GSA: A Gravitational Search Algorithm Rashedi, Esmat; Nezamabadi-pour, Hossein; Saryazdi, Saeid
Information sciences,
06/2009, Volume:
179, Issue:
13
Journal Article
Peer reviewed
In recent years, various heuristic optimization methods have been developed. Many of these methods are inspired by swarm behaviors in nature. In this paper, a new optimization algorithm based on the ...law of gravity and mass interactions is introduced. In the proposed algorithm, the searcher agents are a collection of masses which interact with each other based on the Newtonian gravity and the laws of motion. The proposed method has been compared with some well-known heuristic search methods. The obtained results confirm the high performance of the proposed method in solving various nonlinear functions.
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Stochastic configuration network (SCN), as a novel incremental generation model with supervisory mechanism, has an excellent superiority in solving large-scale data regression and classification ...problems. However, the accuracy of the SCN is affected by the assignation and selection of some network parameters significantly Sparrow search algorithm (SSA) is a new meta-heuristic algorithm that simulates the foraging and anti-predation behavior of sparrow population. In this paper, a stochastic configuration network based on chaotic sparrow search algorithm is first introduced, termed as CSSA-SCN. Firstly, chaotic sparrow search algorithm (CSSA) is designed which mainly utilizes logistic mapping, self-adaptive hyper-parameters, mutation operator to enhance the global optimization capability of SSA; Secondly, as the performance of SCN is related to regularization parameter r and scale factor λ of weights and biases, then CSSA is employed to give better parameters for SCN automatically; Finally, 13 benchmark functions and several datasets are used to evaluate the performance of CSSA and CSSA-SCN respectively. Experimental results demonstrate the feasibility and validity of CSSA-SCN compared with SCN and other contrast algorithms.
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This article presents a hybrid optimization technique particle swarm optimization-gravitational search algorithm for optimal over-current relay coordination. The main contribution of this article is ...to find the optimized relay settings during variation in environmental factors that affect distributed generation performance. The detailed model of wind and photovoltaic source, modeled in a PSCAD/EMTDC platform is penetrated in a 13-bus distribution system. The optimal relay settings are found for different cases, including change in wind speed, change in penetration level, change in cell temperature, and insolation level of photovoltaic. A comparison of particle swarm optimization-gravitational search algorithm with particle swarm optimization and gravitational search algorithm technique is also done, and it is shown that the particle swarm optimization-gravitational search algorithm is a superior method that can be applied in relay coordination tasks.
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•A novel metaheuristic algorithm called queuing search (QS) is proposed, which is inspired from human activities in queuing process.•QS does not need to preset the other parameters except the ...population size and stopping criterion.•Performance of QS is checked for thirty bound-constrained benchmark functions and some constrained engineering optimization problems.•QS shows the great ability of jumping out of a local optimal solution and searching the global optimum.
This paper presents a novel metaheuristic algorithm called queuing search (QS), which is inspired from human activities in queuing. Some common phenomena are considered in QS: (1) customers actively follow the queue that provides fast service; (2) each customer service is mainly affected by the staff or customer itself; and (3) a customer can be influenced by others during the service when the queue order is not strictly maintained. The performance of QS is tested on 30 bound-constrained benchmark functions scalable with 30 and 100 dimensions from CEC 2014, 5 standard and 4 challenging constrained engineering optimization problems. Meanwhile, comparisons are performed among the results of QS and some state-of-the-art or well-known metaheuristic algorithms.
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Gravitational search algorithm is an effective population-based algorithm. It simulates the law of gravity to implement the interaction among particles. Although it can effectively optimize many ...problems, it generally suffers from premature convergence and low search capability. To address these limitations, a gravitational search algorithm with hierarchy and distributed framework is proposed. A distributed framework randomly groups several subpopulations and a three-layered hierarchy manages them. Communication among subpopulations finally enhances the search performance. Experiments discuss parameters and strategies of the proposed algorithm. Comparison between it and sixteen state-of-the-art algorithms demonstrates its superior performance. It also shows the practicality for two real-world optimization problems.
•A new hierarchical and distributed gravitational search algorithm is proposed.•Its hierarchical and distributed structures exert different search effects.•Its performance is significantly enhanced in comparison with other algorithms.•It shows the practicality for real-world optimization problems.•Its computational efficiency is improved.
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Heart failure is considered one of the leading cause of death around the world. The diagnosis of heart failure is a challenging task especially in under-developed and developing countries where there ...is a paucity of human experts and equipments. Hence, different researchers have developed different intelligent systems for automated detection of heart failure. However, most of these methods are facing the problem of overfitting i.e. the recently proposed methods improved heart failure detection accuracy on testing data while compromising heart failure detection accuracy on training data. Consequently, the constructed models overfit to the testing data. In order, to come up with an intelligent system that would show good performance on both training and testing data, in this paper we develop a novel diagnostic system. The proposed diagnostic system uses random search algorithm (RSA) for features selection and random forest model for heart failure prediction. The proposed diagnostic system is optimized using grid search algorithm. Two types of experiments are performed to evaluate the precision of the proposed method. In the first experiment, only random forest model is developed while in the second experiment the proposed RSA based random forest model is developed. Experiments are performed using an online heart failure database namely Cleveland dataset. The proposed method is efficient and less complex than conventional random forest model as it produces 3.3% higher accuracy than conventional random forest model while using only 7 features. Moreover, the proposed method shows better performance than five other state of the art machine learning models. In addition, the proposed method achieved classification accuracy of 93.33% while improving the training accuracy as well. Finally, the proposed method shows better performance than eleven recently proposed methods for heart failure detection.
The degradation of power converter performance is one of the most critical issues of complex system with the improvement of power capacity and density. Power converter bears severe electrical and ...thermal stress, resulting in an increase in the probability of failure and significant economic losses. Most research addresses performance evaluation either through reliability theory without physical understanding or through data-driven methods requiring high experimental cost. Few studies focus on predicting system-level performance degradation, which is technically difficult as many components degrade randomly. Identifying the parameters of electronic components based on sensor data has become possible with the development of neural networks and computational power. Therefore, in this article, we propose a novel system-level power degradation predicting framework, which combines the advantages of neural networks in nonlinear fitting and empirical knowledge to predict the degradation of the power converter. In addition, a comprehensive and improved feature parameter screening method is proposed to identify the most critical feature parameters of the power converter systems. Furthermore, the neural network parameter identification method based on the sparrow search algorithm-Elman neural network is introduced to improve prediction accuracy. Finally, the result shows that the proposed method can accurately predict the degradation of the system by using a DC-DC converter as an example.
•The components decomposed by EMD algorithm are predicted separately.•A reinforced LSTM model is designed to improve prediction accuracy.•An ISSA combines Cauchy variation and reverse learning is ...proposed.•The ISSA is used to optimize the hyperparameters of the reinforced LSTM model.
Accurate ultra-short-term wind speed prediction is of great significance to the power generation efficiency of wind farms, and also has a good application prospect in the field of meteorology. In this study, a novel prediction model based on empirical mode decomposition and improved sparrow search algorithm optimized reinforced long short-term memory neural network is proposed. The implementation process of the proposed prediction model is: (a) the ultra-short-term wind speed is decomposed by empirical mode decomposition algorithm. (b) the decomposed components are predicted by reinforced long short-term memory neural network. (c) the hyperparameters of long short-term memory neural network are optimized by designed improved sparrow search algorithm. (d) the predicted values of each reinforced long short-term memory model are superimposed to get the final predicted value. The actual ultra-short-term wind speed data is taken as the research object. R2, RMSE, RRMSE, MRMSE, MAE, MAPE, TIC, IA, Pearson’s test, prediction error distribution box-plot, and Taylor diagram are used to judge the performance indexes of the designed prediction model. In case studies, the performance of the designed prediction model is compared with standard LSTM, EMD and standard LSTM, EMD and standard SSA optimized reinforced LSTM, and other state-of-the-art models. The results show that the proposed ultra-short-term wind speed prediction model achieves the best prediction performance and prediction accuracy.
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In this manuscript, new quadrant-based search algorithm with zero motion prejudgment is proposed for motion estimation (ME) in HEVC (High Efficiency Video Coding) standard. The HEVC standard is used ...to obtain efficient output with low motion estimation time. The proposed quadrant-based search algorithm is a fast block matching algorithm that obtain better block matching amid the current block and reference block. The zero motion prejudgment (ZMP) method is used to find the block, whether it is motion or static and it is used for decreasing the computational complexity (CC) in the proposed quadrant-based search algorithm. The proposed quadrant-based search algorithm with ZMP technique for motion estimation in HEVC is implemented on the FPGA hardware platform. The entire architecture is executed in Verilog HDL with Virtex-5 technology and integrated with Xilinx ISE Design Suite 14.5. The results are integrated into the CIF (352 × 288 pixels) and HD (1280 × 720 pixels) video input sequence. The evaluation metrics like PSNR, Motion estimation time, sum of absolute difference (SAD) value are analyzed with existing method like hexagon, adaptive root pattern algorithm, and diamond search algorithm. Then the hardware parameters like power consumption and maximum operating frequency are measured. The hardware utilization is reduced and the power consumption of the proposed model is diminished to 0.143 W. The maximal operating frequency of the proposed model is 440.470 MHz. The experimental outcomes demonstrate that the proposed motion evaluation approach in HEVC is more effective than existing algorithms.
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Summary
The main goal behind the combined economic emission dispatch (CEED) is to reduce the costs incurred upon fuel and emission for the generating units available without any intention to violate ...the generator and security constraints. Hence, the CEED must be handled after considering two challenging goals such as the costs involved with emission and fuel. In this paper, chaotic self‐adaptive interior search algorithm (CSAISA) was proposed to solve the CEED problems, considering the nonlinear behavior of generators in terms of valve point effects, prohibited operating zones, and security constraints. The proposed algorithm was tested for its effectiveness using 11‐generating units (without security), IEEE‐30 bus system, and IEEE‐118 bus system with security constraints. The results of the proposed CSAISA were compared with interior search algorithm (ISA), harmony search algorithm (HSA), differential evolution (DE), particle swarm optimization (PSO), and genetic algorithm (GA). To conclude, the proposed CSAISA outperformed all other algorithms in terms of convergence speed, implementation time, and solution quality, which was tested using performance metrics.
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