This paper studies wireless sensor networks node deployment problem and proposes intelligent single particle optimizer based wireless sensor networks adaptive coverage. According to the probability model measure characteristic of wireless sensor nodes, the method adaptively determines the optimal deployment of sensor nodes using intelligent single particle optimizer, achieving sensor node based...

Clustering is one of the known techniques in the field of data mining where data with similar properties is within the set of categories. K-means algorithm is one the simplest clustering algorithms which have disadvantages sensitive to initial values of the clusters and converging to the local optimum. In recent years, several algorithms are provided based on evolutionary algorithms for cluster...

Abstract Efficient and accurate porosity prediction is essential for the fine description of reservoirs, which an optimized BP neural network (BPNN) model proposed. Aiming at problem that BPNN sensitive to initialization converges local optimum easily, improved shuffled frog leaping algorithm (ISFLA) proposed based on roulette genetic coding. Firstly, a mechanism introduced improve selection pr...

In this paper a novel method based on evolutionary algorithms is presented to estimate the harmonic components. In general, the optimization of the harmonic estimation process is a multi-component problem, in which evaluation of the phase and harmonic frequency is the nonlinear part of the problem and is solved based on the mathematical and evolutionary methods; while estimation of amplitude of...

The aim of this study is to focus on spectrum sensing in cognitive radio which is a recently introduced technology in order to increase the spectrum efficiency. We studied the Singular value decomposition based signal detector and its advantages over the energy based signal detection. Soft thresholding technique for spectrum sensing is optimized using SFLA (Shuffled Frog Leaping) and Ant Bee Co...