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...

The frequency modulation sound parameter identification is a complex multimodal optimization problem. This problem is modeled in the form of a cost function that is the sum-squared error between the samples of estimated wave and the samples of real wave. In this research, the authors propose a shuffled particle swarm optimization algorithm to solve this problem. In the shuffled particle swam op...

This study investigates a hybrid algorithm between Grey Wolf Optimization (GWO) and Cuckoo Search (CS) algorithms to find the parameters of induction motors. The motor have been estimated by using data supplied manufacturer. problem for parameter estimation is formulated as an optimization problem. Then, solved GWO based on CS parameters. Numerical results show that both are capable solving fin...

In order to handle large scale problems, this study has used shuffled frog leaping algorithm. This algorithm is an optimization method based on natural memetics that uses a new two-phase modification to it to have a better search in the problem space. The suggested algorithm is evaluated by comparing to some well known algorithms using several benchmark optimization problems. The simulation res...

in distribution systems, in order to diminish power losses and keep voltage profiles within acceptable limits, network reconfiguration and capacitor placement are commonly used. in this paper, the hybrid shuffled frog leaping algorithm (hsfla) is used to optimize balanced and unbalanced radial distribution systems by means of a network reconfiguration and capacitor placement. high accuracy and ...

Shuffled frog-leaping algorithm (SFLA) has long been considered as new evolutionary algorithm of group evolution, and has a high computing performance and excellent ability for global search. Knapsack problem is a typical NP-complete problem. For the discrete search space, this paper presents the improved SFLA (ISFLA), and solves the knapsack problem by using the algorithm. Experimental results...

Optimal placement and sizing of DG in distribution network is an optimization problem with continuous and discrete variables. Many researchers have used evolutionary methods for finding the optimal DG placement. This paper proposes a shuffled frog leaping algorithm (SFLA) for optimal placement and sizing of distributed generation (DG) in radial distribution system to minimize the total real pow...

Shuffled Frog Leaping Algorithm (SFLA) is one of the most widespread algorithms. It was developed by Eusuff and Lansey in 2006. SFLA a population-based metaheuristic algorithm that combines benefits memetics with particle swarm optimization. has been used various areas, especially engineering problems due to its implementation easiness limited variables. Many improvements have made alleviate dr...