In this paper, a new ALGORITHM which is the result of combination of cellular learning automata (CLA) and shuffled FROG leap ALGORITHM (SFLA) is proposed for optimization of functions in continuous, static environments. In the FROG LEAPING ALGORITHM, every FROG represents a feasible solution within the problem space. In the proposed ALGORITHM, each memeplex of FROGs is placed in a cell of CLA. Learning automata in each cell acts as the brain of memeplex and will determine the strategy of motion and search. The proposed ALGORITHM along with the standard SFLA and two global and local versions of particle swarm optimization ALGORITHM have been tested in 30-dimensional space on five standard merit functions. Experimental results show that the proposed ALGORITHM has a performance of the introduced ALGORITHM is due to the control of search behavior of FROGs during the optimization process.

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 clustering, but unfortunately they have shown disappointing behavior. In this study, a shuffled FROG LEAPING ALGORITHM (LSFLA) is proposed for clustering, where the concept of mixing and chaos is used to raise the accuracy of the ALGORITHM. Because the use of concept of entropy in the fitness functions, we are able to raise the efficiency of the ALGORITHM for clustering. To perform the test, the four sets of real data are used which have been compared with the ALGORITHMs K-menas, GA, PSO, CPSO. The results show better performance of this method in the clustering.

Measurement of magnitude and frequency of the voltage flicker is very important for monitoring and controlling voltage flicker efficiently to improve the network power quality. This paper presents two new methods for measurement of flicker signal parameters using Shuffled FROG LEAPING ALGORITHM (SFLA) and Imperialist Competitive ALGORITHM (ICA). This paper estimates fundamental voltage and flicker magnitudes and frequencies with proposed methods. The goal is to minimize the error of the estimated magnitudes and frequencies via a designed fitness function. At first, we introduce voltage flicker and its measuring techniques. Then voltage flicker model is analyzed. At the next part, a review of SFLA and ICA is presented. These methods will be applied to a test voltage signal and the results are be analyzed.

Various methods have been proposed for constructing and optimizing fuzzy inference systems. This paper first proposes a new method to cre-ate zero-order Sugeno fuzzy inference systems using the Shuffled FROG LEAPING ALGORITHM (SFLA). As the second contribution, the paper introduces four improvements over SFLA. The resulting version of SFLA, called ISFLA (Improved SFLA), is also applied to create zero-order Sugeno fuzzy inference systems. We conducted experiments to assess ISFLA and compare it with the original SFLA and three well-known evolutionary ALGORITHMs over five standard classification data sets from the UCI machine learning repository. The experimental results show that ISFLA creates fuzzy systems more efficiently than the standard SFLA and some other evolutionary ALGORITHMs, i. e., GA, ACO and PSO. Moreover, with respect to the accuracy and the convergence speed criteria, ISFLA and PSO outperform other evolutionary ALGORITHMs, while their performance is comparable to each other.

In this paper a new method has been proposed to solve Combined Heat and Power Economic Dispatch (CHPED) based on Modified Shuffled FROG LEAPING ALGORITHM (MSFLA). Difficulty on complexity of the problem is related to its constraints. This ALGORITHM can satisfy constraints simply. This optimization ALGORITHM has a wide spread search space and this characteristic helps to achieve optimum solutions. Application of MSFLA in CHPED problem has been simulated on two test cases. Numerical results show that MSFLA has proposed better solutions in comparison with other existing methods.

The Muskingum model is a hydrologic flood routing method in which the accuracy of its parameter estimation affects the routed hydrograph, especially in the value of the peak flow flood. Meta-heuristic ALGORITHMs are good candidates to determine optimal/near-optimal parameters in the Muskingum model. In this paper, shuffled FROG LEAPING ALGORITHM (SFLA) was applied and compared in two benchmark and real case studies. The sum of the squared deviation (SSQ) between observed and routed outflows and the sum of the absolute value of deviation (SAD) between observed and routed outflow as the objective functions (DPO) as an important parameter on the routed flood hydrograph. Results show were considered that the SFLA improves (decreases) the SSQ and SAD by 0.03 and 0.40 percent in the benchmark problem, and by 0.27 and 3.67 percent in the real case study, respectively, compared to reported results using various optimization ALGORITHMs. In addition, the SFLA improves (decreases) the DPO of the routed hydrograph in the benchmark and real problem by 17.65 and 3.67 percent compared to the best (minimum) reported result.

The niche methods for search ALGORITHMs are important techniques in optimization. Most niche techniques need some extra tunable parameters to get a better performance. Achieving a good method for multimodal optimization by heuristic ALGORITHMs will be possible if and only if the population diversity is preserved. Shuffled FROG LEAPING (SFL) ALGORITHM is a new heuristic ALGORITHM that its ability is not proved for solving the multimodal problems. This paper proposes a niche method for SFL. Several benchmark problems are considered for testing the robustness and effectiveness of the proposed method over the results available in the literature. The results show that the proposed method performs well.

Wireless sensor networks consist of thousands of sensor nodes with limited energy. Energy efficiency is a fundamental challenge issue for wireless sensor networks. Clustering sensor nodes in separate categories and exchanging information through clusters is one of the ways to improve energy consumption. This paper presents a new cluster-based routing protocol called SFLCFBA. The proposed protocol biologically uses fast and effective search features inspired by the Shuffled FROG LEAPING ALGORITHM, which acts based on the FROG food behavior to cluster sensor nodes. The proposed protocol also uses fuzzy logic to calculate the node fitness, based on the two criteria of distance to the sink and the remaining energy of the sensor node or power of battery level. IEEE 802. 15. 4 Protocol and NODIC Protocol with the proposed methodology and OPNET Simulator were simulation and the results in terms of energy consumption, end to end delay, signal to noise ratio, the success property data and throughput were compared with each other. The results of the simulation showed that the proposed method outperforms the IEEE 802. 15. 4 Protocol and NODIC Protocol due to the use of the criteria listed.