captured much attention since it has provided higher performance in solving various optimization problems. This study hybridizes the GSA and chaotic equations. Ten chaotic-based GSA (GSA-CM) methods, which define the random selections by different chaotic maps, have been developed. The proposed methods have been applied to the minimization of benchmark problems and the results have been compared. The obtained numeric results show that most of the proposed algorithms have increased the performance of GSA and have developed its quality of solution.

Gravitational search algorithm for probabilistic design of HBPS canals

In this study, the performance of a recent meta-heuristic technique, namely, gravitational search algorithm (GSA) is evaluated for a deterministic as well as for a probabilistic design of canals that have cross-sectional shape of horizontal bottom and parabolic sides (HBPS). The uncertainty in various input parameters is modeled using probabilistic approach and a modified chance-constrained model is presented for an optimal design of HBPS canals with the aim of minimizing the total cost, while satisfying the basic flow constraints and reliability constraints on the canal capacity and overtopping. First, the GSA method is applied to solve the HBPS canal design problem under different constraints, and its performance is evaluated by comparing with the solutions of the deterministic models by the particle swarm optimization and genetic algorithm. Then, the GSA is applied to obtain the solution of the probabilistic model and in view of multiple conflicting goals; pseudo-weight vector approach is adopted to assist in decision-making and demonstrate its applicability for arriving at a suitable decision. The results obtained suggest that the proposed GSA approach has good potential for a reliable and cost-effective design of canals.

Employing Nelder-Mead and Gravitational Search Algorithm Methods in Point Clouds Registration Process

Abstract In reverse engineering it may be required to perform multiple digitizing due to the size and part complexity. Registration process for point clouds is to find the geometric transform between them in which all point clouds are transformed into a single absolute coordinate system. The process of registration is strongly affected by inaccurate data and may fail in the case of noisy data, hence other methods are usually sought to find the transformation matrix. This paper tries to solve the problem in practical applications. The Nelder-Mead and Gravitational Search Algorithm methods were employing for point clouds registration for the first time. The registration was also performed using Singular value decomposition and Genetic Algorithm methods. The four methods were compared in terms of convergence, accuracy and computation time.

Enhanced GSA-Based Optimization for Minimization of Power Losses in Power System

Gravitational Search Algorithm (GSA) is a heuristic method based on Newton’s law of gravitational attraction and law of motion. In this paper, to further improve the optimization performance of GSA, the memory characteristic of Particle Swarm Optimization (PSO) is employed in GSAPSO for searching a better solution. Besides, to testify the prominent strength of GSAPSO, GSA, PSO, and GSAPSO are applied for the solution of optimal reactive power dispatch (ORPD) of power system. Conventionally, ORPD is defined as a problem of minimizing the total active power transmission losses by setting control variables while satisfying numerous constraints. Therefore ORPD is a complicated mixed integer nonlinear optimization problem including many constraints. IEEE14-bus, IEEE30-bus, and IEEE57-bus test power systems are used to implement this study, respectively. The obtained results of simulation experiments using GSAPSO method, especially the power loss reduction rates, are compared to those yielded by the other modern artificial intelligence-based techniques including the conventional GSA and PSO methods. The results presented in this paper reveal the potential and effectiveness of the proposed method for solving ORPD problem of power system.

Gravitational Search Algorithm for Optimal Distributed Generation Operation in Autonomous Network

The gravitational search algorithm (GSA) has been used in this research to optimize output of the multiple distributed generator (DG) units in autonomous distribution network. With the optimal operation of DGs, the voltage profile can be improved, thus reducing the power losses in the system. The performance of GSA is compared with an established paper, which uses the genetic algorithm (GA) in analysing similar problem. The results show that the GSA has superior performance in finding the optimal DG output compared to the GA technique, either in terms of power loss as well as the voltage profile. Furthermore, the consistency of the proposed GSA method is proven by the small standard deviation value obtained from 20 repetitions of the same analysis.

A hybrid approach based on an improved gravitational search algorithm and orthogonal crossover for optimal shape design of concrete gravity dams

A hybrid approach based on an improved gravitational search algorithm (IGSA) and orthogonal crossover (OC) is proposed to efficiently find the optimal shape of concrete gravity dams. The proposed hybrid approach is called IGSA-OC. The hybrid of IGSA and the OC operator can improve the global exploration ability of the IGSA method, and increase its convergence rate. To find the optimal shape of concrete gravity dams, the interaction effects of dam–water–foundation rock subjected to earthquake loading are considered in this study. The computational cost of the optimal shape of concrete gravity dams subjected earthquake loads is usually high. Due to this problem, the weighted least squares support vector machine (WLS-SVM) regression as an efficient metamodel is utilized to considerably predict dynamic responses of gravity dams by spending low computational cost. To testify the robustness and efficiency of the proposed IGSA-OC, first, four well-known benchmark functions in literatures are optimized using the proposed IGSA-OC, and provides comparisons with the standard gravitational search algorithm (GSA) and the other modified GSA methods. Then, the optimal shape of concrete gravity dams is found using IGSA-OC. The solutions obtained by the IGSA-OC are compared with those of the standard GSA, IGSA and particle swarm optimization (PSO). The numerical results demonstrate that the proposed IGSA-OC significantly outperforms the standard GSA, IGSA and PSO.

Gravitational Search Algorithm Optimization for Dynamic Economic Load Dispatch with Valve-Point Effects

The dynamic economic load dispatch problem (DELDP), with the consideration of valve-point effects, is a complicated non-linear constrained optimization problem with non-smooth and non-convex characteristics. The main goal of DELDP is to find out the optimal generation schedule of the generators corresponding to the most economical operating point of the system over the considered timing horizon. In this paper, a new swarm based optimization algorithm, so called gravitational search algorithm (GSA) has been proposed to solve the DELDP with considering valve-point effects and transmission power loss constraints. The performance of the proposed method is rigorously tested for optimality, convergence, robustness and computational efficiency on a 10-unit test system with a 24-h load demand at each 1-h time intervals which is so well-known and common in literature. The proposed algorithm also takes care of different constraints like power balance, ramp rate limits and generation limits. The simulation results reveal that, compared with other methods reported in literatures recently, the proposed GSA method are capable of obtaining better quality solutions with higher efficiency. In addition, sensitivity analysis has been performed on the controlling parameters of GSA to reach the best arrangement for them.

Optimal Placement and Sizing of Thyristor-controlled-series-capacitor using Gravitational Search Algorithm

This paper represents the Gravitational Search Algorithm (GSA) that can be used to determine the optimal location and rating of Thyristor controlled Series Capacitor (TCSC). TCSC is equipment used to regulate and improve power flow in power system. The method used in this study was GSA. TCSC were the implemented on 500kV Java-Bali Power System. Loadflow results before optimization using Newton Rapshon method showed that active power loss was 297.607MW. While loadflow results after optimization using GSA with 5-TCSC obtained were 287.926MW of active power loss, with 10-TCSC, it was obtained 281.143MW of active power loss. In addition, using 15-TCSC, active power loss obtained was 279.405MW. GSA methods can be used to minimize power losses and transmission lines as well as to improve value of voltage in the range of 0.95+ 1.05pu compared with loadflow results before optimization.The more TCSC is used, then value of active power losses small.