Improved Gravitational Search Algorithm Research Articles

An Improved Gravitational Search Algorithm for Task Offloading in a Mobile Edge Computing Network with Task Priority

Mobile edge computing (MEC) distributes computing and storage resources to the edge of the network closer to the user and significantly reduces user task completion latency and system energy consumption. This paper investigates the problem of computation offloading in a three-tier mobile edge computing network composed of multiple users, multiple edge servers, and a cloud server. In this network, each user’s task can be divided into multiple subtasks with serial and parallel priority relationships existing among these subtasks. An optimization model is established with the objective of minimizing the total user delay and processor cost under constraints such as the available resources of users and servers and the interrelationships among the subtasks. An improved gravitational search algorithm (IGSA) is proposed to solve this optimization model. In contrast with the other gravitational search algorithm, the convergence factor is introduced in the calculation of the resultant force and the crossover operation in a genetic algorithm is performed when generating the new particles during each iteration. The simulation results show that the proposed IGSA greatly improves the system performance compared with the existing algorithms.

Fractional-PID and Its Parameter Optimization for Pumped Storage Units Considering the Complicated Conduit System

Speed governing control is significant in ensuring the stable operation of pumped storage units. In this study, a state-space equation mathematical model of the pumped storage governing system considering the complex hydraulic pipeline structure of the pumped storage plant is proposed to describe the system’s dynamic behaviors under small disturbance conditions. Considering the frequent operating condition transitions and the complicated nonlinear dynamic characteristics of the pumped storage units, the fractional-order PID (FOPID) scheme that possesses a higher degree of control freedom than the traditional PID scheme is discussed in detail. To optimize the control parameters of the unit governor, an improved gravitational search algorithm (IGSA) that combines the basic searching mechanisms of the gravitational search algorithm and chaotic search, elastic sphere boundary treatment, and elite guidance strategy is developed. Comparative studies have been carried out under frequency and load disturbance conditions. Simulation results indicate that the control performance of FOPID is better than that of PID under diverse operating conditions and the proposed IGSA has satisfactory parameter optimization capability.

Parameter Optimization in a Leaky Integrator Echo State Network with an Improved Gravitational Search Algorithm

In the prediction of a nonlinear time series based on a leaky integrator echo state network (leaky-ESN), building a reservoir related to the specific problem is a key step. For problems such as poor performance of randomly generated reservoirs, it is tough to determine the parameter values of the reservoirs. The work in this paper uses the gravitational search algorithm (GSA) to optimize the global parameters of a leaky-ESN, such as the leaking rate, the spectral radius, and the input scaling factor. The basic GSA has some problems, such as slow convergence and poor balance between exploration and exploitation, and it cannot solve some complex optimization problems well. To solve these problems, an improved gravitational search algorithm (IGSA) is proposed in this paper. First, the best agent and elite agents were archived and utilized to accelerate the exploration phase and improve the convergence rate in the exploitation phase. Second, to improve the effect of the poor fitness agents on the optimization result, a differential mutation strategy was proposed, which generated new individuals to replace original agents with worse fitness, increasing the diversity of the population and improving the global optimization ability of the algorithm. Finally, two simulation experiments showed that the leaky-ESN optimized by the IGSA had better prediction accuracy.

Humanoid robot path planning using memory-based gravity search algorithm and enhanced differential evolution approach in a complex environment

The present work focuses on the optimal path planning of humanoid robots in a rugged terrain using a hybrid-based improved gravitational search algorithm (IGSA) tuned with a differentially perturbed velocity (DV) approach. The primary IGSA suffers from the disadvantage of a lower convergence rate and the risk of getting trapped in optimal local conditions. The drawbacks are eliminated by employing a hybrid IGSA-DV path planning approach, which improves the memory and velocity updating scheme. The algorithm is designed to minimize the overall path length of the humanoid, from source to goal, in the minimal time possible. The humanoids, during their locomotion, coordinate with each other to avoid collisions in their journey. The robots make decisions based on the position of the various obstacles within the search space. So, path smoothness is also considered to ensure stability during the locomotion. The work is further focussed on optimizing the energy efficiency of the different joints of the humanoid while walking on even and uneven surfaces. The path planning is performed in real-world and simulation environments, and the results are then compared with the different existing, individual, and hybrid techniques. The comparison of the above approach revealed that the IGSA-DV algorithm showed an optimal outcome in terms of path length and time taken. Moreover, the percentage deviation in the path length and time in both the simulation and experimental environment was within 6%. The Petri-Net approach is implemented along with the proposed technique to avoid confusion among the robots during multiple robot navigations.

Fast Frequency Regulation Method for Power System With Two-Stage Photovoltaic Plants

The full utilization of solar energy is of great significance in reducing carbon emissions and alleviating environmental problems. Fast frequency regulation plays an important role in the power system with grid-connected two-stage photovoltaic (PV) plants. The presented fast frequency regulation method is composed of droop control, virtual inertia control and de-loading control. This work focuses on improving droop control and virtual inertia control for PV plants connected to grid. Due to non-Gaussian disturbance induced by solar irradiance, an adaptive control strategy is proposed by minimizing an improved performance index which combines the mean square error (MSE) and the minimum error entropy (MEE) criteria. Based on the collected frequency deviation series data, the entropy of the frequency deviation is estimated with the aid of a quantizer. The droop control coefficient and inertia gain are then solved by an improved gravitational search algorithm (IGSA). Finally, the effectiveness of the proposed strategy is testified by an illustrative power system with a two-stage grid-connected PV plant.

Optimized economic operation of energy storage integration using improved gravitational search algorithm and dual stage optimization

This paper presents optimized energy storage and distribution network model under uncertainty of the power generation problem, based on an improved gravitational search algorithm (IGSA). Throughout the search phase, the IGSA uses a trial-and-error strategy to update the best agent. It also modifies the orbit of the poor agent in the late stages of the search and uses the coordinate descent approach to search for the optimal agent's position. A dual-stage optimization (DSO) model is employed for energy storage and uncertainty of energy generation. The hybrid IGSA-DSO used an adoptive speed inertia coefficient which ensures global and local searchability. The features of the proposed model were validated using IEEE-RTS 24 bus test system with various cases to realize the coordinated planning of the power grid and energy storage. Finally, the simulation shows interesting results of the proposed approach (IGSA-DSO), which reduces the investment cost by 0.248% comparatively.

High-Speed Train Platoon Dynamic Interval Optimization Based on Resilience Adjustment Strategy

Resilience adjustment refers to the generation of a control strategy by evaluating the interaction between related factors. Tracking intervals of the high-speed train platoon change dynamically, which directly influences the operation safety and efficiency, and constrains the train operation trajectories. In China, the tracking interval is getting shorter. To ensure safety and improve efficiency, we research a dynamic interval resilience adjustment strategy based on the moving block system. Firstly, the optimal offline operation strategy is obtained by solving the multi-objective optimization model with the improved gravitational search algorithm (I-GSA). The resilience adjustment mechanism is developed to evaluate the tracking interval and choose the appropriate driving strategy to adjust operation states based on the resilience tracking interval model. Then, we study the relation between operation strategy and departure interval, and a seeker optimization algorithm (SOA) is used to obtain the optimal departure intervals and driving strategies. Simulations are conducted based on the sections between Chibi North station and Changsha South station in Wuhan-Guangzhou high-speed railway. The results indicate that the total operation time decreased by 191s and the operation safety can be ensured at any time.

A Novel Shearer Cutting State Recognition Method Based on Improved Variational Mode Decomposition and LSSVM with Acoustic Signals

The recognition of shearer cutting state is the key technology to realize the intelligent control of the shearer, which has become a highly difficult subject concerned by the world. This paper takes the sound signal as analytic objects and proposes a novel recognition method based on the combination of variational mode decomposition (VMD), principal component analysis method (PCA), and least square support vector machine (LSSVM). VMD can decompose a signal into various modes by using calculus of variation and effectively avoid the false component and mode mixing problems. On this basis, an improved gravitational search algorithm (IGSA) is designed by using the position update mechanism of Levy flight strategy to find the optimal parameter combination of VMD. Then, the feature extraction is achieved by calculating the envelope entropy and kurtosis of the decomposed intrinsic mode functions (IMFs). To avoid dimensional disasters and reinforce the classification performance, PCA is introduced to choose useful features, and the LSSVM-based classifier is reasonably constructed. Finally, the experimental results indicate that the proposed method is more feasible and superior in the recognition of shearer cutting states.

Impact of cost–benefit analysis on financial benefit evaluation of investment projects under back propagation neural network

In order to realize the financial benefit evaluation of the investment project, the investment project of power transmission and transformation of a power grid enterprise in Sichuan province is taken as an example. First, the overall cost of the investment project is analyzed and introduced, and the sales revenue, running costs, taxes and surcharges in the four years from 2016 to 2019 are calculated, and the financial benefit evaluation index and calculation method are determined. Then, the Fuzzy optimal back propagation neural network (BPNN) is constructed based on BPNN and fuzzy optimization strategy for cost–benefit analysis. Then an improved Gravitational search algorithm (IGSA) algorithm based on GSA algorithm is proposed applied to the optimization of fuzzy optimal BPNN. Finally, the influence of some uncertainty factors on the financial benefit of investment project is measured through break-even analysis and sensitivity analysis. The results show that the operation index of the substation is stable and the electricity sold shows an increasing trend after its operation. The financial situation of the investment project is optimistic with high economic benefits. The internal rate of return of the capital can reach 17.13%, and the payback period of the investment is less than 7 years. The sensitivity of the internal rate of return of project investment from high to low is unit price of electricity sold, electricity sold and operation cost. The output value of the samples trained by the fuzzy optimal BPNN is close to the expected value, with a small error of about 0.3% at most, and the training speed is fast and the training effect is good. The actual output result of the financial evaluation index is basically consistent with the expected output result of the expert survey, with a small error. Moreover, the application of the model to other similar investment projects still has good financial benefit evaluation results and good generalization ability. In conclusion, BPNN can be used to calculate the cost and benefit and predict the financial benefit of investment projects. According to the forecast results, a feasible investment scheme is proposed, which can be used for reference in the financial benefit evaluation of similar investment projects.

An improved gravitational search algorithm to the hybrid flowshop with unrelated parallel machines scheduling problem

ABSTRACT The hybrid flowshop scheduling problem with unrelated parallel machines exists in many industrial manufacturers, which is an NP-hard combinatorial optimisation problem. To solve this problem more effectively, an improved gravitational search (IGS) algorithm is proposed which combines three strategies: generate new individuals using the mutation strategy of the standard differential evolution (DE) algorithm and preserve the optimal solution via a greedy strategy; substitute the exponential gravitational constant of the standard gravitational search (GS) algorithm with a linear function; improve the velocity update formula of the standard GS algorithm by mixing an adaptive weight and the global search strategy of the standard particle swarm optimisation (PSO) algorithm. Benchmark examples are solved to demonstrate the proposed IGS algorithm is superior to the standard genetic algorithm, DE, GS, DE with local search, estimation of distribution algorithm and artificial bee colony algorithms. Two more examples from a real-world water-meter manufacturing enterprise are effectively solved.

Partial disassembly line balancing under uncertainty: robust optimisation models and an improved migrating birds optimisation algorithm

A partial disassembly line balancing problem under uncertainty is studied in this paper, which concerns the allocation of a sequence of tasks to workstations such that the overall profit is maximised. We consider the processing time uncertainty and develop robust solutions to accommodate it. The problem is formulated as a non-linear robust integer program, which is then converted into an equivalent linear program. Due to the intractability of such problems, the exact algorithms are only applicable to small-scale instances. We develop an improved migrating birds optimisation algorithm. Two enhancement techniques are proposed. The first one finds the optimal number of tasks to be performed for each sequence rather than random selection used in the literature; while the second one exploits the specific problem structure to construct effective neighbourhoods. The numerical results show the strong performance of our proposal compared to CPLEX and the improved gravitational search algorithm (IGSA), especially for large-scale problems. Moreover, the enhancement due to the proposed techniques is obvious across all instances considered.

Automated Built-Up Extraction Index: A New Technique for Mapping Surface Built-Up Areas Using LANDSAT 8 OLI Imagery

Accurate built-up area extraction is one of the most critical issues in land-cover classification. In previous studies, various techniques have been developed for built-up area extraction using Landsat images. However, the efficiency of these techniques under different technical and geographical conditions, especially for bare and sandy areas, is not optimal. One of the main challenges of built-up area extraction techniques is to determine an optimum and stable threshold with the highest possible accuracy. In many of these techniques, the optimum threshold value fluctuates substantially in different parts of the image scene. The purpose of this study is to provide a new index to improve built-up area extraction with a stable optimum threshold for different environments. In this study, the developed Automated Built-up Extraction Index (ABEI) is presented to improve the classification accuracy in areas containing bare and sandy surfaces. To develop and evaluate the accuracy of the new method for built-up area extraction with Landsat 8 OLI reflective bands, five test sites located in the Iranian cities (Babol, Naqadeh, Kashmar, Bam and Masjed Soleyman), eleven European cities (Athens, Brussels, Bucharest, Budapest, Ciechanow, Hamburg, Lyon, Madrid, Riga, Rome and Porto) and high resolution layer imperviousness (HRLI) data were used. Each site has varying environmental and complex surface coverage conditions. To determine the optimal weights for each of the Landsat 8 OLI reflective bands, the pure pixel sets for different classes and the improved gravitational search algorithm (IGSA) optimization were used. The Kappa coefficient and overall error were calculated to evaluate the accuracy of the built-up extraction map. Additionally, the ABEI performance was compared with the urban index (UI) and normalized difference built-up index (NDBI) performances. In each of the five test sites and eleven cities, the extraction accuracy of the built-up areas using the ABEI was higher than that using the UI, and NDBI (P-value of 0.01). The relative standard deviations of the optimal threshold values for the ABEI and UI were 27 and 155% (at five test sites) and were 16 and 37% (at eleven European cities), respectively, which indicates the stability of the ABEI threshold value when the location and environmental conditions change. The results of this study demonstrated that the ABEI can be used to extract built-up areas from other land covers. This index is effective even in bare soil and sandy areas, where other indices experience major challenges.

Forecasting reservoir monthly runoff via ensemble empirical mode decomposition and extreme learning machine optimized by an improved gravitational search algorithm

Monthly streamflow prediction plays a significant role in reservoir operation and water resource management. Hence, this research tries to develop a hybrid model for accurate monthly streamflow prediction, where the ensemble empirical mode decomposition (EEMD) is firstly used to decompose the original streamflow data into a finite amount of intrinsic mode functions (IMFs) and a residue; and then the extreme learning machine (ELM) is employed to forecast each IMFs and the residue, while an improved gravitational search algorithm (IGSA) based on elitist-guide evolution strategies, selection operator and mutation operator is used to select the parameters of all the ELM models; finally, the summarized predicated results for all the subcomponents are treated as the final forecasting result. The hybrid method is applied to forecast the monthly runoff of Three Gorges in China, while four quantitative indexes are used to test the performances of the developed forecasting models. The results show that EEMD can effectively separate the internal characteristics of the original monthly runoff, and the hybrid model is able to make an obvious improvement over other models in hydrological time series prediction.

Forecasting of Coal Demand in China Based on Support Vector Machine Optimized by the Improved Gravitational Search Algorithm

The main target of the energy revolution in the new period is coal, but the proportion of coal in primary energy consumption will gradually decrease. As coal is a major producer and consumer of energy, analyzing the trend of coal demand in the future is of great significance for formulating the policy of coal development planning and driving the revolution of energy sources in China. In order to predict coal demand scientifically and accurately, firstly, the index system of influencing factors of coal demand was constructed, and the grey relational analysis method was used to select key indicators as input variables of the model. Then, the kernel function of SVM (support vector machine) was optimized by taking advantage of the fast convergence speed of GSA (gravitational search algorithm), and the memory function and boundary mutation strategy of PSO (particle swarm optimization) were introduced to improve the gravitational search algorithm, and the improved GSA (IGSA)–SVM prediction model was obtained. After that, the effectiveness of IGSA–SVM in predicting coal demand was further proven through empirical and comparative analysis. Finally, IGSA–SVM was used to forecast China’s coal demand in 2018–2025. According to the forecasting results, relevant suggestions about coal supply, consumption, and transformation are put forward, providing scientific basis for formulating an energy development strategy.

Homogeneity Distance Classification Algorithm (HDCA): A Novel Algorithm for Satellite Image Classification

Image classification is one of the most common methods of information extraction from satellite images. In this paper, a novel algorithm for image classification based on gravity theory was developed, which was called “homogeneity distance classification algorithm (HDCA)”. The proposed HDCA used texture and spectral information for classifying images in two iterative supplementary computing stages: (1) merging, (2) traveling and escaping operators. The HDCA was equipped by a new concept of distance, the weighted Manhattan distance (WMD). Moreover, an improved gravitational search algorithm (IGSA) was applied for selecting features and determining optimal feature space scale in HDCA. In the case of multispectral satellite image classification, the proposed method was compared with two well-known classification methods, Maximum Likelihood classifier (MLC) and Support Vector Machine (SVM). The results of the comparison indicated that overall accuracy values for HDCA, MLC, and SVM are 95.99, 93.15, and 95.00, respectively. Furthermore, the proposed HDCA method was also used for classifying hyperspectral reference datasets (Indian Pines, Salinas and Salinas-A scene). The classification results indicated substantial improvement over previous algorithms and studies by 2% in Indian Pines dataset, 0.7% in the Salinas dataset and 1.2% in the Salinas-A scene. These experimental results demonstrate that the proposed algorithm can classify both multispectral and hyperspectral remote sensing images with reliable accuracy because this algorithm uses the WMD in the classification process and the IGSA to select automatically optimal features for image classification based on spectral and texture information.

A novel modeling approach and its application in polymer quality index prediction

Accurate and reliable quality index prediction is indispensable in quality control of the industrial propylene polymerization (PP) processes. This paper presents a novel modeling approach for quality index prediction based on optimal fuzzy wavelet neural network (FWNN) with improved gravitational search algorithm (IGSA), where the constant or a linear function of inputs in conclusion part of traditional TSK fuzzy model is replaced with wavelet neural network (WNN). Then, an online learning algorithm of the FWNN model is derived by using gradient descent algorithm, and an IGSA algorithm is proposed to online adapt the learning rates of FWNN. Research on the proposed soft sensor is carried out with the data from a real industrial PP plant, and the results are compared among the WNN, FWNN and IGSA-FWNN models. The research results show that the proposed prediction model achieves a good performance in practical industrial quality index, melt index, prediction process.

Optimal Placement and Sizing of Photovoltaic Based Distributed Generation Considering Costs of Operation Planning of Monocrystalline and Thin-Film Technologies

Distributed generation (DG) technology has been growing rapidly in industries as this technology can increase the overall efficiency to the power systems. Improper placement and sizing can lead to power losses and interrupt the voltage profile of distribution systems. Studies have been done to solve the DG placement and sizing problem considering several factors, and one of the common factor is minimizing the power losses. However, it is not adequate by only considering the power losses, whereas, the costs of the generation, investment, maintenance, and losses of the distribution system must be taken in consideration. In this research, DG chosen to study is photovoltaic (PV) type which is monocrystalline and thin-film. Costs of operation planning with respect to the power losses is considered which include the costs of investment, maintenance, power loss, and generation that are determined for optimal placement and sizing of DG. The proposed method improved gravitational search algorithm (IGSA) is used in the matlab environment to find the optimal placement and sizing of DG and is tested with the IEEE 34-bus system. The performance of IGSA is then compared with gravitational search algorithm (GSA) and particle swarm optimization (PSO) to find out which algorithm gives the best fitness value and convergence rate. The purpose of this research is to identify the operation planning cost based on the optimization results and improves the optimal placement and sizing of DG in future, to provide maximum economical, technical, environmental benefits, and increase the overall efficiency to the power system.

Adaptive jammer localization in wireless networks

The shared nature of wireless communication, the open access to wireless medium, the tightly coupled design principle of wireless network protocol and the scarcity of radio spectrum make wireless networks vulnerable to different types of jammers, which prevents the normal communication among legitimate nodes by occupying wireless channel or corrupting network protocols’ working process and poses a serious threat to the stability and security of the network. To tackle this security threat, a few anti-jamming schemes have been put forward including channel hopping, spatial retreat, etc. Among these strategies, jammer localization has attracted much attention in recent years since it is very helpful for jamming-avoidance routing and even jammer elimination. However, existing algorithms mainly focus on the localization of jammers equipped with omnidirectional antennas and fail to handle directional jammers, i.e., those attackers with directional antennas. In order to bridge this gap, this paper puts forward an Adaptive Jammer Localization Algorithm (AJLA) which can locating both types of jammers. At first, an identification method is employed to estimate the jammer’s antenna type. If a omnidirectional antenna is adopted, typical existing algorithm, such as Centroid Localization (CL), Virtual Force Iteration Localization (VFIL), can be utilized to locate the jammer. Otherwise, an Improved Gravitational Search Algorithm (IGSA) is developed for directional jammer localization. A series of simulation experiments have been conducted to evaluate the performance of the proposed method. Experimental results show that AJLA-IGSA could locate the directional jammer efficiently and accurately. Besides, the complexity of information collection and localization process are also analyzed by experiments.

A VVWBO-BVO-based GM (1,1) and its parameter optimization by GRA-IGSA integration algorithm for annual power load forecasting.

Annual power load forecasting is not only the premise of formulating reasonable macro power planning, but also an important guarantee for the safety and economic operation of power system. In view of the characteristics of annual power load forecasting, the grey model of GM (1,1) are widely applied. Introducing buffer operator into GM (1,1) to pre-process the historical annual power load data is an approach to improve the forecasting accuracy. To solve the problem of nonadjustable action intensity of traditional weakening buffer operator, variable-weight weakening buffer operator (VWWBO) and background value optimization (BVO) are used to dynamically pre-process the historical annual power load data and a VWWBO-BVO-based GM (1,1) is proposed. To find the optimal value of variable-weight buffer coefficient and background value weight generating coefficient of the proposed model, grey relational analysis (GRA) and improved gravitational search algorithm (IGSA) are integrated and a GRA-IGSA integration algorithm is constructed aiming to maximize the grey relativity between simulating value sequence and actual value sequence. By the adjustable action intensity of buffer operator, the proposed model optimized by GRA-IGSA integration algorithm can obtain a better forecasting accuracy which is demonstrated by the case studies and can provide an optimized solution for annual power load forecasting.

Spectrum Allocation Based on an Improved Gravitational Search Algorithm

In cognitive radio networks (CRNs), improving system utility and ensuring system fairness are two important issues. In this paper, we propose a spectrum allocation model to construct CRNs based on graph coloring theory, which contains three classes of matrices: available matrix, utility matrix, and interference matrix. Based on the model, we formulate a system objective function by jointly considering two features: system utility and system fairness. Based on the proposed model and the objective problem, we develop an improved gravitational search algorithm (IGSA) from two aspects: first, we introduce the pattern search algorithm (PSA) to improve the global optimization ability of the original gravitational search algorithm (GSA); second, we design the Chebyshev chaotic sequences to enhance the convergence speed and precision of the algorithm. Simulation results demonstrate that the proposed algorithm achieves better performance than traditional methods in spectrum allocation.