DEPSO: hybrid particle swarm with differential evolution operator
نویسندگان
چکیده
A hybrid particle swarm with differential evolution operator, termed DEPSO, which provide the bell-shaped mutations with consensus on the population diversity along with the evolution, while keeps the selforganized particle swarm dynamics , is proposed. Then it is applied to a set of benchmark functions, and the experimental results illustrate its efficiency.
منابع مشابه
A image segmentation algorithm based on differential evolution particle swarm optimization fuzzy c-means clustering
This paper presents a hybrid differential evolution, particle swarm optimization and fuzzy c-means clustering algorithm called DEPSO-FCM for image segmentation. By the use of the differential evolution (DE) algorithm and particle swarm optimization to solve the FCM image segmentation influenced by the initial cluster centers and easily into a local optimum. Empirical results show that the propo...
متن کاملApplication of Signal Feature Extraction of Double Cavity Jaw Crusher Based on DEPSO
The sparse decomposition of vibration signal is the important part of the fault diagnosis of Double Cavity Jaw Crusher. But the calculation count of sparse decomposition is very large, and it is difficult to fulfill signal processing. After analyzing characteristics of Double Cavity Jaw Crusher, this paper proposes applying the hybrid algorithm, DEPSO which mixed the characteristics of particle...
متن کاملModeling of gene regulatory networks with hybrid differential evolution and particle swarm optimization
In the last decade, recurrent neural networks (RNNs) have attracted more efforts in inferring genetic regulatory networks (GRNs), using time series gene expression data from microarray experiments. This is critically important for revealing fundamental cellular processes, investigating gene functions, and understanding their relations. However, RNNs are well known for training difficulty. Tradi...
متن کاملRNN based MIMO channel prediction
A new hybrid PSO-EA-DEPSO algorithm based on particle swarm optimization (PSO), evolutionary algorithm (EA), and differential evolution (DE) is presented for training a recurrent neural network (RNN) for multiple-input multiple-output (MIMO) channel prediction. This algorithm is shown to outperform RNN predictors trained off-line by PSO, EA, and DEPSO as well as a linear predictor trained by th...
متن کاملAn Efficient Hybrid SIMBO-GA Approach to Design FIR Low Pass Filter
In this paper a narrative approach for designing FIR low pass filter is presented by practicing hybrid technique of Swine Influenza Model based Optimization (SIMBO) and Genetic Algorithm (GA). Premature convergence was the major difficulty faced by SIMBO algorithm individually in FIR filter design. To address this problem, a hybrid SIMBO-GA is proposed in this paper. GA is used to help SIMBO es...
متن کامل