Structural reliability analysis using enhanced cuckoo search algorithm and artificial neural network

The present study proposed an enhanced cuckoo search(ECS) algorithm combined with artificial neural network(ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and convergence rate of the original cuckoo search(CS) algorithm, the main parameters namely, abandon probability of worst nests paand search step sizeα0 are dynamically adjusted via nonlinear control equations. In addition, a global-best guided equation incorporating the information of global best nest is introduced to the ECS to enhance its exploitation. Then, the proposed ECS is linked to the well-trained ANN model for structural reliability analysis. The computational capability of the proposed algorithm is validated using five typical structural reliability problems and an engineering application. The comparison results show the efficiency and accuracy of the proposed algorithm.

Enhanced Atom Search Optimization Based Optimal Control Parameter Tunning of PMSG for MPPT

For the past few years,wind energy is the most popular non-traditional resource among renewable energy resources and it’s significant to make full use of wind energy to realize a high level of generating power.Moreover,diverse maximum power point tracking(MPPT)methods have been designed for varying speed operation of wind energy conversion system(WECS)applications to obtain optimal power extraction.Hence,a novel and metaheuristic technique,named enhanced atom search optimization(EASO),is designed for a permanent magnet synchronous generator(PMSG)based WECS,which can be employed to track the maximum power point.One of the most promising benefits of this technique is powerful global search capability that leads to fast response and high-quality optimal solution.Besides,in contrast with other conventional meta-heuristic techniques,EASO is extremely not relying on the original solution,which can avoid sinking into a low-quality local maximum power point(LMPP)by realizing an appropriate trade-off between global exploration and local exploitation.At last,simulations employing two case studies through Matlab/Simulink validate the practicability and effectiveness of the proposed techniques for optimal proportional-integral-derivative(PID)control parameters tuning of PMSG based WECS under a variety of wind conditions.

Feature Selection Based on Enhanced Cuckoo Search for Breast Cancer Classification in Mammogram Image

Proposed system has been developed to extract the optimal features from the breast tumors using Enhanced Cuckoo Search (ECS) and presented in this paper. The texture feature, intensity histogram feature, radial distance feature and shape features have been extracted and the optimal feature set has been obtained using ECS. The overall accuracy of a minimum distance classifier and k-Nearest Neighbor (k-NN) on validation samples is used as a fitness value for ECS. The new approach is carried out on the extracted feature dataset. The proposed system selects only the minimum number of features and performed the accuracy of 98.75% with Minimum Distance Classifier and 99.13% with k-NN Classifier. The performance of the new ECS is compared with the Cuckoo Search and Harmony Search. This result shows that the ECS algorithm is more accurate than the other algorithm. The proposed system can provide valuable information to the physician in medical pathology.