Particle Swarm Harmony Search Algorithm Research Articles

Collaborative Optimization of Stop Schedule Plan and Ticket Allotment for the Intercity Train

As regards the ticket allotment issue of the intercity passenger corridor designed for different train grades, the matching relationship between the ticket allotment and the passenger flow demand is studied. The passenger flow conversion equation which is based on the collaborative optimization of the intercity train stop schedule plan and ticket allotment is established. Then the mathematical model aiming at the maximum revenue of intercity train system and the highest satisfaction from the passengers is established. The particle swarm harmony search algorithm is designed to solve the model. The example verifies the effectiveness of the model and algorithm, which indicates that, through the collaborative optimization of the stop schedule plan and ticket allotment for different grades intercity trains, the sectional utilization rate of the train can be improved; meanwhile, the optimum matching between the intercity train revenue and the passenger satisfaction can be realized.

Structural damage detection using artificial neural networks and least square support vector machine with particle swarm harmony search algorithm

The study presented herein compares the performance of structural damage detection using artificial neural networks (ANNs) and least square support vector machines (LS–SMVs). Structural response signals under ambient vibration are processed according to wavelet energy spectrum for feature extraction. The feature vectors are used as inputs to both classifiers based on ANNs and LS–SVMs. LS–SVM parameters along with the selection of input features are optimised using particle swarm harmony search (PSHS) algorithm with a distance evaluation fitness function. The PSHS that has been introduced in this paper is a new hybrid meta–heuristic algorithm for improving the accuracy and the convergence rate of harmony search (HS) algorithm. The effectiveness of different feature extraction methods and different optimisation algorithms are investigated. This investigation shows that although performance of both classifiers is improved by employing PSHS–based selection, for most cases considered, the classification accuracy of LS–SVM is better than ANN. Furthermore, the results demonstrate the efficiency and the robustness of PSHS.