Hybrid Gravitational Search Algorithm Research Articles

Implementation of Hybrid GSA SHE Technique in Hybrid Nine-Level Inverter Topology

The recent trend in multilevel inverters (MLIs) is to produce the high quality staircase ac voltage waveform while utilizing the optimum number of switching devices. In this context, this article presents a combination of topological and modulation scheme-based solution for the optimum performance of MLI. A concept of adaptable dc voltage link in conjunction with modified H-bridge unit is introduced to reduce the number of switching devices. Further to improve the quality of voltage waveform and to reduce the switching losses in the proposed topology, a hybrid gravitational search algorithm (GSA) selective harmonic elimination (SHE) technique is proposed and implemented on the newly developed MLI topology with symmetric dc sources. Comparative analysis is performed with the conventional and existing hybrid cascaded MLI topologies, which exhibits the superiority of the proposed topology in terms of reduction in count of switching devices, count of components, and conduction losses. The efficacy of the proposed hybrid GSA SHE technique is shown in the simulated results. The feasibility of the proposed MLI topology and SHE technique is further verified by the experimental results of a nine-level inverter with both equal and unequal dc sources.

Performance assessment of hybrid PVT air collector using GSA-CS algorithm

In the last few decades, enormous attention is drawn towards PV/T systems due to their advantages as compared to solar PV or solar thermal systems individually. In this proposed paper, hybrid Gravitational Search Algorithm (GSA)-Cuckoo Search (CS) has been implemented to optimize the parameters of glazed hybrid PVT air collector. Although there are various parameters which affects the thermal and electrical performance of PVT system but in this paper only four parameters namely Channel length below the PV panel, channel depth, velocity of fluid flowing through the channel and temperature of fluid at the inlet of the channel have been considered for optimization using the hybrid approach. The outcomes shows that GSA-CS algorithm is proved to be very efficient techniques to be used to optimized the parameters of hybrid PVT module. The result of the analysis shows that the average value of exergy efficiency is 14.8228% when the parameters are optimized using hybrid GSA-CS algorithm.

Gravitational search algorithm based novel workflow scheduling for heterogeneous computing systems

The significance of workflow applications (WAs) in various domains like, scientific experiments, research, education, health-care, etc. is increasing with the technological advancements of modern computing world. The WA consists of a set of tasks with complex dependency relationships. Generating a valid execution sequence by preserving precedence constraints is challenging. Workflow scheduling algorithms (WSAs) are invoking more attention as one of the real time concerns to the researchers. Although, number of research attitudes have been shown for WSA, still it is hard to design one single coherent algorithm that satisfies multiple criteria simultaneously. Moreover, WSA is well known due to its non-deterministic polynomial (NP)-hard nature. In this paper, we have proposed a gravitational search algorithm (GSA) based workflow scheduling for heterogeneous computing systems. The proposed work considers multiple conflicting objectives which are minimization of makespan, load-balancing, and energy-consumption. A novel representation of agents is demonstrated by preserving dependency constraints amongst the tasks. A recursive algorithm is designed to generate a valid execution sequence of tasks that helps to restrict the precedence relationship. Derivation of fitness function is done based on the considered multiple objectives. The performances are analyzed and validated by extensive simulations on different set of scientific, Fast Fourier Transformation (FFT), and synthetic workflow applications data set. It is observed that the proposed GSA shows considerable improvements in terms of the considered objectives over recent GSA based approaches like, gravitation search algorithm for load scheduling (GSAL) and hybrid gravitational search algorithm (HGSA). The results are also validated using a statistical hypothesis test, Analysis of Variance (ANOVA) to demonstrate the effectiveness of the proposed work.

Hybrid Intelligent Modelling of the Viscoelastic Moduli of Coal Fly Ash Based Polymer Gel System for Water Shutoff Treatment in Oil and Gas Wells

Qualitative polymer gels exhibiting considerable viscoelastic properties are of immense significance in the petroleum industry as they curtail excessive water production during the tertiary stage of crude oil production. The incorporation of suitable organic or inorganic solid substances into the polymer gel system strengthens its viscoelastic properties. This research work presents hybrid gravitational search algorithm (GSA) and support vector regression (SVR) as novel modelling tools for viscoelastic properties estimation of polymer gel synthesized from polyacrylamide (PAM)/polyethyleneimine (PEI). The coal fly ash (CFA) was incorporated in PAM/PEI gel in order to enhance its viscoelastic characteristics. The hybrid GSA‐SVR models were developed using real‐life experimental data. The results of the models validated using unseen data show a high degree of correlation coefficients (87.3 % for elastic modulus and 97.2 % for viscous modulus), with experimental data generating a small root mean square error (coefficients 669.5Pa for elastic modulus and 38.5Pa for viscous modulus). The developed hybrid models were further used to scrutinize the effect of the addition of various amounts of CFA on the PAM/PEI viscoelastic properties and observed behaviours were presented and discussed. The predictive capability of the developed hybrid model coupled with its strength in modelling the impact of CFA in the PAM/PEI gel are highly meritorious and the outcomes from this investigation would be relevant during the design and formulation of polymer gels employed for water shutoff treatment during hydrocarbon recovery.

Analysis of PID controller for the load frequency control of static synchronous series compensator and capacitive energy storage source-based multi-area multi-source interconnected power system with HVDC link

In this research work, a maiden approach is made by using the static synchronous series compensator (SSSC) and capacitive energy storage (CES) devices as a frequency damping controller in the most realistic scenario of automatic generation control (AGC) of a multi-area multi-source (thermal-hydro-gas) interconnected power system along with the consideration of AC tie-line and HVDC link. At first, the performance of the HVDC link with AC tie line is investigated for the considered power system. A PID controller is utilised to control each of the generating units in all the areas and shows its superior performance compared to other conventional controllers. A hybrid particle swarm optimisation (PSO) and gravitational search algorithm (GSA) is proposed for tuning the gains of the PID controller and is compared with some conventional controllers. Later the effect of SSSC and CES, when added as a damping controller to the power system is analysed. It is observed that, with the addition of the SSSC and CES as a damping controller along with the optimised PID controller, the dynamic performance of the proposed interconnected power system is improved.

Model-free chaos control based on AHGSA for a vibro-impact system

For a vibro-impact system with clearance, the model-free chaos control method based on adaptive hybrid gravitational search algorithm (or AHGSA algorithm for short) is proposed. Nonparametric time-varying dynamic linear model based on pseudo-partial-derivative is established using input/output data of the controlled system, and on this basis, the optimal controller is designed according to the quadratic performance index, and the controller parameters is optimized using AHGSA algorithm. By combining the artificial bee colony search operator and chaos optimization strategy, gravitational search algorithm (or GSA algorithm for short) is improved from three aspects (i.e., population initialization, velocity and position update, gravity coefficient adjustment) to achieve a balance between the global detection ability and the local development ability. AHGSA algorithm has good optimization accuracy and efficiency: The arbitrariness is avoided in controller parameters selection, and the quality of the chaos control is ensured as well. In simulation experiment, the model-free controller optimized is used to control the chaotic motion of a single-degree-of-freedom vibro-impact system with clearance to verify the validity and feasibility of the proposed chaos control method. The simulation results show that the control effect is good, and the proposed chaos control method has the following advantages: the proposed chaos control method does not depend on the precise model of the controlled system, and the controller is easy to be designed and implemented.

Classification of Data by Hybrid Particle Swarm Optimization and Gravitational Search Algorithm

Classification of Data by Hybrid Particle Swarm Optimization and Gravitational Search Algorithm

Synthesis of multi‐polarised upside conical frustum array antenna for 5G mm‐Wave base station at 28/38 GHz

In this study, a multi-polarised millimetre-wave (mm-Wave) base station operating at 28/38 GHz is introduced for future fifth generation (5G) mobile communication networks. The proposed station employs 32-element array antenna distributed in upside conical frustum configuration (UCFC) to synthesise multi-beam patterns adopting directivity and polarisation control. First, the design of dual-band circularly polarised antenna element at 28/38 GHz is discussed. The measured results show that the designed antenna element has a reflection coefficient less than -23 dB with a realised antenna gain of 8 dBi, on average, in the assigned frequency bands. Based on the designed antenna element, the antenna array performance is studied in terms of gain, radiation efficiency, reflection coefficient, and coverage efficiency. The results are compared with those obtained from 32-element structured in an octagonal prism configuration (OPC). For antenna design parameters and multi-beam pattern synthesis, a modified version of hybrid gravitational search algorithm and particle swarm optimisation is proposed. It is found that, the introduced UCFC outperformed the OPC by 19.8%. Moreover, distributing the antenna array in UCFC improved the convergence by 21.7% compared to OPC.

A synergy of the sine-cosine algorithm and particle swarm optimizer for improved global optimization and object tracking

Abstract Due to its simplicity and efficiency, a recently proposed optimization algorithm, Sine Cosine Algorithm (SCA), has gained the interest of researchers from various fields for solving optimization problems. However, it is prone to premature convergence at local minima as it lacks internal memory. To overcome this drawback, a novel Hybrid SCA-PSO algorithm for solving optimization problems and object tracking is proposed. The P b e s t and G b e s t components of PSO (Particle Swarm Optimization) is added to traditional SCA to guide the search process for potential candidate solutions and PSO is then initialized with P b e s t of SCA to exploit the search space further. The proposed algorithm combines the exploitation capability of PSO and exploration capability of SCA to achieve optimal global solutions. The effectiveness of this algorithm is evaluated using 23 classical, CEC 2005 and CEC 2014 benchmark functions. Statistical parameters are employed to observe the efficiency of the Hybrid SCA-PSO qualitatively and results prove that the proposed algorithm is very competitive compared to the state-of-the-art metaheuristic algorithms. The Hybrid SCA-PSO algorithm is applied for object tracking as a real thought-provoking case study. Experimental results show that the Hybrid SCA-PSO-based tracker can robustly track an arbitrary target in various challenging conditions. To reveal the capability of the proposed algorithm, comparative studies of tracking accuracy and speed of the Hybrid SCA-PSO based tracking framework and other trackers, viz., Particle filter, Mean-shift, Particle swarm optimization, Bat algorithm, Sine Cosine Algorithm (SCA) and Hybrid Gravitational Search Algorithm (HGSA) is presented.

Optimization of highly efficient random grating thin-film solar cell using modified gravitational search algorithm and particle swarm optimization algorithm

A design of hydrogenated amorphous silicon (a-Si:H) solar cell (SC) based on one-dimensional subwavelength grating with nonuniform distribution is proposed and optimized using modified hybrid gravitational search algorithm and particle swarm optimization (GSA-PSO). The reported structure consists of nine gratings with different widths, positions, and heights that can enhance the light trapping through the SC. The nonuniform grating positions and geometrical parameters of the proposed design are optimized in terms of absorption, ultimate efficiency, and short circuit current density. The absorption inside the active layer is enhanced by 67.5% over the conventional thin-film SC. Further, the exposed area caused by forward nanotexturing surface is increased. Moreover, the nonuniform grating parameters and distribution support better light absorption than the periodic grating distribution by increasing the optical path length of the incident light through the active layer. Therefore, the suggested SC achieves a broadband absorption improvement. The enhanced absorption in the active layer is reported using three-dimensional finite-difference time-domain method. In addition, the performance of the suggested SC using different active layer materials, i.e., crystalline silicon, a-Si:H, and gallium arsenide is studied. The hydrogenated amorphous silicon-based design shows high ultimate efficiency of 38.73%, short-circuit current density (JSC) of 34.69 mA / cm2, open-circuit voltage of 1.0393 V, and power conversion efficiency of 35.4%. The modified GSA-PSO algorithm shows also high potential for the design and optimization of different types of solar cells.

A hybrid gravitational search algorithm with swarm intelligence and deep convolutional feature for object tracking optimization

Large number of object trackers based on particle swarm optimization (PSO) and its variants have been published in the recent decade. However, the majority of algorithms does not perform well when evaluated against the online object tracking benchmark. In the analysis of the existing swarm intelligence based object trackers, pre-mature convergence, loss in particle information and inadequate feature are identified as the factors that hinder the performance of this class of trackers. In this regard, this paper proposes to use the hybrid gravitational search algorithm (HGSA) to increase the utilization of particle information and to facilitate thorough search inside the video frame before convergence. HGSA elegantly combines GSA's gravitational update component with the cognitive and social components of PSO using a novel weight function. The hybridized algorithm acquires the exploitation of past information and fast convergence property typical of PSO, while retaining the GSA capability in fully utilizing all current information. Moreover, the incorporation of deep convolutional feature is proposed to address the inadequacy of the weak hue, saturation and value (HSV) histogram feature. Experimental results using videos from the online tracking benchmark show that the proposed HGSA tracker with deep convolutional feature (DeepHGSA) has increased accuracy of ADSO, the best existing Swarm Intelligence based tracker, by 50.6% and robustness by 56.9% measured by area under curve.

Performance of Tri-Band Multi-Polarized Array Antenna for 5G Mobile Base Station Adopting Polarization and Directivity Control

In this paper, the performance of tri-band multi-polarized adaptive array antenna for 5G mobile base station is introduced adopting the polarization and directivity control. First, the design of multi-band circularly polarized antenna element at 28/38/48 GHz is presented. The measured results show that the designed antenna element has a reflection coefficient of less than −20 dB in the assigned frequency bands of 28/38/48 GHz with a realized antenna gain of 7.82 dB, 8.39 dB, and 7.73 dB, respectively. Based on the designed antenna element, a 32-element are distributed in an octagonal prism configuration is presented as possible candidates for the future 5G mmWave cellular networks. The antenna array performance is studied in terms of gain, radiation efficiency, return loss, and axial ratio for different scenarios, in addition, the metrics of total scan pattern and coverage efficiency are investigated. To optimize the antenna parameters and synthesizing multi-beam patterns, a modified version of hybrid gravitational search algorithm and particle swarm optimization algorithm is introduced. This enhancement has improved the global search ability and accelerates the convergence capability. The proposed array antenna achieved good S11 < −20 dB with high isolation between the array elements. Besides, an acceptable realized gain of 16.85 dB on average with a radiation efficiency of more than 82 % is obtained in the entire operation bands.

A hybrid gravitational search and pattern search algorithm for tuning damping controller parameters for a unified power flow controller—A comparative approach

AbstractThis paper mainly focuses on the development of a hybrid gravitational search algorithm and the pattern search (hGSA‐PS) algorithm for the optimal tuning of a unified power flow controller (UPFC) ‐based damping controller. The optimal parameter assessment for a damping controller craves a decisive tuning method, which can reduce the system error, thus improves the system stability. The design problem associated with the proposed controllers is formulated as an optimization problem and the hGSA‐PS is used to find the UPFC‐based damping controller parameters. Thus, by achieving the optimal settings of the variables of the damping controller, the damping of the system is reduced, and concurrently stability of the system is maintained. A detailed investigation is carried out considering the 4 alternatives UPFC‐based damping controller. As evidence, the numerous system response curves confirm that the planned hybrid GSA‐PS algorithm achieves improved performance compared with the conventional algorithms and the recent hybrid GA‐GSA algorithm. The proposed work basically compares the hGSA‐PS algorithm with the recent developed hybrid GA‐GSA algorithm and the conventional GSA algorithm by considering the convergence rate. Finally, the stability performance of a Simulink‐based 2 area 4‐machine power system is analyzed by applying the hGSA‐PS algorithm. From the simulation results, it can be presumed that the proposed hybrid approach is the best method for outlining the UPFC‐based damping controller.

Single-machine scheduling with learning effect and resource-dependent processing times in the serial-batching production

• Under the given resource allocation, the structural properties on job batching policies and batching sequencing are proposed. • An optimal batching policy is derived for the special case. • An effective hybrid GSA–TS algorithm is developed to solve the studied problem. In this paper, we study a single machine scheduling problem by simultaneously considering the processing method of serial-batching, learning effect, resource-dependent processing times, and setup operations. We consider minimizing the makespan as the objective of the studied problem under the constraint that the total resource consumption does not exceed a given limit. For the special case where the resource allocation is given, we first propose the structural properties for job batching policies and batching sequencing, and an optimal batching policy is derived based on these properties. Then, we develop a novel hybrid GSA–TS algorithm which combines the Gravitational Search Algorithm (GSA) and the Tabu Search (TS) algorithm to solve the general case. Computational experiments with different scales show the effectiveness and efficiency of the proposed algorithm.

Operation management of daily economic dispatch using novel hybrid particle swarm optimization and gravitational search algorithm with hybrid mutation strategy

This paper presents a hybrid particle swarm optimization and gravitational search algorithm based on hybrid mutation strategy (HGSAPSO-M) to optimize economic dispatch (ED) including distributed generations (DGs) considering market-based energy pricing. A daily ED model was formulated and a hybrid mutation strategy was adopted in HGSAPSO-M. The hybrid mutation strategy includes two mutation operators, chaotic mutation, Gaussian mutation. The proposed algorithm was tested on IEEE-33 bus and results show that the approach is effective for this problem.

Hybrid gravitational search and pattern search–based image thresholding by optimising Shannon and fuzzy entropy for image compression

ABSTRACTImage compression is very significant process in image transmission at high data rate over a communication channel and to increase the storage capacity of storage device. Ordinary image thresholding is a class of clustering technique used for image compression because of its simplicity, robustness and accuracy but it is computationally expensive when extending for multilevel image thresholding. An attempt is made in this paper to reduce the computational time of multilevel image thresholding using hybrid gravitational search algorithm and pattern search (hGSA-PS) by optimising a criterion such as Shannon entropy or Fuzzy entropy for seeking appropriate threshold values. From literature, gravitational search algorithm (GSA) is designed to explore the global search space (exploitation), and pattern search (PS) is designed to exploit a local search space (exploration), so we hybridise the GSA and PS to achieve exploitation and exploration of search space by incorporating strengths and weakness of both, and results are compared with differential evolution, particle swarm optimisation and bat algorithm and proved better in standard deviation, peak signal-to-noise ratio (PSNR), weighted PSNR and reconstructed image quality. The performance of the proposed algorithm is found better with fuzzy entropy compared to Shannon entropy.

Neural Network-based Model Predictive Control with CPSOGSA for SMBR Filtration

This paper presents the development of neural network based model predictive control (NNMPC) for controlling submerged membrane bioreactor (SMBR) filtration process.The main contribution of this paper is the integration of newly developed soft computing optimization technique name as cooperative hybrid particle swarm optimization and gravitational search algorithm (CPSOGSA) with the model predictive control. The CPSOGSA algorithm is used as a real time optimization (RTO) in updating the NNMPC cost function. The developed controller is utilized to control SMBR filtrations permeate flux in preventing flux decline from membrane fouling. The proposed NNMPC is comparedwith proportional integral derivative (PID) controller in term of the percentage overshoot, settling time and integral absolute error (IAE) criteria. The simulation result shows NNMPC perform better control compared with PID controller in term measured control performance of permeate flux.

Hybrid Gravitational Search Algorithm Based on Fuzzy Logic

Gravitational search algorithm (GSA) is a novel heuristic optimization algorithm which is used to search for the global optimal solution by iteration. However, GSA is easy to fall into local minima and convergence slowly. To improve the exploration and exploitation abilities of the GSA, a new hybrid GSA (HGSA) is proposed. In this algorithm, the local search technique (LST) is incorporated into the optimization process of the GSA. For each agent, GSA is performed with probability $p$ , and LST is performed with probability $1-p$ . The probability $p$ is obtained using fuzzy logic. The strategy makes full use of the exploration ability of GSA and the exploitation ability of LST. The HGSA is tested on 23 benchmark functions. By comparing the HGSA with GSA and other algorithms that were published in recent studies, the numerical results demonstrate that the HGSA can improve the performance of GSA in terms of global optimality and solution accuracy.

A comparison of recent metaheuristic algorithms for crashworthiness optimisation of vehicle thin-walled tubes considering sheet metal forming effects

In this paper, recent optimisation techniques have been used to improve crash performance of a thin-walled tube considering influence of the forming history. In the optimisation study, weight and amount of the energy absorption of the thin-walled tubes are chosen as objective function and constraint, respectively. Radial basis functions (RBFs) metamodelling technique was used to approximate the structural responses selected in crash and forming analyses. The performances of recent optimisation algorithms which are particle swarm algorithm, cuckoo search algorithm, gravitational search algorithm (GSA), hybrid gravitational search-Nelder Mead algorithm (HGSANM), league championship algorithm (LCA), firefly algorithm, bat algorithm, interior search algorithm (ISA) and imperialist competitive algorithm (ICA) were investigated to find the optimal thin-walled tube design. The lowest mass of the components is obtained at 1000 function evaluation number (FEN) using hybrid GSA.

Solving k-Barrier Coverage Problem Using Modified Gravitational Search Algorithm

Coverage problem is a critical issue in wireless sensor networks for security applications. The k-barrier coverage is an effective measure to ensure robustness. In this paper, we formulate the k-barrier coverage problem as a constrained optimization problem and introduce the energy constraint of sensor node to prolong the lifetime of the k-barrier coverage. A novel hybrid particle swarm optimization and gravitational search algorithm (PGSA) is proposed to solve this problem. The proposed PGSA adopts a k-barrier coverage generation strategy based on probability and integrates the exploitation ability in particle swarm optimization to update the velocity and enhance the global search capability and introduce the boundary mutation strategy of an agent to increase the population diversity and search accuracy. Extensive simulations are conducted to demonstrate the effectiveness of our proposed algorithm.