Generation Bidding Strategy in a Pool-Based Electricity Market Using Oppositional Gravitational Search Algorithm

Hybridization of Constriction Coefficient Based Particle Swarm Optimization and Gravitational Search Algorithm for Function Optimization

— In this paper, two optimization algorithms i.e., Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) are combined to produce a hybrid population-based algorithm PSOGSA. The approach of HPSOGSA is to use the assessment of PSO using the social thinking capability and the manipulation of GSA using the local search proficiency. To increase the performance of HPSOGSA, some benchmark functions are used. Two standard database ORL and YALE is used to assess the classification performance of the proposed method. Classification accuracy is compared for diverse number of training samples per class. Further lead of proposed method is confirmed by analyzing percentage improvement in classification accuracy. With limited accessibility of training sample, percentage enhancement is very successful for face recognition applications. Keywords: Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), Hybrid Particle Swarm Optimization and Gravitational Search Algorithm (HPSOGSA). Cite this Article Purvaa Saxena, Gargi Mishra, Apoorva Aggarwal, Ayush Priyadarshi, Nitin Kumar, Rahul. Pattern Recognition Using Hybrid Meta-Heuristic. Journal of Artificial Intelligence Research & Advances. 2019; 6(2): 100–108p.

The history matching, a key step in the reservoir numerical simulation, is performed to obtain a set of appropriate reservoir parameters to match the modeling results with the oilfield historical production data. The history matching problem is solved conventionally with Gauss-Newton, L-M, BFGS, and LBFGS methods, which require calculation of the gradient of the objective function, and is very slow in solving large-scale problems. In recent years, modern optimization algorithms, such as the genetic algorithm, particle swarm optimization, and differential evolution algorithm, have been applied in the reservoir history matching. In this paper, the hybrid particle swarm optimization and gravitational search algorithm (PSOGSA) is used to carry out the research on the automatic reservoir history matching. First, the basic concepts of the particle swarm optimization (PSO) and the gravity search algorithm (GSA) were introduced. Then, the concept and algorithm of PSOGSA were described. Moreover, the actual oil field data were used to establish a geological model and an objective function, and the PSOGSA was used to carry out the automatic history matching. The PSOGSA was compared with other algorithms. Finally, the effects of parameters on the PSOGSA were analyzed. The results show that the PSOGSA has a good performance in the automatic reservoir history matching, increases the diversity of particles, improves the global search capability, and accelerates the convergence. The PSOGSA is superior to the PSO or the GSA.

Particle swarm optimization (PSO) has been proven to show good performance for solving various optimization problems. However, it tends to suffer from premature stagnation and loses exploration ability in the later evolution period when solving complex problems. This paper presents a sequential hybrid particle swarm optimization and gravitational search algorithm with dependent random coefficients called HPSO-GSA, which first incorporates the gravitational search algorithm (GSA) with the PSO by means of a sequential operating mode and then adopts three learning strategies in the hybridization process to overcome the aforementioned problem. Specifically, the particles in the HPSO-GSA enter into the PSO stage and update their velocities by adopting the dependent random coefficients strategy to enhance the exploration ability. Then, the GSA is incorporated into the PSO by using fixed iteration interval cycle or adaptive evolution stagnation cycle strategies when the swarm drops into local optimum and fails to improve their fitness. To evaluate the effectiveness and feasibility of the proposed HPSO-GSA, the simulations were conducted on benchmark test functions. The results reveal that the HPSO-GSA exhibits superior performance in terms of accuracy, reliability, and efficiency compared to PSO, GSA, and other recently developed hybrid variants.

In the present research, four meta-heuristic algorithms named Genetic Algorithm (GA), Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC) have been used for optimal design of cascade spillway to minimize the construction cost.Usually, the traditional design methods such as Vittal and Porey (VP) method or experimental modeling are used to solve this problem leading to infeasible or near optimal solution.The main novelty of this paper is to use effective methods to solve this complex highly constrained problem.Therefore, due to unique features of meta-heuristic algorithms, these algorithms are used here to minimize the construction cost of cascade spillway as the energy dissipater structure.As the case study, cascade spillway of Tehri dam in India had been chosen.The algorithms results have been compared together and with the VP results.Comparison of the results show the effectiveness and affectivity of these algorithms to solve this optimization problem.In other words, when three-stepped spillway are considered, the results are improved with 16.16%, 16.4%, 17.73% and 17.63% respectively using GA, GSA, PSO and ABC algorithms and in the same manner, for four-stepped spillway, the results are improved 14.5%, 16.1%, 16.45% and 16.05% respectively using GA, GSA, PSO and ABC algorithms in comparison with the VP results.

Estimation of transformer parameters from nameplate data by imperialist competitive and gravitational search algorithms

The detection of variables that contribute to the variation of a system is one of the most important considerations in the industrial manufacturing processes. This work presents the combination of Mahalanobis–Taguchi system and a hybrid binary metaheuristic based on particle swarm optimization and gravitational search algorithm (BPSOGSA) to perform an optimal feature selection in order to detect the relevant variables in a real process of foam injection in automotive industry. The proposed method is compared with other feature selection approach based in binary PSO algorithm. The experimental results revealed that BPSOGSA is faster and successfully converge selecting a smallest subset of features than BPSO. Moreover, the feature selection effect is validated through other widely used machine learning algorithms which improve their accuracy performance when they are trained with the subset of detected variables by the proposed system.

Optimal static state estimation using improved particle swarm optimization and gravitational search algorithm

In this study, an artificial intelligence technique is proposed to be implemented in the parameter tuning of a PVD process. Due to its previous adaptation in similar optimization problems, genetic algorithm (GA) is selected to optimize the parameter tuning of the RF magnetron sputtering process. The most optimized parameter combination obtained from GA’s optimization result is expected to produce the desirable zinc oxide (ZnO) thin film from the sputtering process. The parameters involved in this study were RF power, deposition time and substrate temperature. The algorithm was tested to optimize the 25 datasets of parameter combinations. The results from the computational experiment were then compared with the actual result from the laboratory experiment. Based on the comparison, GA had shown that the algorithm was reliable to optimize the parameter combination before the parameter tuning could be done to the RF magnetron sputtering machine. In order to verify the result of GA, the algorithm was also been compared to other well known optimization algorithms, which were, particle swarm optimization (PSO) and gravitational search algorithm (GSA). The results had shown that GA was reliable in solving this RF magnetron sputtering process parameter tuning problem. GA had shown better accuracy in the optimization based on the fitness evaluation.

This paper presents a modified sliding mode controller (MSMC) for tracking purpose of electro-hydraulic actuator system with mismatched disturbance. The main contribution of this study is in attempting to find the optimal tuning of sliding surface parameters in the MSMC using a hybrid algorithm of particle swarm optimization (PSO) and gravitational search algorithms (GSA), in order to produce the best system performance and reduce the chattering effects. In this regard, Sum square error (SSE) has been used as the objective function of the hybrid algorithm. The performance was evaluated based on the tracking error identified between reference input and the system output. In addition, the efficiency of the designed controller was verified within a simulation environment under various values of external disturbances. Upon drawing a comparison of PSOGSA with PSO and GSA alone, it was learnt that the proposed controller MSMC, which had been integrated with PSOGSA was capable of performing more efficiently in trajectory control and was able to reduce the chattering effects of MSMC significantly compared to MSMC-PSO and MSMC-GSA, respectively when the highest external disturbance, 10500N being injected into the system’s actuator.

Wireless Localization based on Received Signal Strength Indication (WL-RSSI) consists of predicting the localization of a particular device given the radio signals it receives. WL-RSSI methods are suitable for specific scenarios where Global Positioning System (GPS) is unstable or unavailable, such as indoor localization. Developing more efficient WL-RSSI methods is necessary to supplement GPS localization in such applications. Feedforward neural network trained by hybrid Particle swarm optimization and Gravitational search algorithm (FPG) is an optimization strategy that aims at better exploring the network weight-space when compared to methods such as Backpropagation (BP). Feedforward neural network trained by hybrid Particle swarm optimization and Gravitational search algorithm (FPG) is a kind of machine learning model with better exploring ability in the solution search space compared with conventional neural network training methods such as Backpropagation (BP). This article investigates a method to solve the slow convergence problem of conventional FPG and further improve its performance. Extreme Learning Machines (ELMs) are used to pre-train initial particles of the FPG (EP-FPG). This article also presents the application of EP-FPG to classification and regression WL-RSSI problems. Experimental results demonstrate that the proposed EP-FPG performs better on WL-RSSI problems than conventional FPG and BP.

Summary A reliable protection system is needed for transmitting large amount of power over long distances. An alternating current–direct current mix network in offshore wind farm (OWF) interconnection with onshore grid offers a major challenge for faulty section identification. The transient faults in OWF system are simulated in MATLAB 7.10.0(R2010a). The fault signals, voltage and current, are retrieved at common collector platform and at voltage-source converter–high-voltage direct current sides of network in the OWF system. The detection capability of signal-processing techniques, Stockwell transform, empirical mode decomposition, and Hilbert transform, is explored for localization of different types of faults. In addition, fault events at onshore grid and those developed at the time of change in operating parameters are also simulated and analyzed. Further, the quantitative analysis for detection of fault events with the computation of maximum and mean indices and instantaneous time indices of the fault signal is presented in the study. Coordination of overcurrent relays is another challenging task in OWF interconnection with onshore grid system. The objective function of relay coordination problem is to minimize the operation time of associated relays. Time multiplier setting is used as the test variable in the objective function. An optimal coordination of overcurrent relays by using genetic algorithm, particle swarm optimization, and gravitational search algorithm is discussed in this paper. Further, these techniques are compared with hybrid meta-heuristic optimization method, a combination of particle swarm optimization and gravitational search algorithm to achieve relay coordination more efficiently. Copyright © 2015 John Wiley & Sons, Ltd.

The development of a novel hybrid algorithm (AOA) with the aid of simulated annealing technique is discussed in this paper. The novel algorithm, named modified arithmetic optimization algorithm (mAOA), is proposed as an effective tool for optimizing power system stabilizer (PSS) adopted in a single-machine infinite-bus power system. To perform the assessments, MATLAB/Simulink software was used. The evaluations on the proposed algorithm are initially performed using several benchmark functions that have unimodal and multimodal natures. The results are then compared with five of the other competitive approaches (arithmetic optimization algorithm, simulated annealing algorithm, genetic algorithm, particle swarm optimization and gravitational search algorithm). The comparisons with respect to those algorithms demonstrate the great promise of the constructed hybrid mAOA algorithm. This shows the greater balance between global and local search stages achieved by the mAOA algorithm. The performance of the developed mAOA algorithm is also assessed through designing an optimally performing PSS for further evaluation which allows the observation of its capability for complex real world engineering problems. To do so, PSS damping controller is formulated as an optimization problem and the constructed mAOA algorithm is used to search for optimal controller parameters to demonstrate the applicability and the greater performance of the proposed hybrid algorithm for such a complex real-world engineering problem. The obtained results for the latter case are compared with the sine-cosine and symbiotic organisms search algorithms as they are the best performing reported algorithms. The comparisons have demonstrated the superiority of the mAOA algorithm over reported best performing algorithms in terms of PSS design, as well.

Hypersonic reentry trajectory planning by using hybrid fractional-order particle swarm optimization and gravitational search algorithm

<strong class="journal-contentHeaderColor">Abstract.</strong> In this paper, we have developed the Matlab code for a weighted hybrid of particle swarm optimization (PSO) and gravitational search algorithm (GSA) known as wPSOGSA, GSA, and PSO algorithms to interpret one-dimensional magnetotelluric (MT) data for some corrupted and non-corrupted synthetic data, as well as two examples of MT field data over different geological terrains: (i) geothermal rich area, Island of Milos, Greece, and (ii) Southern Scotland due to the occurrence of a significantly high electrical conductivity anomaly under crust and upper mantle extending from the Midland Valley across the Southern Uplands into northern England. Even though the fact that many models provide a good fit in a large predefined search space, specific models do not fit well. As a result, we used a Bayesian statistical technique to construct and assess the posterior probability density function (PDF) rather than picking the global model based on the lowest misfit error. This is proceeded by 68.27 % confidence interval for selecting a region where PDF is more prevalent to estimate the mean model which is more accurate and close to the true model. For illustration, correlation matrices show a significant relationship among layer parameters. The findings indicate, the wPSOGSA is less sensitive to model parameters and produces well, more stable and reliable results with the least uncertainty in the model that is compatible with existing borehole samples. Furthermore, the present methods resolve two additional geologically significant layers, one highly conductive (less than 1.0 Ωm) and another resistive (300.0 Ωm) over the Island of Milos, Greece, characterized by alluvium and volcanic deposits, respectively, as corroborated by borehole stratigraphy.