Gravitational Search Algorithm Research Articles

Memory, evolutionary operator, and local search based improved Grey Wolf Optimizer with linear population size reduction technique

Optimization of multi-modal functions is challenging even for evolutionary and swarm-based algorithms as it requires an efficient exploration for finding the promising region of the search space, and effective exploitation to precisely find the global optimum. Grey Wolf Optimizer (GWO) is a recently developed metaheuristic algorithm that is inspired by nature with a relatively small number of parameters for tuning. However, GWO and most of its variants may suffer from the lack of population diversity, premature convergence, and the inability to preserve a good balance between exploratory and exploitative behaviors. To address these limitations, this work proposes a new variant of GWO incorporating memory, evolutionary operators, and a stochastic local search technique. It further integrates Linear Population Size Reduction (LPSR) technique. The proposed algorithm is comprehensively tested on 23 numerical benchmark functions, high dimensional benchmark functions, 13 engineering case studies, four data classifications, and three function approximation problems. The benchmark functions are mostly taken from the CEC 2005 and CEC 2010 special sessions, and they include rotated, shifted functions. The engineering case studies are from the CEC 2020 real-world non-convex constrained optimization problems. The performance of the proposed GWO is compared with popular metaheuristics, namely, particle swarm optimization (PSO), gravitational search algorithm (GSA), slap swarm algorithm (SSA), differential evolution (DE), self-adaptive differential evolution (SADE), basic GWO and its three recently improved variants. Statistical analysis and Friedman tests have been conducted to thoroughly compare their performance. The obtained results demonstrate that the proposed GWO outperforms the algorithms compared for the benchmark functions and engineering case studies tested.

An efficient honey badger algorithm for scheduling the microgrid energy management

Nowadays, it is essential for modern grids to operate at optimal scheduling, this helps in reducing the energy costs, mitigating the pollutant emissions, and making better use of renewable energy resources (RESs) such as photovoltaic (PV) and wind turbine (WT). Therefore, this paper proposes an energy management scheme for microgrid (MG) using recent metaheuristic honey badger algorithm (HBA) to enhance its operation via identifying the optimal scheduling of the installed generation units. HBA has the ability to solve complex optimization problem and avoid stuck in local optima due to balance between the exploration and exploitation phases. The constructed MG composes PV, WT, microturbine (MT), fuel cell (FC), and battery storage system. Operation of PV and WT at their normal generations, operation of WT at its rated power, and operation of PV and WT at their maximum limits are three cases analyzed in this work. Two objective functions are considered which are mitigating the operating cost and minimizing the pollutant emission. The proposed HBA is evaluated via conducting comparison to some reported approaches like Fuzzy self-adaptive particle swarm optimizer (FSAPSO), sparrow search algorithm (SSA), and gravitational search and pattern search algorithm (GSA-PS). Moreover, other programmed approaches of aquila optimizer (AO), Tasmanian devil optimizer (TDO), artificial rabbits optimizer (ARO), coronavirus herd immunity optimizer (CHIO), manta-ray foraging optimizer (MRFO), and dynamic arithmetic optimization approach (DAOA) are implemented and compared to the proposed algorithm. The fetched results proved the robustness and preference of HBA in achieving the best operation of MG in all studied operating conditions.

Conformational Search for the Building Block of Proteins Based on the Gradient Gravitational Search Algorithm (ConfGGS) Using Force Fields: CHARMM, AMBER, and OPLS-AA.

Proteins are linear polymers built from a repertoire of 20 different amino acids, which are considered building blocks of proteins. The diversity and versatility of these 20 building blocks with regard to their conformations are key to adopting three-dimensional structures that facilitate proteins to undergo important mechanistic biological processes in living systems. The present investigation reports a conformational search of 20 different amino acids, building blocks of proteins, using three different force fields, CHARMM, AMBER, and OPLS-AA, implemented in the gradient gravitational search algorithm. The search technique (ConfGGS) includes the contribution from both bonded and nonbonded terms using Cartesian coordinates. The efficiency of such conformational searches has also been compared with other optimization algorithms: DE/Best, DE/Rand, and PSO algorithms with respect to computational time and accuracy based on the minimum number of iteration steps and computed lowest mean absolute error (MAE) and mean standard deviation (MSD) values for dihedral angles of respective near-optimal structures. Moreover, the ConfGGS technique has also been extended to an ordered protein fragment (PQITL) extracted from HIV-1 protease (PDB ID: 1YTH), an intrinsically disordered protein fragment, i.e., an amyloid-forming segment (AVVTGVTAV), from the NAC domain of Parkinson's disease protein α-synuclein, residues 69-77 (PDB ID: 4RIK), the experimental NMR atomic-resolution structure of α-synuclein fibrils (PDB ID: 2N0A), and a disulfide bond-containing protein fragment sequence (PCYGWPVCY), residues 59-67 (PDB ID: 6Y4F) toward structure prediction as a close homologue compared with experimental accuracy, using the CHARMM force field. The MolProbity validation results for the protein fragment (PQITL) obtained by ConfGGS/CHARMM are in better agreement with the native protein fragment structure of HIV-1 protease (PDB ID: 1YTH). Furthermore, the computed results have also been compared with the coordinates obtained from the AlphaFold network.

Multi-objective optimal dispatching of combined cooling, heating and power using hybrid gravitational search algorithm and random forest regression: Towards the microgrid orientation

In order to eventually reach the net-zero carbon target, the low carbon transition demands substantial growth in the penetration of renewable generation in energy systems. It is essential to have enough flexible resources to prevent the curtailment of energy systems with high intermittent renewable output to run consistently. Therefore, this study focuses on the multi-objective optimal dispatching model of combined cooling, heating, and power (CCHP) microgrid with power-to-gas (P2G). The structure of the CCHP microgrid and the working principle of P2G are studied and analyzed. First, briefly described the multi-objective optimal dispatch model of the CCHP microgrid with P2G established with the goal of operational and environmental cost. Second, a hybrid gravitational search algorithm and random forest regression (GSA-RFR) are introduced to find out the optimal values. The proposed model verifies that P2G improves the wind abandonment capacity of the CCHP microgrid operation system. Furthermore, it reduces environmental costs and transforms environmental values into economic benefits during system operation. Noting that, the GSA-RFR improves the system economy by 7.13% comparatively.

Design of intelligent nonlinear robust controller for hydro-turbine governing system based on state-dynamic-measurement hybrid feedback linearization method

Due to the stochastic grid loads and intermittent renewable energy sources in today’s power grid, hydro-generator and grid system stability is increasingly dependent on the hydro-turbine governing system (HTGS). For the HTGS with external disturbances and system uncertainties, an intelligent nonlinear robust controller (INRC) based on state-dynamic-measurement feedback linearization (SDMFL) is proposed to enhance the HTGS’s control performance in this paper. This strategy establishes a dynamic output function including actuator displacement, generator rotor angle, and turbine parameters. The relationship between dynamic output and control input is derived based on this function to establish the corresponding linear state-space model. Then, the mixed H2/H∞ robust controller based on the linear model is designed to suppress the effects of external disturbances and system uncertainties on the system performance. In addition, the controller performance is optimized by levy flight and chaos theory-based gravitational search algorithm (LCGSA), resulting in the INRC. Simulations are performed in MATLAB, and the results are compared with the nonlinear robust controller (NRC) and PID controller. The results show that the proposed controller INRC improves the control performance of nonlinear HTGS with smaller overshoot and shorter adjustment time in all cases.

Optimized Deep Neuro Fuzzy Network for Cyber Forensic Investigation in Big Data-Based IoT Infrastructures

Forensic skills analysts play an imperative support to practice streaming data generated from the IoT networks. However, these sources pose size limitations that create traffic and increase big data assessment. The obtainable solutions have utilized cybercrime detection techniques based on regular pattern deviation. Here, a generalized model is devised considering the MapReduce as a backbone for detecting the cybercrime. The objective of this model is to present an automatic model, which using the misbehavior in IoT device can be manifested, and as a result the attacks exploiting the susceptibility can be exposed by newly devised automatic model. The simulation of IoT is done such that energy constraints are considered as basic part. The routing is done with fractional gravitational search algorithm to transmit the information amongst the nodes. Apart from this, the MapReduce is adapted for cybercrime detection and is done at base station (BS) considering deep neuro fuzzy network (DNFN) for identifying the malwares.

A Compound Coordinated Optimal Operation Strategy of Day-Ahead-Rolling-Realtime in Integrated Energy System

Aiming at the impact of the uncertainty of source load on the optimal scheduling in an integrated energy system (IES), in this paper, based on hybrid resolution modeling and hybrid instruction cycle scheduling technology, three time scales of day-ahead, intra-day rolling and real-time feedback optimization scheduling models are established, respectively, with the objectives of the economic optimal daily operation of the system, the minimum sum of the operation cost of energy purchase and wind curtailment penalty cost in the rolling control time domain, and the minimum adjustment amount of equipment output power. Then, the chaotic gravitational search algorithm (CGSA) is used to solve the problem, and the composite coordination optimization operation strategy of IES with mixed time scales based on CGSA is proposed. In the example, the comparison between the multi-timescale scheduling plan and the actual output, the comparison of the system scheduling results under different strategies and the comparison of different optimization algorithms show that the proposed optimization operation strategy is beneficial to optimize the energy flow distribution, reduce the system operation cost, improve the IES economy and optimization speed.

HYBRID PARTICLE SWARM OPTIMIZATION-GRAVITATIONAL SEARCH ALGORITHMS DEEP LEARNING NETWORKS TO SIMULTANEOUSLY PROJECT MULTIPLE CRUDE OIL PRICE

The conventional linear econometric and statistical models are not effective for forecasting the nonlinear and complex nature of crude oil prices. Computational intelligence techniques and hybrid modelling principles have been proposed to address this issue. Multiple forecasts can be combined using linear or nonlinear methods to create an aggregate forecast. Currently, there is no study on the optimization of deep learning with a hybrid of gravitational search algorithm (GSA) and particle swarm optimization (PSO) for forecasting crude oil price benchmarks, including WTI, Brent, and Dubai. Additionally, most studies have focused only on using West Texas Intermediate (WTI) crude oil spot prices as their benchmark. A Bidirectional Long Short-Term Memory with hybrid gravitational search algorithm (GSA) and particle swarm optimization (PSO) to forecast Crude Oil prices is proposed. The proposed model outperformed FNNPSOGSA, FNNGA, LSTM and BiLSTM models with root mean square errors (RMSE) of 0.0029, 0.0011, and 0.0029, respectively. The proposed model is suited for Crude Oil forecasting.

Optimization Task Scheduling Using Cooperation Search Algorithm for Heterogeneous Cloud Computing Systems

Cloud computing has taken over the high-performance distributed computing area, and it currently provides on-demand services and resource polling over the web. As a result of constantly changing user service demand, the task scheduling problem has emerged as a critical analytical topic in cloud computing. The primary goal of scheduling tasks is to distribute tasks to available processors to construct the shortest possible schedule without breaching precedence restrictions. Assignments and schedules of tasks substantially influence system operation in a heterogeneous multiprocessor system. The diverse processes inside the heuristic-based task scheduling method will result in varying makespan in the heterogeneous computing system. As a result, an intelligent scheduling algorithm should efficiently determine the priority of every subtask based on the resources necessary to lower the makespan. This research introduced a novel efficient scheduling task method in cloud computing systems based on the cooperation search algorithm to tackle an essential task and schedule a heterogeneous cloud computing problem. The basic idea of this method is to use the advantages of meta-heuristic algorithms to get the optimal solution. We assess our algorithm’s performance by running it through three scenarios with varying numbers of tasks. The findings demonstrate that the suggested technique beats existing methods New Genetic Algorithm (NGA), Genetic Algorithm (GA), Whale Optimization Algorithm (WOA), Gravitational Search Algorithm (GSA), and Hybrid Heuristic and Genetic (HHG) by 7.9%, 2.1%, 8.8%, 7.7%, 3.4% respectively according to makespan.

Energy aware Load Balanced Clustering using Golden Eagle Optimization in MS based LR-WPAN

Escalation in the real world applications using Internet of Things (IoT) now demands low cost and minimum power consuming devices having sensing, communicating, and computing capabilities. Low-rate Wireless Personal Area Network (LR-WPAN) is an emerging solution which can provide these capabilities with low data rate and less complexity. The longevity and connectivity of the network is a critical issue during the data collection in LR-WPAN. An Energy aware Load Balanced Clustering (ELBC) approach is proposed in this paper that enhances the network life time by minimizing the energy consumption. Clustering is done using the Golden Eagle Optimization (GEO) and by taking connectivity, Average energy of cluster, balancing factor and cohesion as parameters for optimal cluster head selection. Mobile Sink based data collection strategy using Gravitational Search Algorithm (GSA) is used to collect data from clusters. The simulation is done in network simulator-3 and the result shows that, the performance of ELBC-GEO is better in terms of network lifetime, average energy of nodes, and data collection delay.

Optimized Load Frequency Controller Performances With a Reduced Number of Decision Variables: A Gravitational Search Algorithm-Based Method

Ensuring stability and maintaining performances of the load frequency controller (LFC) are stimulating tasks. A state feedback controller (SFC) with a generalized range of operation satisfying the necessary and sufficient conditions for system stability along with optimized performances through the gravitational search algorithm (GSA) are proposed. The proposed range is dependent on the system parameters. The agent-based systems in the literature consider the dimension of an agent as per the number of decision variables of the problem. The proposed method reduces the number of decision variables to obtain the controller gains and to use in the optimization process. The proposed method is validated through extensive results for different objective functions, contemplating both the steady state and the transient requirements as well as a consideration of different perturbed system parameters and variations in the input disturbances. Results and comparative studies with popular control techniques and optimization algorithms testify that the proposed method maintains system stability and improves the system performances while considering different system parameters, parametric uncertainties, and variations in input disturbances.

A non-linear optimization based robust attribute weighting model for the two-class classification problems.

This article aims to determine the coefficients that will reduce the in-class distance and increase the distance between the classes, collecting the data around the cluster centers with meta-heuristic optimization algorithms, thus increasing the classification performance. The proposed mathematical model is based on simple mathematical calculations, and this model is the fitness function of optimization algorithms. Compared to the methods in the literature, optimizing algorithms to obtain fast results is more accessible. Determining the weights by optimization provides more sensitive results than the dataset structure. In the study, the proposed model was used as the fitness function of the metaheuristic optimization algorithms to determine the weighting coefficients. In this context, four different structures were used to test the independence of the results obtained from the algorithm: the particle swarm algorithm (PSO), the bat algorithm (BAT), the gravitational search algorithm (GSA), and the flower pollination algorithm (FPA). As a result of these processes, a control group from unweighted attributes and four experimental groups from weighted attributes were obtained for each dataset. The classification performance of all datasets to which the weights obtained by the proposed method were applied increased. 100% accuracy rates were obtained in the Iris and Liver Disorders datasets used in the study. From synthetic datasets, from 66.9% (SVM classifier) to 96.4% (GSA Weighting + SVM) in the Full Chain dataset, from 64.6% (LDA classifier) to 80.2% in the Two Spiral datasets (weighted by BA + LDA). As a result of the study, it was seen that the proposed method successfully fulfills the task of moving the attributes to a linear plane in the datasets, especially in classifiers such as SVM and LDA, which have difficulties in non-linear problems, an accuracy rate of 100% was achieved.

An Intelligent Approach for Accurate Prediction of Chronic Diseases

Around the globe, chronic diseases pose a serious hazard to healthcare communities. The majority of the deaths are due to chronic diseases, and it causes burdens across the world. Through analyzing healthcare data and extracting patterns healthcare administrators, victims, and healthcare communities will get an advantage if the diseases are early predicted. The majority of the existing works focused on increasing the accuracy of the techniques but didn’t concentrate on other performance measures. Thus, the proposed work improves the early detection of chronic disease and safeguards the lives of the patients by increasing the specificity and sensitivity of the classifiers along with the accuracy. The proposed work used a hybrid optimization algorithm called the Hybrid Gravitational Search algorithm and Particle Swarm Optimization algorithm (HGSAPSO) to upgrade the detection of chronic diseases. Existing classifier parameters with their optimized parameters are compared and evaluated. Classifiers such as Artificial Neural Network (ANN), Support Vector Machines (SVM), K-Nearest Neighbor (Knn), and Decision tree (DT) are used. Health care data are obtained from the UCI machine learning repository to evaluate the proposed work. The proposed work is assessed on 6 benchmark datasets and the performance metrics such as Accuracy, Specificity, Sensitivity, F-measure, Recall, and Precision are compared. The experimental results exhibit that the proposed work attains better accuracy on Artificial Neural Network-Hybrid Gravitational Search algorithm and Particle Swarm Optimization algorithm (ANN-HGSAPSO) classifier compared to other classifiers. ANN-HGSAPSO provides 93% accuracy for Chronic Kidney Disease (CKD), Cardio Vascular Disease (CVD) 96%, Diabetes 82%, Hepatitis 94%, Wisconsin Breast Cancer (WBC) 91%, and for Liver disease dataset 96%.

Research on the MPPT of Photovoltaic Power Generation Based on the CSA-INC Algorithm

The existing Maximum Power Point Tracking (MPPT) method has low tracking efficiency and poor stability. It is easy to fall into the Local Maximum Power Point (LMPP) in Partial Shading Condition (PSC), resulting in the degradation of output power quality and efficiency. It was found that various bio-inspired MPPT based optimization algorithms employ different mechanisms, and their performance in tracking the Global Maximum Power Point (GMPP) varies. Thus, a Cuckoo search algorithm (CSA) combined with the Incremental conductance Algorithm (INC) is proposed (CSA-INC) is put forward for the MPPT method of photovoltaic power generation. The method can improve the tracking speed by more than 52% compared with the traditional Cuckoo Search Algorithm (CSA), and the results of the study using this algorithm are compared with the popular Particle Swarm Optimization (PSO) and the Gravitational Search Algorithm (GSA). CSA-INC has an average tracking efficiency of 99.99% and an average tracking time of 0.19 s when tracking the GMPP, which improves PV power generation’s efficiency and power quality.

Task Scheduling Optimization in Cloud Computing by Coronavirus Herd Immunity Optimizer Algorithm

Cloud computing is now dominant in high-performance distributed computing, offering resource polling and ondemand services over the web. So, the task scheduling problem in a cloud computing environment becomes a significant analysis space due to the dynamic demand for user services. The primary goal of scheduling tasks is to allocate tasks to processors to achieve the shortest possible makespan while respecting priority restrictions. In heterogeneous multiprocessor systems, task and schedule assignments significantly impact the system's operation. Therefore, the different processes within the heuristic-based scheduling task algorithm will lead to a different makespan on a heterogeneous computing system. Thus, a suitable algorithm for scheduling should set precedence efficiently for every subtask based on the resources required to reduce its makespan. This paper proposes a novel efficient scheduling task algorithm based on the coronavirus herd immunity optimizer algorithm to solve task scheduling problems in a cloud computing environment. The basic idea of this method is to use the advantages of meta-heuristic algorithms to get the optimal solution. We evaluate the performance of our algorithm by applying it to three cases. The collected findings suggest that the proposed strategy successfully achieved the best solution in terms of makespan, speedup, efficiency, and throughput compared to others. Furthermore, the results demonstrate that the suggested technique beats existing methods new genetic algorithm (NGA), proposed particle swarm optimization (PPSO), whale optimization algorithm (WOA), enhanced genetic algorithm for task scheduling (EGA-TS), gravitational search algorithm (GSA), genetic algorithm (GA), and hybrid heuristic and genetic (HHG) by 22.8%, 12.3%, 8.8%, 7.3%, 7.3%, 3.4%, and 3.4% respectively according to makespan.

Al-Biruni Earth Radius (BER) Metaheuristic Search Optimization Algorithm

Metaheuristic optimization algorithms present an effective method for solving several optimization problems from various types of applications and fields. Several metaheuristics and evolutionary optimization algorithms have been emerged recently in the literature and gained widespread attention, such as particle swarm optimization (PSO), whale optimization algorithm (WOA), grey wolf optimization algorithm (GWO), genetic algorithm (GA), and gravitational search algorithm (GSA). According to the literature, no one metaheuristic optimization algorithm can handle all present optimization problems. Hence novel optimization methodologies are still needed. The Al-Biruni earth radius (BER) search optimization algorithm is proposed in this paper. The proposed algorithm was motivated by the behavior of swarm members in achieving their global goals. The search space around local solutions to be explored is determined by Al-Biruni earth radius calculation method. A comparative analysis with existing state-of-the-art optimization algorithms corroborated the findings of BER’s validation and testing against seven mathematical optimization problems. The results show that BER can both explore and avoid local optima. BER has also been tested on an engineering design optimization problem. The results reveal that, in terms of performance and capability, BER outperforms the performance of state-of-the-art metaheuristic optimization algorithms.

SIMULTANEOUS PROJECTION OF WORLD MULTIPLE CRUDE OIL PRICE BENCHMARKS VIA HYBRIDE OF PARTICLE SWARM OPTIMIZATION-GRAVITATIONAL SEARCH ALGORITHM REDESIGNED NEURAL NETWORKS

Conventional methods such as econometrical and statistical models are no longer feasible to handle the nonlinear, non-stationary, chaotic, volatile, and complex nature of crude oil prices due to their linearity nature; computational intelligence techniques were proposed to address these issues. More so, single modelling principle and traditional methods have not been effective enough in forecasting of crude oil prices, hybrid modelling principles have been employed to predict crude oil prices by researchers with better improvements. A group researchers opined that the use of hybrid models can also reduce the risk of choosing an inappropriate model because the use of a single model cannot always accurately forecast the extremely complex crude oil price time-series. A combination method can be applied to multiple forecasts and perform linear or nonlinear combinations of the forecasts, leading to an aggregate forecast. However, no research is found to have used the hybrid of gravitational search algorithm (GSA) and particle swarm optimization (PSO) to train artificial neural network (ANN) for simultaneous prediction of crude oil price benchmarks of WTI, Brent and Dubai. More so, most researchers have dwelled on only West Texas Intermediate (WIT) crude oil spot prices as their benchmark.

A Hybrid Gravitational Emulation Local Search‐Based Algorithm for Task Scheduling in Cloud Computing

The flexibility of cloud computing to provide a dynamic and adaptable infrastructure in the context of information technology and service quality has made it one of the most challenging issues in the computer industry. Task scheduling is a major challenge in cloud computing. Scheduling tasks so that they may be processed by the most effective cloud network resources has been identified as a critical challenge for maximizing cloud computing’s performance. Due to the complexity of the issue and the size of the search space, random search techniques are often used to find a solution. Several algorithms have been offered as possible solutions to this issue. In this study, we employ a combination of the genetic algorithm (GA) and the gravitational emulation local search (GELS) algorithm to overcome the task scheduling issue in cloud computing. GA and the particle swarm optimization (PSO) algorithms are compared to the suggested algorithm to demonstrate its efficacy. The suggested algorithm outperforms the GA and PSO, as shown by the experiments.

Enhanced Gravitational Search Algorithm Based on Improved Convergence Strategy

Enhanced Gravitational Search Algorithm Based on Improved Convergence Strategy

Design of Cross‐Coupled Nonlinear PID Controller Using Single‐Objective Evolutionary Algorithms

The effectiveness of evolutionary algorithms (EAs) such as differential search algorithm (DSA), Real‐Coded genetic algorithm with simulated binary crossover (RGA‐SBX), particle swarm optimization (PSO), and chaotic gravitational search algorithm (CGSA) on the optimal design of cross‐coupled nonlinear PID controllers is compared in this paper. A cross‐coupled multivariable PID controller structure for the binary distillation column was developed with two inputs and two outputs. EA simulations are run to lower IAE using two stopping criteria, namely, maximum number of functional evaluations (Fevalmax) and Fevalmax plus PID parameter and IAE tolerance. Over 20 separate trials were used to compare the performances of several EAs using statistical measures such as best, mean, standard deviation of outcomes, and average calculation time. This article presents the design of a cross‐coupled nonlinear PID controller using single‐objective evolutionary algorithms. Using evolutionary algorithms (EAs) with a multicrossover strategy, the results achieved by various EAs are compared to previously reported results. The results of a multivariable cross‐coupled system clearly show that a single‐objective nonlinear PID controller performs better. Simulations further show that all four techniques evaluated are suitable for PID controller tweaking off‐line. However, only the single‐objective evolutionary algorithms are acceptable for online PID tuning due to their higher consistency and shorter computation time.