Showing 2 results for Particle Swarm Optimization.
Mehdi Mahnam , Seyyed Mohammad Taghi Fatemi Ghomi ,
Volume 23, Issue 4 (11-2012)
Abstract
Fuzzy time series have been developed during the last decade to improve the forecast accuracy. Many algorithms have been applied in this approach of forecasting such as high order time invariant fuzzy time series. In this paper, we present a hybrid algorithm to deal with the forecasting problem based on time variant fuzzy time series and particle swarm optimization algorithm, as a highly efficient and a new evolutionary computation technique inspired by birds’ flight and communication behaviors. The proposed algorithm determines the length of each interval in the universe of discourse and degree of membership values, simultaneously. Two numerical data sets are selected to illustrate the proposed method and compare the forecasting accuracy with four fuzzy time series methods. The results indicate that the proposed algorithm satisfactorily competes well with similar approaches.
Ramin Giahi, Reza Tavakkoli-Mogahddam,
Volume 25, Issue 1 (2-2014)
Abstract
Bus systems are unstable without considering any control. Thus, we are able to consider some control strategies to alleviate this problem. A holding control strategy is one commonly used real-time control strategy that can improve service quality. This paper develops a mathematical model for a holding control strategy. The objective of this model is to minimize the total cost related to passengers at any stop. To solve the model, particle swarm optimization (PSO) is proposed. The results of the numerical examples show that the additional total cost caused by service irregularity is reduced by 25% by applying the presented holding model to the given problem.
