shuffled frog leaping algorithm

The knapsack problem is known as a NP-hard problem. The knapsack or rucksack problem consists of determining, given a set of items, each of which has a cost and a value, the number of items included in a collection such that the total cost is less than a given cost and the total value is as large as possible. There is a dynamic programming solution for this problem called the 0-1 knapsack. The 0-1 knapsack problem restricts the number of individual items to zero or one. The shuffled frog-leaping algorithm (SFLA) has long been considered a meta-heuristic algorithm that derives from how frog groups search for food. SFLA can improve computing performance by letting all frogs participate in memetic evolution and access an excellent ability for global search by adding the self-variation behavior to the frog. This study represents an efficient solution for the 0-1 knapsack problem using SFLA. Regarding the parallel nature of most meta-heuristic algorithms, they can be successfully used for speedup. Since it is time-consuming to test all the cases when the problems become larger, Compute Unified Device Architecture (CUDA) is used to implement the solution in parallel. The results of simulating the 0-1 knapsack problem using SFLA on the CUDA platform show that the execution time for a parallel solution decreases as the population of frogs increases. For the 0-1 Knapsack problem, it is 252 times faster than the sequential solution.

This paper presents a new damping controller design based on fuzzy wavelet neural network (FWNN) to damp the multi-machine power system low frequency oscillations. The error between the desired system output and the output of control object is directly utilized to tune the network parameters. The orthogonal least square (OLS) algorithm is used to purify the wavelets for each rule and determine the number of fuzzy rules and network dimension. In this paper, Shuffled Frog Leaping Algorithm (SFLA) is proposed for learning of FWNN and to find the optimal values of the parameters of the FWNN damping controller. To illustrate the capability of the proposed approach, some numerical results are presented on a 2-area 4-machine and a 5-area-16-machine power system. To show the effectiveness and robustness of the designed controller, the case studies are tested under two conditions: applying a line-to-ground fault at a bus and applying a three phase fault at a bus. Furthermore, to make a comparison, the proposed approach is compared with a classical based method and a FWNN based genetic algorithm approach, which is adopted from literature, through eigenvalue analysis, time- domain simulation and some performance indices. The simulation results show the superiority and capability of the proposed FWNN damping controller.

This paper presents a new Modified Shuffled Frog Leaping Algorithm (MSFLA) applied to design simultaneous coordinated tuning of damping controllers to damp the power system low frequency oscillations. For this, a new frog leaping rule is proposed to improve the local exploration and performance of the original SFLA and the genetic mutation operator is employed for new frog generation instead of random frog generation to improve the performance and quicker algorithm convergence. In order to verify the effectiveness of the proposed method, a 2-area-4-machine and a 5-area-16-machine power system are considered which two power system stabilizers (PSSs) are designed coordinately for the first system and one PSS for a generator and one supplementary controller for a Static Var Compensator (SVC) are designed simultaneously for the second system. To show the effectiveness of the designed controllers, study systems are tested under two different operating conditions and simulation studies are presented.