Superior solution guided particle swarm optimization combined with local search techniques

Wu, Guohua; Qiu, Dishan; Yu, Ying; Pedrycz, Witold; Ma, Manhao; Li, Haifeng

doi:10.1016/j.eswa.2014.06.005

Cited by 79 publications

(36 citation statements)

References 55 publications

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“…The particle swarm optimization conducts search by each particle following the optimized particle. Therefore, it is simple and easy, and does not need to adjust many parameters [2].…”

Section: Basic Particle Swarm Optimization 21 Basic Idea Of the Basmentioning

confidence: 99%

Application of Chaotic Particle Swarm Optimization in Wavelet Neural Network

Zhao

Wang²

2014

TELKOMNIKA

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Currently, the method of optimizing the wavelet neural network with particle swarm plays a certain role in improving the convergence speed and accuracy; however, it is not a good solution for problems of turning into local extrema and poor global search ability. To solve these problems, this paper, based on the particle swarm optimization, puts forward an improved method, which is introducing the chaos mechanism into the algorithm of chaotic particle swarm optimization. Through a series of comparative simulation experiments, it proves that applying this algorithm to optimize the wavelet neural network can successfully solve the problems of turning into local extrema, and improve the convergence speed of the network, in the meantime, reduce the output error and improve the search ability of the algorithm. In general, it helps a lot to improve the overall performance of the wavelet neural network. Keywords: chaotic particle swarm optimization, convergence speed, wavelet neural network IntroductionThe optimism theory and method have existed since ancient time, among which, the relatively representative one is the golden section method. Optimism mainly solves the problem of finding the best solution from many solutions. We can defined optimism as: under certain restrictions, to make the problem reach a best measurement, or to find out a set of parameters, and make certain indicators reach the maximum or minimum. As an important branch of science, the optimism method is gaining more and more attention, and plays important roles in many fields, such as engineering technology, electrical engineering, image processing etc. However, in real life application, since the complexity and nonlinearity of many problems, the target functions of these problems are often discrete and of multi-point value, furthermore, the modeling the problem itself is also very difficult. When applying traditional optimizations like Newton method, dynamic programming, branch and bound method, etc. to solve these complex optimism problems, one usually need to traverse the entire search space, which will waste a lot of time, and can not meet the actual requirement in the aspects of the convergence of the problems and the optimization calculation speed. Therefore, in the current field of optimism, the key job is to seek the efficient optimization.Particle swarm optimization gain the attention of many international scholars in related fields rapidly since its advent. First, Kennedy J and Eberhart R. C. put forward the binary particle swarm optimization in 1997. Then, in 1998, in order to improve the convergence of the algorithm, Shi Y and Eberhart R C introduced the inertia weight parameter into the speed item of the PSO and proposed to dynamically adjust the inertia weight to balance the convergence speed during the process of evolution. This algorithm is called the standard PSO. Then, they put forward the linear decreasing inertia weight LDW-PSO, however, if it deviated from the overall optimum solution in the initial state, then the linear...

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“…The particle swarm optimization conducts search by each particle following the optimized particle. Therefore, it is simple and easy, and does not need to adjust many parameters [2].…”

Section: Basic Particle Swarm Optimization 21 Basic Idea Of the Basmentioning

confidence: 99%

Application of Chaotic Particle Swarm Optimization in Wavelet Neural Network

Zhao

Wang²

2014

TELKOMNIKA

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“…(12) best(t) = min j∈1,...,n max j∈1,...,n f it j (t) (13) worst(t) = max j∈1,...,n min j∈1,...,n f it j (t) (14) In order to find a good compromise between exploration and exploitation, the number of agents with a lapse of time has to be reduced. To improve the performance of GSA by controlling exploration and exploitation, only the K best agents will attract the others [22].…”

Section: The Gravitational Search Algorithmmentioning

confidence: 99%

“…The gravitational and inertial masses are updated by the Equations (10)- (12), where f it i (t) represents the fitness value of the agent i at time t, and worst(t) and best(t) are defined as in (13) and (14) for minimization (maximization) problems. (12) best(t) = min j∈1,...,n max j∈1,...,n f it j (t) (13) worst(t) = max j∈1,...,n min j∈1,...,n f it j (t) (14) In order to find a good compromise between exploration and exploitation, the number of agents with a lapse of time has to be reduced.…”

Section: The Gravitational Search Algorithmmentioning

confidence: 99%

“…Another evolutionary algorithm is the Particle Swarm Optimization (PSO), which depends on the simulation of social behavior [10]. This search method has been applied in various optimization problems [11][12][13][14][15]. An algorithm that operates through the computational steps of a standard evolutionary algorithm is the Differential Evolution (DE).…”

Section: Introductionmentioning

confidence: 99%

“…In fact, there are many optimization problems [13,[29][30][31][32][33][34], where the solutions are encoded as binary vectors. Rashedi et al [35] proposed a binary version of GSA, called BGSA.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Revised Gravitational Search Algorithms Based on Evolutionary-Fuzzy Systems

2017

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Abstract:The choice of the best optimization algorithm is a hard issue, and it sometime depends on specific problem. The Gravitational Search Algorithm (GSA) is a search algorithm based on the law of gravity, which states that each particle attracts every other particle with a force called gravitational force. Some revised versions of GSA have been proposed by using intelligent techniques. This work proposes some GSA versions based on fuzzy techniques powered by evolutionary methods, such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE), to improve GSA. The designed algorithms tune a suitable parameter of GSA through a fuzzy controller whose membership functions are optimized by GA, PSO and DE. The results show that Fuzzy Gravitational Search Algorithm (FGSA) optimized by DE is optimal for unimodal functions, whereas FGSA optimized through GA is good for multimodal functions.

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Classification of multiple power quality events via compressed deep learning

Liu

Hussain

Shen

et al. 2019

Int Trans Electr Energ Syst

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Summary This paper presents a recently established compressed sensing (CS) and sparse autoencoder (SAE) based on deep learning (DL) method for classification of single and multiple power quality disturbances (PQDs). The CS technique is paying considerable attention in recent years due to below sampling rate comparatively Nyquist sampling. Initially, the CS technique is applied to extract the features of PQD waveforms. The extracted features are applied as inputs to the sparse autoencoder based on DL for classification of nine single and 22 combined classes of PQDs. The DL helps to remove a redundant feature and improves classification performance. Finally, backpropagation is applied to fine‐tune the entire network. The effectiveness of the proposed algorithm has been tested with more than 6580 numbers of real and synthetic single and multiple PQD data, and the results are recorded. High correct classification rate is obtained with noise and without noise level. Noise level was considered from 20 to 50 dB. The performance of the proposed technique has been assessed by comparing the results against recently reported methods. Results show that the proposed CS‐ and SAE‐based DL algorithms can be efficiently used for single and multiple PQDs classifications.

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Superior solution guided particle swarm optimization combined with local search techniques

Cited by 79 publications

References 55 publications

Application of Chaotic Particle Swarm Optimization in Wavelet Neural Network

Application of Chaotic Particle Swarm Optimization in Wavelet Neural Network

Revised Gravitational Search Algorithms Based on Evolutionary-Fuzzy Systems

Classification of multiple power quality events via compressed deep learning

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