Linear Decreasing Weight Particle Swarm Research Articles

An Improved Deep Neural Network Model of Intelligent Vehicle Dynamics via Linear Decreasing Weight Particle Swarm and Invasive Weed Optimization Algorithms.

We propose an improved DNN modeling method based on two optimization algorithms, namely the linear decreasing weight particle swarm optimization (LDWPSO) algorithm and invasive weed optimization (IWO) algorithm, for predicting vehicle’s longitudinal-lateral responses. The proposed improved method can restrain the solutions of weight matrices and bias matrices from falling into a local optimum while training the DNN model. First, dynamic simulations for a vehicle are performed based on an efficient semirecursive multibody model for real-time data acquisition. Next, the vehicle data are processed and used to train and test the improved DNN model. The vehicle responses, which are obtained from the LDWPSO-DNN and IWO-DNN models, are compared with the DNN and multibody results. The comparative results show that the LDWPSO-DNN and IWO-DNN models predict accurate longitudinal-lateral responses in real-time without falling into a local optimum. The improved DNN model based on optimization algorithms can be employed for real-time simulation and preview control in intelligent vehicles.

Applying the linear particle swarm optimization to a serial multi-echelon inventory model

In the aspect of supply chain management; responding to the accurate needs of customers and effectively reducing the total costs are of significance activities for companies to achieve their competitive edges and to have opportunities to gain large amounts of advantages in the current highly competitive global supply chain management environment. Therefore, this article provides a serial multi-echelon integrated just-in-time (JIT) model based on uncertain delivery lead time and quality unreliability (SMEIJI model) considerations. Hence, we will apply the particle swarm optimization (PSO) as a method to result an improved solution solving a mixed nonlinear integer problem. Based on our fitting parameter settings, the final result show that the linear decreasing weight particle swarm optimization (LDW-PSO) will be efficiently performable and have a primary solution in solving the multi-echelon inventory problem.