Hybridization Of Gravitational Search Algorithm Research Articles

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.

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 approach of gravitational search algorithm and ant miner plus for fingerprint recognition

The gravitational search algorithm (GSA) is an eminent heuristic algorithm inspired by the laws of gravity and motion. It possesses an independent physical model in which the mass agents are guided by gravitational force to quickly achieve the convergence. Although the GSA is proven to be efficient for science and engineering problems, the mass agents can be trapped in premature convergence due to the heaviness of masses in the later iterations. The occurrence of premature convergence impedes the agents’ further exploration of the search space for a better solution. Here, the ant miner plus (AMP) variant of the ant colony optimization (ACO) algorithm is utilized to avoid the trapping of agents in local optima. The AMP algorithm extends the exploration ability of the GSA algorithm by using the attributes of pheromone updating rules generated by best ants and a problem-dependent heuristic function. The AMP variant adheres to the attributes of the ACO algorithm and is also a decision-making variant which determines the problem solution more efficiently by constructing a directed acyclic graph, considering class-specific heuristic values, and including weight parameters for the pheromone and heuristic values. In this research, this hybridization of GSA and AMP (GSAMP) algorithms is presented, and it is utilized for the decision-making application of fingerprint recognition. Here, fingerprint recognition is conducted for complete as well as latent fingerprints, which are poor quality partial fingerprints, mostly acquired from crime scenes by law enforcement agencies. The experiments are performed for the complete fingerprint dataset of FVC2004 and the latent fingerprint dataset of NIST SD27, using the proposed GSAMP approach and the individual algorithms of Ant Miner (AM) and AMP. The experimental evaluation indicates the superiority of the proposed approach compared to other methods.

Estimation of minimum ignition energy of explosive chemicals using gravitational search algorithm based support vector regression

Adequate knowledge of minimum ignition energy (MIE) of a flammable chemical compound plays a significant role while handling and characterizing the hazardous materials and further ensures reliable ignition of fuel-air mixtures in many engines. Despite the significances of this parameter (MIE), its experimental determination is very dangerous, expensive and might be time consuming. The challenges associated with the experimental determination of minimum ignition energy are addressed in this present work through hybridization of gravitational search algorithm (GSA) with support vector regression (SVR) for estimating MIE using relatively few descriptors which include the number of carbon and hydrogen atoms as well as molecular weight of the compound. Novelties of this approach as compared with existing methods include (i) hybridization of GSA with SVR for modeling MIE for the first time, (ii) utilization of relatively few (three) descriptors and (iii) the ease with which the descriptors can be assessed. On the basis of root mean square error, the developed hybrid GSA-SVR shows superior performance as compared with the existing Beibei Wang et al. model with performance improvement of 24.03%. The accuracy of the proposed hybrid GSA-SVR model coupled with the ease of its implementation would definitely ensure quick estimation of MIE of compounds, prevent accidental explosion of hazardous chemicals in industry and enhance aviation safety.

A developed system based on nature-inspired algorithms for DNA motif finding process

In this paper, recent algorithms are suggested to repair the issue of motif finding. The proposed algorithms are cuckoo search, modified cuckoo search and finally a hybrid of gravitational search and particle swarm optimization algorithm. Motif finding is the technique of handling expressive motifs successfully in huge DNA sequences. DNA motif finding is important because it acts as a significant function in understanding the approach of gene regulation. Recent results of existing motifs finding programs display low accuracy and can not be used to find motifs in different types of datasets. Practical tests are implemented first on synthetic datasets and then on benchmark real datasets that are based on nature-inspired algorithms. The results revealed that the hybridization of gravitational search algorithm and particle swarm algorithms provides higher precision and recall values and provides average enhancement of F-score up to 0.24, compared to other existing algorithms and tools, and also that cuckoo search and modified cuckoo search have been able to successfully locate motifs in DNA sequences.

A NOVEL HYBRIDIZED ALGORITHM FOR REDUCTION OF REAL POWER LOSS

This paper proposes Hybridization of Gravitational Search algorithm with Simulated Annealing algorithm (HGS) for solving optimal reactive power problem. Individual position modernize strategy in Gravitational Search Algorithm (GSA) may cause damage to the individual position and also the local search capability of GSA is very weak. The new HGS algorithm introduced the idea of Simulated Annealing (SA) into Gravitational Search Algorithm (GSA), which took the Metropolis-principle-based individual position modernize strategy to perk up the particle moves, & after the operation of gravitation, Simulated Annealing operation has been applied to the optimal individual. In order to evaluate the efficiency of the proposed Hybridization of Gravitational Search algorithm with Simulated Annealing algorithm (HGS), it has been tested on standard IEEE 118 & practical 191 bus test systems and compared to the standard reported algorithms. Simulation results show that HGS is superior to other algorithms in reducing the real power loss and voltage profiles also within the limits.

A Hybridization of Gravitational Search Algorithm and Particle Swarm Optimization for Odor Source Localization

This paper concerns with the problem of odor source localization by a team of mobile robots. The authors propose two methods for odor source localization which are largely inspired from gravitational search algorithm and particle swarm optimization. The intensity of odor across the plume area is assumed to follow the Gaussian distribution. As robots enter in the vicinity of plume area they form groups using K-nearest neighbor algorithm. The problem of local optima is handled through the use of search counter concept. The proposed approaches are tested and validated through simulation.

A novel hybrid algorithm of GSA with Kepler algorithm for numerical optimization

It is now well recognized that pure algorithms can be promisingly improved by hybridization with other techniques. One of the relatively new metaheuristic algorithms is Gravitational Search Algorithm (GSA) which is based on the Newton laws. In this paper, to enhance the performance of GSA, a novel algorithm called “Kepler”, inspired by the astrophysics, is introduced. The Kepler algorithm is based on the principle of the first Kepler law. The hybridization of GSA and Kepler algorithm is an efficient approach to provide much stronger specialization in intensification and/or diversification. The performance of GSA–Kepler is evaluated by applying it to 14 benchmark functions with 20–1000 dimensions and the optimal approximation of linear system as a practical optimization problem. The results obtained reveal that the proposed hybrid algorithm is robust enough to optimize the benchmark functions and practical optimization problems.

Inverse Kinematic Solution of Robot Manipulator Using Hybrid Neural Network

Inverse kinematics of robot manipulator is to determine the joint variables for a given Cartesian position and orientation of an end effector. There is no unique solution for the inverse kinematics thus necessitating application of appropriate predictive models from the soft computing domain. Although artificial neural network (ANN) can be gainfully used to yield the desired results but the gradient descent learning algorithm does not have ability to search for global optimum and it gives slow convergence rate. This paper proposes structured ANN with hybridization of Gravitational Search Algorithm to solve inverse kinematics of 6R PUMA robot manipulator. The ANN model used is multi-layered perceptron neural network (MLPNN) with back-propagation (BP) algorithm which is compared with hybrid multi layered perceptron gravitational search algorithm (MLPGSA). An attempt has been made to find the best ANN configuration for the problem. It has been observed that MLPGSA gives faster convergence rate and improves the problem of trapping in local minima. It is found that MLPGSA gives better result and minimum error as compared to MLPBP.