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.

Several industrial products such as moulds, dies, engine block, automotive parts, etc., require machining of a large number of holes. Similarly, applications like boiler plates, food-business processing separator's, printed circuit boards, drum and trammel screens, etc., consist of a matrix of a large number of holes. Many machining operations, such as drilling, enlargement, tapping, or reaming, are needed to achieve the final sizes of individual holes, resulting in a variety of possible sequences to complete the hole-making operations. The major issue involved in hole-making operations is the tool travel time. It is often vital to determine the optimal sequence of operations so that the overall processing cost of hole-making operations can be minimized. In this work, thus an attempt is made to minimize the total tool travel of hole-making operations by using a relatively new optimization ALGORITHM known as modified SHUFFLED FROG LEAPING for the determination of the optimal sequence of operations. Modification is made in the present SHUFFLED FROG-LEAPING ALGORITHM by using three parameters with their positive values in order to widen the search capability of the existing ALGORITHM. This paper considers three case studies of a rectangular matrix of holes to explain the proposed procedure. The outcomes of optimization with a modified SHUFFLED FROG-LEAPING ALGORITHM are compared to those obtained with the genetic ALGORITHM and the ant colony ALGORITHM. Additionally, the higher dimensional problem of 20 x 20 rectangular matrix of holes is considered in this work.

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.

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.