Gravitational Search Algorithm Research Articles

Electrical line fault prediction using a novel grey wolf optimization algorithm based on multilayer perceptron

AbstractGrey wolf optimization algorithm (GWO) has achieved great results in the optimization of neural network parameters. However, it has some problems such as insufficient precision, poor robustness, weak searching ability and easy to fall into local optimal solution. Therefore, a grey wolf optimization algorithm combining Levy flight and nonlinear inertia weights (LGWO) is proposed in this paper. The combination of Levy flight and nonlinear inertia weight is to improve the search efficiency and solve the problem that the search ability is weak and it is easy to fall into the local optimal solution. In summary, LGWO solves the problems of insufficient precision, poor robustness, weak searching ability and easy to fall into local optimal. This paper uses Congress on Evolutionary Computation benchmark function and combines algorithms with neural network for power line fault classification prediction to verify the effectiveness of each strategy improvement in LGWO and its comparison with other excellent algorithms (sine cosine algorithm, tree seed algorithm, wind driven optimization, and gravitational search algorithm). In the combination of neural networks and optimization algorithms, the accuracy of LGWO has been improved compared to the basic GWO, and LGWO has achieved the best performance in multiple algorithm comparisons.

Optimal planning for distribution networks considering system uncertainties using pseudo-inspired gravitational search algorithm

Optimal planning for distribution networks considering system uncertainties using pseudo-inspired gravitational search algorithm

Enhancing forest optimization algorithm with gravitational search for nonlinear continuous optimization

Meta-heuristic algorithms have great role in solving problems related to optimization. Meta-heuristic method cannot solve problems related to optimization due to No Free Lunch theory. Hence different optimization methods are proposed by various researchers each year in order to solve optimization problems. Forest Optimization Algorithm (FOA) is an evolutionary optimization algorithm that is appropriate for continuous nonlinear optimization problems. The algorithm drawbacks include entrapment in local optimum and failure in achieving global optimum. The paper proposes hybrid algorithm called FOAGSA, in which the Gravitational Search Algorithm (GSA) is employed to improve the FOA performance in order to solve nonlinear continuous problems. The FOAGSA was evaluated through 39 benchmark optimization functions and two engineering problems. The experimental results proved that the FOAGSA exhibited acceptable results compared to state-of-art and well-known Meta-heuristic algorithms. Friedman ranking algorithm was utilized to compare FOAGSA with existing methods. The FOAGSA was ranked first on that basis.

Deep transfer learning with gravitational search algorithm for enhanced plant disease classification

Plant diseases annually cause damage and loss of much of the crop, if not its complete destruction, and this constitutes a significant challenge for farm owners, governments, and consumers alike. Therefore, identifying and classifying diseases at an early stage is very important in order to sustain local and global food security. In this research, we designed a new method to identify plant diseases by combining transfer learning and Gravitational Search Algorithm (GSA). Two state-of-the-art pretrained models have been adopted for extracting features in this study, which are MobileNetV2 and ResNe50V2. Multilayer feature extraction is applied in this study to ensure representations of plant leaves from different levels of abstraction for precise classification. These features are then concatenated and passed to GSA for optimizing them. Finally, optimized features are passed to Multinomial Logistic Regression (MLR) for final classification. This integration is essential for categorizing 18 different types of infected and healthy leaf samples. The performance of our approach is strengthened by a comparative analysis that incorporates features optimized by the Genetic Algorithm (GA). Additionally, the MLR algorithm is contrasted with K-Nearest Neighbors (KNN). The empirical findings indicate that our model, which has been refined using GSA, achieves very high levels of precision. Specifically, the average precision for MLR is 99.2%, while for KNN it is 98.6%. The resulting results significantly exceed those achieved with GA-optimized features, thereby highlighting the superiority of our suggested strategy. One important result of our study is that we were able to decrease the number of features by more than 50%. This reduction greatly reduces the processing requirements without sacrificing the quality of the diagnosis. This work presents a robust and efficient approach to the early detection of plant diseases. The work demonstrates the utilization of sophisticated computational methods in agriculture, enabling the development of novel data-driven strategies for plant health management, therefore enhancing worldwide food security.

Photovoltaic module performance: Modeling, parameter estimation, and environmental effects

This research addresses the pressing need for clean energy solutions by focusing on the increasing adoption of photovoltaic (PV) modules as alternatives to fossil fuel-based energy sources. Despite their promise, PV modules face challenges in maintaining efficiency under varying environmental conditions. To tackle this, the study develops a one-diode model of PV modules using the Gravitational Search Algorithm (GSA) to obtain the optimal PV parameters so that the performance of the PV modules across diverse weather conditions can be obtained. The integration of a microcontroller and sensors in the experimental setup allows for real-time monitoring of critical environmental variables such as irradiance, temperature, and humidity, alongside voltage and current measurements. While there are certain constraints, such as the sensitivity of sensor data to weather conditions and the significant dependency of estimated parameters on measurements. However, by analyzing the performance of three specific PV modules SOLTECH215, PHOTOWATT220, and KC200GT under varying conditions, this research provides invaluable insights into optimizing energy production and efficiency in practical applications. By bridging theoretical modeling with experimental validation, it lays the groundwork for more efficient and reliable solar energy systems, thus driving the transition towards a cleaner and more sustainable energy future.

Reconfiguration of distributed power distribution networks based on improved gravitational search algorithms

In order to solve the problem of weak global search ability and premature convergence of GSA, a hybrid algorithm combining GSA and PSO is proposed. This algorithm can effectively use the advantages of PSO in global search. In addition, the update strategy of the gravitational constant is optimized, which improves the early search ability of the algorithm. Taking the reduction of network loss as the objective function, the improved algorithm is used to reconstruct the distribution network with distributed generation. The simulation results of the IEEE33 system show that this algorithm can efficiently decrease the network loss and enhance the node voltage.

Optimization for cost-effective design of water distribution networks: a comprehensive learning approach

AbstractThe Comprehensive Learning Gravitational Search Algorithm (CLGSA) has demonstrated its effectiveness in solving continuous optimization problems. In this research, we extended the CLGSA to tackle NP-hard combinatorial problems and introduced the Discrete Comprehensive Learning Gravitational Search Algorithm (D-CLGSA). The D-CLGSA framework incorporated a refined position and velocity update scheme tailored for discrete problems. To evaluate the algorithm's efficiency, we conducted two sets of experiments. Firstly, we assessed its performance on a diverse range of 24 benchmarks encompassing unimodal, multimodal, composite, and special discrete functions. Secondly, we applied the D-CLGSA to a practical optimization problem involving water distribution network planning and management. The D-CLGSA model was coupled with the hydraulic simulation solver EPANET to identify the optimal design for the water distribution network, aiming for cost-effectiveness. We evaluated the model's performance on six distribution networks, namely Two-loop network, Hanoi network, New-York City network, GoYang network, BakRyun network, and Balerma network. The results of our study were promising, surpassing previous studies in the field. Consequently, the D-CLGSA model holds great potential as an optimizer for economically and reliably planning and managing water networks.

An adaptive fault detection model based on variational auto-encoders and unsupervised transfer learning

Aiming at the problem of insufficient generalization of fault detection in traditional machine learning, an SDN controller fault detection method based on unsupervised transfer learning is proposed. The method mainly includes two parts. (1) A Gaussian mixture variational autoencoder based on the autoregressive flow is proposed. First, the encoder and decoder of variational autocoding are improved with gated recurrent units, and the improved variational autocoding can process time series data. Secondly, the gated recurrent unit is improved by using the gravitational search algorithm, which speeds up the search of the weight of the gated recurrent unit. Further, considering that the latent space of the variational autoencoder is a single Gaussian distribution, and the complex data in reality is often too simple to be represented by a single Gaussian distribution. (2) Aiming at the problem of poor generalization of fault detection models in practical scenarios, a domain adaptive fault detection algorithm based on multi-kernel maximum mean difference and intra-class distance constraints is proposed. Map the features into the manifold space to eliminate the distortion of the features in the original space. After mapping, the distance between fields needs to be measured, and the maximum mean difference of a single kernel cannot determine which kernel function is more suitable for the current task in practical applications. Therefore, the maximum mean difference based on multi-core is introduced to measure between the two fields. The experimental results show that the algorithm proposed improves the accuracy about 5% compared with the previous algorithm.

Controlled random initialization of search vectors for application of gravitational search algorithm to selective harmonic elimination PWM

The prime objective of SHE PWM is to eliminate selected lower order harmonic by solving a set of non-linear transcendental equation while maintaining the fundamental component at a desired satisfactory value. In order to solve these non-linear equations different metaheuristic optimization techniques has been used in recent past. Due to faster convergence rate increased precision and higher probability to calculus based mathematical approximation techniques. In this manuscript an improved initialized Gravitational search algorithm has been proposed for a uni-polar 3-phase inverter. The algorithm is improved upon through a controlled random initialization of search agent, specifically for the purpose of solving the set of non-linear transcendental equation in SHEPWM for 3- switching angles. The simulation of the gravitational search algorithm and the proposed improved initialized GSA has been performed in MATLAB software and comparison of different performance parameter like Harmonic distortion, convergence rate etc. has been studied. The simulation results proves that a considerable improvement has been achieved by applying improved initialized GSA algorithm in terms of harmonic mitigation, convergence rate and accuracy in finding out the appropriate switching angles. Finally, the results obtained in simulation are verified through an appropriate experimental setup.

A novel federated learning aggregation algorithm for AIoT intrusion detection

AbstractNowadays, the development of Artificial Intelligence of Things (AIoT) is advancing rapidly, and intelligent devices are increasingly exposed to more security risks on the network. Deep learning‐based intrusion detection is an effective security defence approach. Federated learning (FL) is capable of enabling deep learning models to be trained on local clients without uploading their data to a central server. This paper proposes a novel federated learning aggregation algorithm called fed‐dynamic gravitational search algorithm (Fed‐DGSA), which incorporates the GSA algorithm to optimize the weight updating process of FL local models. During the updating process, the decay rate of the gravity coefficient is optimized and random perturbations and dynamic weights are introduced to ensure a more stable and efficient FL aggregation process. The experimental results show that the detection accuracy of Fed‐DGSA has reached about 97.8%, and it is demonstrated that the model trained using Fed‐DGSA achieves higher accuracy compared to Fed‐Avg.

An optimal multipath routing protocol using hybrid gravitational search particle swarm optimization for secure communication

SummaryCommunicating large amounts of data requires an encryption solution that ensures fast and secure data transfer. Unfortunately, it faced privacy and security issues, resulting in higher power consumption, data scarcity, energy, diversity, data reliability, low security, and packet loss in the communication process. The goal of this research is to ensure data transfer while conserving energy and protecting data transmission in WSNs. Therefore, this paper proposes a multipath routing protocol using the HGSPSO algorithm, which is a combination of hybrid gravitational search algorithm (HGSA) and particle swarm optimization (PSO), to securely transmit data from one node to another node under public and private key cryptography. The PSO approach is utilized to resolve the energy hole problem by selecting the cluster head in the sink coverage area. The RSA can be employed for data encryption, data decryption, key generation, and sharing. The metrics like acceleration constants and inertial weights are used for the HGSPSO algorithm. The result of this implementation demonstrated that the scheme had a low energy consumption of 36%, a maximum packet delivery ratio (PDR) of 96%, a higher throughput rate of 88%, and a low packet loss of 39%.

Fixed and mobile energy storage coordination optimization method for enhancing photovoltaic integration capacity considering voltage offset

Mobile energy storage has the characteristics of strong flexibility, wide application, etc., with fixed energy storage can effectively deal with the future large-scale photovoltaic as well as electric vehicles and other fluctuating load access to the grid resulting in the imbalance of supply and demand. To this end, this paper proposes a coordinated two-layer optimization strategy for fixed and mobile energy storage that takes into account voltage offsets, in the context of improving the demand for local PV consumption. Among them, the upper layer optimization model takes into account the minimum operating cost of fixed and mobile energy storage, and the lower layer optimization model minimizes the voltage offset through the 24-h optimal scheduling of fixed and mobile energy storage in order to improve the in-situ PV consumption capacity. In addition, considering the multidimensional nonlinear characteristics of the model, the interaction force of particles in the Universe is introduced, and the hybrid particle swarm-gravitational search algorithm (PSO-GSA) is proposed to solve the model, which is a combination of the individual optimization of the particle swarm algorithm and the local search capability of the gravitational search algorithm, which improves the algorithm’s optimization accuracy. Finally, the feasibility and effectiveness of the proposed model and method are verified by simulation analysis with IEEE 33 nodes.

MAR-GSA: Mixed attraction and repulsion based gravitational search algorithm

As a population-based stochastic optimization algorithm, Gravitational Search Algorithm (GSA) has attracted numerous interests and has been applied in various applications. However, GSA has drawbacks such as uneven search and premature convergence in practical applications. This paper specifically explains the inherent characteristic of GSA in prioritizing the center position. Correspondingly, an improvement strategy of fitness normalization with mass shift is proposed, creating a situation where gravity and repulsion are mixed. Then, the global best mechanism with weights is incorporated into the particle's velocity update formula, which compensates for the difficulties in the later exploitation stage. Finally, an empirical formula for the initial gravitational constant related to the size of the solution space is proposed, which enhances the global search ability together with the former strategy. 12 shifted benchmark functions are used to construct 20 optimization problems ranging from 2 to 120 dimensions. The average performance of the proposed algorithm, other GSA and well-known algorithms are compared under the same budget. The results demonstrate that the proposed GSA not only effectively addresses the drawbacks of GSA and maintains good performance, but also exhibits strong competitiveness compared to various similar algorithms.

Star-Quake: A New Operator in Multi-Objective Gravitational Search Algorithm for Task Scheduling in IoT-Based Cloud–Fog Computing System

Star-Quake: A New Operator in Multi-Objective Gravitational Search Algorithm for Task Scheduling in IoT-Based Cloud–Fog Computing System

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.

Application of dynamic event-triggered hybrid gravitational search algorithm in pipeline leakage detection

In this paper, a hybrid gravitational search algorithm (GSA) based on the dynamic event-triggered mechanism, which is called DHGSA, is proposed to alleviate the slow exploitation problem of the GSA that may result in premature convergence and falling into local optimization. First, the DHGSA is divided into the exploration stage and the exploitation stage through the introduction of the population diversity. Then, the memory and social information exchange abilities of the particle swarm optimization algorithm are added in the DHGSA’s exploration stage, which can accelerate the convergence speed of the GSA. Next, the dynamic event-triggered mechanism (DETM) is added in the exploitation stage of the DHGSA, and a “celestial banishment strategy” is proposed, which improves the exploitation ability of the GSA and makes it have the ability to avoid falling into local optimal value. The derived results indicate that, compared with several other well-known search algorithms, the DHGSA has better performance on convergence speed, optimization accuracy and stability. Finally, the DHGSA is employed to optimize the parameters of the variational mode decomposition (VMD) algorithm for the oil and gas pipeline signal decomposition. The signal-to-noise ratio (SNR) of the three types of pipeline signals after denoising reaches 8.8406 dB, 9.0869 dB and 5.6880 dB, respectively. It is proved that the leakage signals can be denoised effectively in the research of the oil and gas pipeline signal processing.

User Interest Identification with Social Media Information using Natural Language and Meta-Heuristic Technique

As the number of Internet users and social networking apps has grown in recent years, interest-based recommendation systems have been more commonly used in practice. Given the vast quantity of data available from LinkedIn and Twitter, as well as the expanding number of users, it was critical to create a real-time framework for recommending and monitoring relevant tweets or posts based on the user's interests. Using association rules, the interests of social network users can be uncovered. A considerable number of association rules extracted from social networks were found to be mostly dependent on coverage requirements. After finding patterns of frequent and non-frequent patterns, a large number of non-frequent terms were eliminated in association rule mining. In order to reduce the complexity of the association rule mining process, the more relevant terms are selected by the Hybridized Competitive Swarm Optimizer and Gravitational Search Algorithm (CSO-GSA), which is utilized for association rule generation and classification using deep learning techniques. In this research, numerous relevant rules for human interest are identified. The numerical outcome of the proposed strategy is compared with existing state-of-the-art techniques. The proposed CSO with GSA outperforms the existing techniques.

Blind CFO estimation based on weighted subspace fitting criterion with fuzzy adaptive gravitational search algorithm

AbstractThis paper deals with the blind carrier frequency offset (CFO) estimation based on weighted subspace fitting (WSF) criterion with fuzzy adaptive gravitational search algorithm (GSA) for the interleaved orthogonal frequency-division multiplexing access (OFDMA) uplink system. For the CFO estimation problem, it is well known that the WSF has superior statistical characteristics and better estimation performance. However, the type of CFO estimation must pass through the high-dimensional space problem. Optimizing complex nonlinear multimodal functions requires a large computational load, which is difficult and not easy to maximize or minimize nonlinear cost functions in large parameter spaces. This paper firstly presents swarm intelligence (SI) optimization algorithms such as GSA, particle swarm optimization (PSO), and hybrid PSO and GSA (PSOGSA) to improve estimation accuracy and reduce the computational load of search. At the same time, this paper also integrates a fuzzy inference system to WSF-GSA to dynamically adjust the gravitational constant, which can not only reduce the searching computational load, but also improve the performance of GSA in the global optimization and solution accuracy. Finally, several simulation results are provided for illustrating the effectiveness of the proposed estimator.

Notice of Violation of IEEE Publication Principles: A Gravitational Search based Fuzzy Approach for Edge Detection in Colour And Grayscale Images

Notice of Violation of IEEE Publication Principles A Gravitational Search based Fuzzy Approach for Edge Detection in Colour And Grayscale by Swagatam Das, Satrajit Mukherjee, Bodhisattwa Prasad Majumder and Aritran Piplai Submitted to IEEE Transactions on Fuzzy System After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles. This paper duplicates significant amounts of content from the original paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article: An Optimal Fuzzy System for Edge Detection in Color Images using Bacterial Foraging Algorithm, by Om Verma Submitted to IEEE Transactions on Evolutionary Computation, 12 April 2012 This article presents an optimal edge detection scheme based on the concepts of fuzzy Smallest Univalue Assimilating Nucleus (SUSAN) and the Gravitational Search Algorithm (GSA). Initially, the Univalue Assimilating Nucleus (USAN) area is calculated from the gray levels of every neighborhood pixel of a pixel of interest in the test image. In accordance with the SUSAN principle, the neighborhood is chosen as a circular mask and applied separately on the individual RGB components of the image in case the image is a color image. The USAN area edge map of each component is fuzzified using a Gaussian membership function (used for detecting strong edges) and a bell-shaped function (used for detecting weak edges). Then the entropy and edge sharpness factors are calculated from these fuzzy measures and optimized using GSA by evolving the fuzzifier and the parameters controlling the shape and range of the bell-shaped curve. Adaptive thresholding converts the fuzzy domain edge map to a spatial domain edge map. Finally, the individual RGB edge maps are concatenated to obtain the final image edge map. Qualitative and quantitative comparisons have been rendered with respect to a few promising edge detectors (both traditional as well as state-of-the-art) and also optimal fuzzy edge detectors based on metaheuristic algorithms like Differential Evolution (DE) and Particle Swarm Optimizer (PSO). Extensive comparisons based on several quantitative measures strongly reflect merits of the proposed method.

Addressing a Collaborative Maintenance Planning Using Multiple Operators by a Multi-Objective Metaheuristic Algorithm

Selective maintenance has a significant impact on the sustainable management of maintenance operations. The collaboration of multiple maintenance teams/operators is helpful to achieve sustainability for selective maintenance sequence planning. For products with a large number of components, a single maintenance team/operator is inefficient due to a long completion time which is not acceptable for emergency planning. Providing specific and efficient maintenance sequence planning is critical to effectively handle different types of emergencies (e.g., wartime) while avoiding vague task assignments to multiple maintenance teams/operators. For scheduling many maintenance jobs while improving the efficiency and quality of maintenance operations, this study proposes a collaborative maintenance planning based on the concept of imperfect maintenance. In this regard, this study develops a multi-objective optimization model to optimize parallel maintenance sequences considering maintenance profit, maintenance cost, maintenance team, and resource limitations. We show the feasibility of the proposed multi-objective optimization model through a real case of maintenance practice for the components of an assistor device. For analyzing the complexity of the proposed maintenance sequence planning problem, this study introduces a new multi-objective metaheuristic algorithm which is an enhanced multi-objective gravitational search algorithm (EMOGSA) to find high-quality Pareto solutions for the proposed problem. Different multi-objective evaluation metrics are used to study the performance of the proposed algorithm. From the results, the proposed model and developed solution algorithm can help maintenance decision-makers to determine complex maintenance planning. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper deals product with a maintenance and proposes gravitational search algorithm based on only maintenance task, which maintenance task. The goal of this paper is to analyze the maintenance problem from the perspective of collaboration of multiple maintenance teams/operators.