Papers by Keyword: Bat Algorithm

Abstract: The genetic bat algorithm is being actively investigated for its application in the complex multi-objective product selective disassembly sequence planning problem. To broaden the scope of the search space and enhance the overall search efficiency, the traditional bat algorithm has undergone discretization, incorporating a cross-mutation mechanism into the construction of the fitness function model. To assess the efficacy of this novel approach, an industrial mechanical arm is utilized as a representative case study. Upon comparison with the traditional bat algorithm, the proposed method exhibits shorter convergence times across a range of population sizes, thus validating its effectiveness.

Abstract: A new method for premature ventricular contraction (PVC) detection and classification is presented. The proposed algorithm is constituted of two principal phases: the features extraction and reduction phase and the optimized classification phase. In the first phase, the discrete cosine transform (DCT) and the continuous wavelet transform (CWT) are applied on each ECG beat to generate an augmented features vector. For the optimized classification phase, the radial basis function (RBF) neural network classifier is trained and optimized by the bat algorithm. For the aim of performances evaluation of the proposed method, the MIT-BIH arrhythmia database has been used. Consequently, the BAT-RBF classifier yielded an overall sensitivity of 95,2% and an accuracy of 98,2%, confirming clearly the competitiveness of the proposed method compared to some recent and powerful algorithms.

Abstract: In the last two decades, developing countries are facing heavy increase in diabetes among their population that is leading to other severe diseases. Hence, there is a great need to develop some effective prediction methods to prevent diabetes. In this paper an attempt has been made to develop Firefly-BAT (FFBAT) optimized Rule Based Fuzzy Logic (RBFL) prediction algorithm for diabetes. The algorithm has two main steps. First, Locality Preserving Projections (LPP) algorithm is used for feature reduction and then classification of diabetes is done by means of RBFL classifier. LPP algorithm has been used to identify the related attributes and then the fuzzy rules are produced from RBFL. The rules are optimized using FFBAT algorithm. Next, the fuzzy system is designed with the help of optimized fuzzy rules and membership functions that will classify the diabetes data. FFBAT is the optimization algorithm which combines the features of BAT and Firefly (FF) optimization techniques. The experiment analysis shows that the RBFL-FFBAT algorithm outperforms the existing approaches.

Abstract: For the standard BP algorithm usually has the limitations of slow convergence and local extreme values, a new method to adjust weights of BP network was proposed based on the bat algorithm of the global optimization ability and the strong convergence. The new algorithm was based on the weight adjustments of error back propagation of BP algorithm and the weight and threshold of BP network modification using the bats position update. The new algorithm can not only use the bat ability of global optimization, but also contain the feature of error back propagation of BP algorithm. The new algorithm was used in simulation test of two typical functions, results of which were analyzed and compared with that of basic BP algorithm and PSO-BP algorithm. Experimental results show that the new algorithm has faster convergence speed and higher convergence accuracy, and improved the learning ability and generalization ability of BP network. The performances of the new algorithm were superior to that of other 2 kinds of BP network algorithm.

Abstract: Multiloop fractional order PID controller is tuned using Bat algorithm for two interacting conical tank process. Two interacting conical tank process is modelled using mass balance equations. Two Interacting Conical Tank process is a complex system involving tedious interaction. Straight forward multiloop PID controller design involves various steps to design the controller. Due to easy implementation and quick convergence, Bat algorithm is used in recent past for solving continuous non-linear optimization problems to achieve global optimal solution. Bat algorithm, a swarm intelligence technique will be attempted to tune the multiloop fractional order PID controller for two interacting conical tank process. The multi objective optimized multiloop fractional PID controller is tested for tracking, disturbance rejection for minimum Integral time absolute error.

Abstract: Permutation flow shop scheduling problem is a complicated global optimum problem. In this paper, according to the characteristics of permutation flow shop scheduling problem, an improved bat algorithm was used to solve permutation flow shop scheduling problem. The algorithm was experimented and the experimental results show that the improved bat algorithm has better feasibility and validity for solving permutation flow shop scheduling problem.

Abstract: Angular error is a major concern for the tool engineers during the taper cutting operation in wire electrical discharge machining (WEDM) process. Due to the complexity and non-linearity involved in the process, it is difficult to obtain good functional relationship between responses and process parameters. To address this issue, the present study proposes artificial neural network (ANN) model to determine the relationship between input parameters and output response. Bayesian regularization is adopted for selection of optimum network architecture because of its ability to fix number of network parameters irrespective of network size. Levenberg-Marquardt algorithm has been used to train the ANN model and the resulting network has good generalization capability minimizing the chance of over fitting. The model is developed based on the data obtained from a laboratory scale experimental set up. A set of six important input parameters such as part thickness, taper angle, pulse duration, discharge current, wire speed and wire tension is chosen to study the tapering operation in WEDM. Finally, a recent meta-heuristic approach known as Bat algorithm is used to suggest the optimum parametric combination for minimizing the angular error during taper cutting process in WEDM.

Abstract: In view of the deficiency of the basic back-propagation (BP) algorithm based on steepest descent method. Bat algorithm (BA) in intelligent optimization is introduced into the training process of feed-forward neural networks, capturing the optimal solution of the objective function with a small population size and less number of iterations, and a prediction model based on BA feed-forward neural network (BA-NN) is given. By the empirical study of stock price prediction in Sany Heavy Industry, the results show that this method has advantages of frequency tuning and dynamic control of exploration and exploitation by automatic switching to intensive exploitation if necessary.

Abstract: In order to solve the problems of bat algorithm including low convergence accuracy, slow convergence velocity and easily falling into local optimization, this paper presents an improved bat algorithm based on differential evolution algorithm. The mutation, crossover and selection mechanism of differential evolution algorithm is introduced into bat algorithm, the bat algorithm lack of mutation mechanism has the variation mechanism, so as to enhance the diversity of bat algorithm, the population can avoid falling into local optimum, which enhances the ability of global optimization for bat algorithm. The Simulation results of three standard benchmark functions show that the improved algorithm can greatly improve the convergence precision, convergence speed and robustness, and can effectively discourage the premature convergence.

Abstract: This paper is intended to demonstrate the use of normalized radial basis function (NRBF) network and Bat Algorithm (BA) for size optimization of a mechanical part under static loading. The data needed for developing the NRBF model is generated simulating a parameterized CAD model in ANSYS Workbench 14.5. Plausible input data for the CAD model is created using Latin Hypercube Sampling (LHS) method. A torque arm is considered to proof the concept. The comparison between the result obtained from the proposed method and the solution from ANSYS Workbench itself shows that, the NRBF-BA model is indeed effective in providing a reasonable solution for a moderately complex problem.