ica algorithm

This paper proposes a novel approach to analyzing and managing electricity consumption using a clustering algorithm and a high-accuracy classifier for smart meter data. The proposed method utilizes a multilayer perceptron neural network classifier optimized by an Imperialist Competitive Algorithm (ICA) called ICA-optimized MLP, and a CD Index based on Fuzzy c-means to optimally determine representative load curves. A case study involving a real dataset of residential smart meters is conducted to validate the effectiveness of the proposed method, and the results demonstrate that the ICA-optimized MLP method achieves an accuracy of 98.62%, outperforming other classification methods. This approach has the potential to improve energy efficiency and reduce costs in the power system, making it a promising solution for analyzing and managing electricity consumption.

Multiple linear regression (MLR) as modeling tools and Imperialist Competitive Algorithm (ICA) as optimization techniques were employed to choose the best set of descriptors for linear -log(IC50) (the empirical negative logarithm half maximal inhibitory concentration) prediction of the Sulfonamide derivatives. A high predictive ability was observed for the MLR-ICA model with the best number of empires/ imperialists (nEmp=20) and (nEmp=60) with root mean sum square errors (RMSE) of 0.03375 and 0.036665 in gas phase and in solvent, respectively. The results obtained using the MLR-ICA method indicated that the activity of the derivatives of Sulfonamide depends on different parameters such as HVcpx, RDF090u, E1v, Wap, R5e, Mor15v, MPC08, RDF115p descriptors in the gas phase and including RDCHI,MATS1v, RDF115v, RDF080v, D/Dr06, piPC05,BEHp6 and G3m descriptors in the solvent phase. It was concluded that simultaneous utilization of MLR-ICA method can lead to a more comprehensive understanding of the relation between physico-chemical, structural or theoretical molecular descriptors of drugs to their biological activities and facilitate designing of new drugs.

In this research, we discuss the methods that have been proposed so far to solve automatic summarization, in which both single-text and multi-text are summarized with emphasis on experimental methods and text extraction techniques. In multi-text summarization, retrieving redundant information that is readable and coherent and contains maximum information from the original text and minimum redundancy has made research in this field very important. An extraction approach based on several methods for identifying sentence similarities and a meta-heuristic optimization algorithm that has been modified and optimized for faster convergence is presented. In this algorithm, changes are made based on density detection through the probability distribution function to avoid being placed in local optimization and try to search more extensively for the response space. The experimental results obtained from the implementation of the algorithm show that the efficiency on criteria such as ROUGE and the accuracy of the proposed method is effectively increased.

According to this fact that wind is now a part of global energy portfolio and due to unreliable and discontinuous production of wind energy; prediction of wind power value is proposed as a main necessity. In recent years, various methods have been proposed for wind power prediction. In this paper the prediction structure involves feature selection and use of Artificial Neural Network (ANN). In this paper, feature selection tool is applied in filtering of inappropriate and irrelevant inputs of neural network and is performed on the biases of mutual information. After determining appropriate inputs, the wind power value for the next 24-hours is predicted using neural network in which BP algorithm and PSO and ICA evolutionary algorithms are used as training algorithm. With investigation and compare numerical results, better performance of PSO and ICA evolutionary algorithm is deduced with respect to BP algorithm. More accurate survey will result in more proper efficiency of imperialist competitive algorithm (ICA) in comparison to swarm particle algorithm. Thus, in this paper; accuracy of the wind power prediction for the next 24-hours is improved considerably using mutual information and providing an irrelevancy filter for reducing the input dimension by eliminating the irrelevant candidates and more effectively using Imperialist competitive evolutionary algorithm for training the neural network.