Text Mining Research Based on Intelligent Computing in Information Retrieval System

With the popularity and rapid development of the Internet, web text information has rapidly grown as well. To address the key problem of text mining, text clustering is investigated in this study. The shuffled frog leaping algorithm as a new type of swarm intelligence optimization algorithm can be used to improve the performance of the K-means algorithm, but the shuffled frog leaping algorithm is influenced by its moving step length. On the basis of this information, the shuffled frog leaping algorithm is improved, and the K-means clustering algorithm based on the improved shuffled frog leaping algorithm is introduced. Experiment results show that the proposed scheme can enhance the ability of searching for the optimal initial clustering center and can effectively avoid instability in the clustering results of the K-means clustering algorithm. The proposed scheme also reduces the chances of the algorithm falling into the local optimum. The performance of the proposed clustering scheme is found to be better than that of the clustering algorithm based on the shuffled frog leaping algorithm

SHUFFLED FROG LEAPING ALGORITHM AND FEATURE SELECTION FOR IMPROVING RECOGNITION RATE OF PERSIAN HANDWRITTEN DIGITS CLASSIFIER

In this paper, Shuffled Frog Leaping Algorithm is used to improve the recognition rate of Persian handwritten digits. In proposed approach, the effective features in increasing the recognition rate are selected using the Binary Shuffled Frog Leaping Algorithm (BSFLA). By selecting the most suitable features from among all extracted features, the recognition rate is improved and computational costs are also decreased. The fitness function in BSFLA is the number of errors in the Fuzzy classifier which its minimum value is desired. The results indicate that Shuffled Frog Leaping algorithm (SFLA) is more efficien

Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL) optimization framework

Simplicity and flexibility of meta-heuristic optimization algorithms have attracted lots of attention in the field of optimization. Different optimization methods, however, hold algorithm-specific strengths and limitations, and selecting the best-performing algorithm for a specific problem is a tedious task. We introduce a new hybrid optimization framework, entitled Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL), which combines the strengths of different evolutionary algorithms (EAs) in a parallel computing scheme. SC-SAHEL explores performance of different EAs, such as the capability to escape local attractions, speed, convergence, etc., during population evolution as each individual EA suits differently to various response surfaces. The SC-SAHEL algorithm is benchmarked over 29 conceptual test functions, and a real-world hydropower reservoir model case study. Results show that the hybrid SC-SAHEL algorithm is rigorous and effective in finding global optimum for a majority of test cases, and that it is computationally efficient in comparison to algorithms with individual EA

Determination of optimal tool path in drilling operation using Modified Shuffled Frog Leaping Algorithm

Applications like boilerplates, food-industry processing separator, printed circuit boards, drum and trammel screens, etc. consists of a matrix of a large number of holes. The primary issue involved in hole-making operations is a tool travel time. It is often necessary to find the optimal sequence of operations so that the total processing cost of hole-making operations can be minimized. In this work, therefore an attempt is made to reduce the total tool travel of hole-making operations by applying a relatively new optimization algorithm known as modified shuffled frog leaping for determining the optimal sequence of operations. Modification is made in the existing shuffled frog-leaping algorithm by introducing three parameters with their positive values to widen the search capability of existing algorithms. A case study of the printed circuit board is considered in this work to demonstrate the proposed approach. Obtained results of optimization using modified shuffled frog leaping algorithm are compared with those obtained using particle swarm optimization, firefly algorithm and shortest path search algorithm

A Modified Shuffled Frog Leaping Algorithm for PAPR Reduction in OFDM Systems

© 2015 IEEE. Significant reduction of the peak-to-average power ratio (PAPR) is an implementation challenge in orthogonal frequency division multiplexing (OFDM) systems. One way to reduce PAPR is to apply a set of selected partial transmission sequence (PTS) to the transmit signals. However, PTS selection is a highly complex NP-hard problem and the computational complexity is very high when a large number of subcarriers are used in the OFDM system. In this paper, we propose a new heuristic PTS selection method, the modified chaos clonal shuffled frog leaping algorithm (MCCSFLA). MCCSFLA is inspired by natural clonal selection of a frog colony, it is based on the chaos theory. We also analyze MCCSFLA using the Markov chain theory and prove that the algorithm can converge to the global optimum. Simulation results show that the proposed algorithm achieves better PAPR reduction than using others genetic, quantum evolutionary and selective mapping algorithms. Furthermore, the proposed algorithm converges faster than the genetic and quantum evolutionary algorithms

Optimal sequence of hole-making operations using particle swarm optimization and modified shuffled frog leaping algorithm

Tool travel and tool switch scheduling are two major issues in hole-making operations. It is necessary to find the optimal sequence of operations to reduce the total processing cost of hole-making operations. In this work therefore, an attempt is made to use both a recently developed particle swarm optimisation algorithm and a shuffled frog leaping algorithm demonstrating in this way an example of plastic injection mould. The exact value of the minimum total processing cost is obtained by considering all possible combinations of sequences. The results obtained using particle swarm optimisation and shuffled frog leaping algorithm are compared with the minimum total processing cost results obtained by considering all possible combinations of sequences. It is observed that the results obtained using particle swarm optimisation and shuffled frog leaping algorithm are closer to the results of the minimum total processing cost obtained by considering all possible combinations of sequences presented in this work. This clearly shows that particle swarm optimisation and shuffled frog leaping algorithm can be effectively used in optimisation of large scale injection mould hole-making operations

Solving Travelling Salesman Problem by Using Optimization Algorithms

This paper presents the performances of different types of optimization techniques used in artificial intelligence (AI), these are Ant Colony Optimization (ACO), Improved Particle Swarm Optimization with a new operator (IPSO), Shuffled Frog Leaping Algorithms (SFLA) and modified shuffled frog leaping algorithm by using a crossover and mutation operators. They were used to solve the traveling salesman problem (TSP) which is one of the popular and classical route planning problems of research and it is considered  as one of the widely known of combinatorial optimization. Combinatorial optimization problems are usually simple to state but very difficult to solve. ACO, PSO, and SFLA are intelligent meta-heuristic optimization algorithms with strong ability to analyze the optimization problems and find the optimal solution. They were tested on benchmark problems from TSPLIB and the test results were compared with each other.Keywords: Ant colony optimization, shuffled frog leaping algorithms, travelling salesman problem, improved particle swarm optimizatio