The existing original BP neural network models for wood performance prediction have low fitting accuracy and imprecise results. We propose a nonlinear, adaptive grouping gray wolf optimization (NAGGWO)-BP model prediction. Firstly, the (GWO) algorithm is optimized. CPM mapping (the Chebyshev method combined with piecewise followed by mod operation) to generate initial populations improve popula...

Localization is crucial to wireless sensor networks. Among the recently proposed localization algorithms, mobile anchor-assisted (MAL) algorithm seems promising. A MAL using a single anchor has low energy consumption but high error. Conversely, with three or more anchors minor errors consumption. By balancing and accuracy, our study developed assisted by two anchors. traverses network along dou...

The non-orthogonal multiple access (NOMA) scheme has proven to be a potential candidate enhance spectral potency and massive connectivity for 5G wireless networks. To achieve effective system performance, user grouping, power control, decoding order are considered fundamental factors. In this regard, joint combinatorial problem consisting of grouping control is considered, obtain high spectral-...

A valve-controlled hydraulic cylinder system has the characteristics of uncertainty and time-variance, electro-hydraulic servo unit encounters shock, vibration, other external interference when working, which seriously affect stability system. Therefore, it is necessary to introduce an active disturbance rejection controller (ADRC) into control. However, there are many ADRC parameters, difficul...

The data classification method based on support vector machine (SVM) has been widely used in various studies as a non-linear, high precision, and good generalization ability learning method. Among them, the kernel function its parameters have great impact accuracy. In order to find optimal improve accuracy of SVM, this paper proposes multi-classification gray wolf algorithm optimized SVM(GWO-SV...

Grey wolf optimizer (GWO) is a new technique, which can be applied successfully for solving optimized problems. The GWO indeed simulates the leadership hierarchy and hunting mechanism of grey wolves. There are four types of grey wolves which are alpha, beta, delta and omega. Those four types can be used for simulating the leadership hierarchy. In order to complete the process of GWO a three mai...

Hybridization of two or more variants algorithms is the recent trend research field. With help a hybridized algorithm we try to find out better optimal solution and solve various optimization applications. In this paper, new approach Advancement on Grey Wolf Optimization with Fitness Based Self Adaptive Differential Evolution (AGWO-FSADE) proposed. populations are calculated using self adaptive...

Cloud Computing is the dynamic provisioning of resources to provide services end-users over internet. The realization cloud computing requires addressing several challenges, such as resource discovery, security, scheduling, and load balancing. Among these research issues, balancing most challenging one. Therefore, in past few years, into various static algorithms achieve optimal results gaining...

Over the last few years, Convolution Neural Networks (CNN) have shown dominant performance over real world applications due to their ability find good solutions and deal with image data. However is highly dependent on network architecture methods for optimizing hyper parameters especially number size of filters. Designing a CNN requires human expertise domain knowledge. So, it difficult in suff...

To address the problem of insufficient coverage WSN and poor network in obstacle environments, study proposes an improved particle swarm optimization (PSO) combined with a hybrid grey wolf algorithm. The speed position PSO particle's search for superiority are enhanced through guiding nature superior (GWO), thus convergence precision improved. Based on this, applies to wireless sensor networks ...