The efficiency and performance of Twin Support Vector Machines (TWSVM) is better than the traditional support vector machines when it deals with the problems. However, it also has some problems. As the same as the traditional support vector machines, its parameters are difficult to be appointed and it is not easy to select the appropriate kernel function. TWSVM generally selects the Gaussian ra...

Shuffled leap frog algorithm (SFLA) is a new nature-inspired intelligent algorithm, which uses the whole update and evaluation strategy on solutions. For solving multi-dimension function optimization problems, this strategy will deteriorate the convergence speed and the quality of solution of algorithm due to interference phenomena among dimensions. To overcome this shortage, a dimension by dim...

in this paper, we propose a new gene selection algorithm based on shuffled frog leaping algorithm that is called sfla-fs. the proposed algorithm is used for improving cancer classification accuracy. most of the biological datasets such as cancer datasets have a large number of genes and few samples. however, most of these genes are not usable in some tasks for example in cancer classification. ...

This paper shows the results obtained in the analysis of the impact of Distributed Generation (DG) on distribution losses and presents a new algorithm to the optimal allocation of distributed generation resources in distribution networks. The optimization is based on a Shuffled Frog Leaping Algorithm (SFLA) aiming to optimal DG allocation in distribution network. Shuffled frogleaping algorithm ...

The paper proposes shuffled frog leaping algorithm (SFLA) based on enhanced learning, which generates a virtual general centre frog that is related to the optimal frog of each memeplex. The algorithm can utilise the superior information of each memeplex, enhance the mutual learning and use the average centre of optimal frog. In the processing of evolution, the optimal frog of sub-memeplex learn...

The shuffled frog leaping algorithm (SFLA) easily falls into local optimum when it solves multioptimum function optimization problem, which impacts the accuracy and convergence speed. Therefore this paper presents grouped SFLA for solving continuous optimization problems combined with the excellent characteristics of cloud model transformation between qualitative and quantitative research. The ...

In this paper, a new algorithm which is the result of the combination of cellular learning automata and frog leap algorithm (SFLA) is proposed for optimization in continuous, static environments.At the proposed algorithm, each memeplex of frogs is placed in a cell of cellular learning automata. Learning automata in each cell acts as the brain of memeplex, and will determine the strategy of moti...

There are various automatic programming models inspired by evolutionary computation techniques. Due to the importance of devising an automatic mechanism to explore the complicated search space of mathematical problems where numerical methods fails, evolutionary computations are widely studied and applied to solve real world problems. One of the famous algorithm in optimization problem is shuffl...

The importance of distributed generation (DG) has increased recently as a result the growth in commercial and industrial loads, which put more pressure on conventional energy sources utilities. Alternative power methods that can handle massive load without endangering environment are therefore urgently needed. installation Energy Storage Systems (ESSs) may give substantial opportunity to enhanc...

Landslide susceptibility analysis is beneficial information for a wide range of applications, including land use management plans. The present attempt has shed light on an efficient landslide mapping framework that involves adaptive neural-fuzzy inference system (ANFIS), which incorporates three metaheuristic methods grey wolf optimization (GWO), particle swarm (PSO), and shuffled frog leaping ...