The focal inadequacy of modern wireless sensor networks is constrained energy resources of sensors. Therefore, Energy efficient clustering techniques and routing algorithms must be deployed to intensify the durability of wireless sensor networks. The research of an improved particle swarm optimization method for wireless sensor networks is presented in this paper. In order to conquer the inadeq...

Energy-aware algorithms are important factors for extending the lifetime of the wireless sensor network. In energy concerned fields, network clustering has proved to be an efficient technique that renders structures of low consumption. Yet, clustering protocols face a major issue that is of grouping sensor nodes in an optimal way. This is an NP-Hard problem which necessitates evolutionary algor...

The multi-item inventory optimization problem with supplier selection is considered to be among the most interesting problems in Operations Research. Constraints such as limited capacity and defective items render the problem a demanding challenge for most solvers. Metaheuristic algorithms have been considered as promising approaches for tackling problems of this type. The present work investig...

A new dynamic clustering approach (DCPSO), based on Particle Swarm Optimization, is proposed. This approach is applied to unsupervised image classification. The proposed approach automatically determines the "optimum" number of clusters and simultaneously clusters the data set with minimal user interference. The algorithm starts by partitioning the data set into a relatively large number of clu...

The dogleg severity is one of the most important parameters in directional drilling. Improvement of these indicators actually means choosing the best conditions for the directional drilling in order to reach the target point. Selection of high levels of the dogleg severity actually means minimizing well trajectory, but on the other hand, increases fatigue in drill string, increases torque and d...

Corresponding Author: Athraa Jasim Mohammed School of Computing, Universiti Utara Malaysia, Kedah, Malaysia Email: [email protected] Abstract: Document clustering is widely used in Information Retrieval however, existing clustering techniques suffer from local optima problem in determining the k number of clusters. Various efforts have been put to address such drawback and this includes...

despite the growing use of evolutionary multi-objective optimization algorithms in different categories of science, these algorithms as a powerful tool in portfolio optimization and specially solving multi-objective portfolio optimization problem is still in its early stages. in this paper, moeas have been used for solving multi-objective portfolio optimization problem in tehran stock market. f...

Data clustering is a recognized data analysis method in data mining whereas K-Means is the well known partitional clustering method, possessing pleasant features. We observed that, K-Means and other partitional clustering techniques suffer from several limitations such as initial cluster centre selection, preknowledge of number of clusters, dead unit problem, multiple cluster membership and pre...

blind source separation technique separates mixed signals blindly without any information on the mixing system. in this paper, we have used two evolutionary algorithms, namely, genetic algorithm and particle swarm optimization for blind source separation. in these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. in order to evalu...

Abstract: One of the equipment that can help improve distribution system status today and reduce the cost of fault time is remote control switches (RCS). Finding the optimal location and number of these switches in the distribution system can be modeled with various objective functions as a nonlinear optimization problem to improve system reliability and cost. In this article, a particle swarm ...