Showing papers in "Engineering Applications of Artificial Intelligence in 2010"

TL;DR: A hybrid ARIMA and neural network model is proposed that is capable of exploiting the strengths of traditional time series approaches and artificial neural networks to provide a robust modeling framework capable of capturing the nonlinear nature of the complex time series and thus producing more accurate predictions.

TL;DR: Experiments results show that the genetic algorithm, the particle swarm optimization and the differential evolution are much better in terms of precision, robustness and time convergence than the ant colony, simulated annealing and tabu search.

TL;DR: Experimental results showed that using reinforcement learning based method with the vehicle dynamic parameters feature outperforms the rest algorithms, and adding the other two features could further improve the prediction accuracy.

TL;DR: The proposed algorithm for the solution of the vehicle routing problem, the hybrid particle swarm optimization (HybPSO), combines a particle swarm optimized algorithm, the multiple phase neighborhood search-greedy randomized adaptive search procedure (MPNS-GRASP), the expanding neighborhood search (ENS) strategy and a path relinking (PR) strategy.

TL;DR: The results show that the MA-ANN has a significant improvement on the forecast accuracy compared with the original four models, mainly due to the improvement of correlation between inputs and outputs depending on the moving average operation.

TL;DR: A model-based approach for human gait recognition, which is based on analyzing the leg and arm movements, and the main focus of this paper is on increasing the discrimination capability of the model through extra features produced from the motion of the arms.

TL;DR: Five newly developed versions of differential evolution (DE) called modified DE versions are applied and the results are compared with the classical DE algorithm and with five more algorithms available in the literature; the numerical results show that the modified DE algorithms outperforms or perform at par with the other algorithms.

TL;DR: A new, real-coded genetic algorithm approach called 'RCGA-ELM' to select the optimal number of hidden neurons, input weights and bias values which results in better performance and two new genetic operators called 'network based operator' and 'weight based operator" are proposed to find a compact network with higher generalization performance.

TL;DR: A general mixed integer programming model of VRP-SPDTW, which contained some classical vehicle routing problems as special cases, and an improved differential evolution algorithm (IDE) for solving this problem.

TL;DR: An ABC algorithm that simulates the foraging behavior of honey bee swarm for model parameter extraction is proposed and the performance comparison of both the algorithms are compared with respect to computational time and the quality of solutions (QoS).

TL;DR: The paper develops a self-tuning PID control scheme with an application to ABS via combinations of fuzzy and genetic algorithms (GAs) to minimize the stopping distance, while keeping the slip ratio of the tires within desired range.

TL;DR: The main purpose of the paper is to present the integrated knowledge management model for the construction industry as well as system architecture and system of the Knowledge Based Decision Support System for Construction Projects Management (KDSS-CPM) which the authors of this paper have developed.

TL;DR: It is shown that the results of 5-objective optimization include those of 2- objective optimization and, therefore, provide more choices for optimal design of a vehicle vibration model.

TL;DR: This paper presents a study investigating the potential of genetic algorithms (GAs) and particle swarm optimization (PSO) to determine the optimum value of degree of attraction, using a hybrid method combining the strengths of PSO with GAs, simultaneously.

TL;DR: Experiments with simulated data show that correct detection rates over 99% and correct localization rates over 92% can be achieved using this approach, which represents a major improvement over the state of the art reference method.

TL;DR: A fusion approach to determine inverse kinematics solutions of a six degree of freedom serial robot makes use of radial basis function neural network for prediction of incremental joint angles which in turn are transformed into absolute joint angles with the assistance of forward kinematic relations.

TL;DR: PSO with quantum infusion (PSO-QI) is used in identification of benchmark IIR systems and a real world problem in power systems and the results show that PSO- QI has better performance over these algorithms in identifying dynamical systems.

TL;DR: A new hybrid evolutionary algorithm is proposed for solving the distribution feeder reconfiguration (DFR) problem, called SAPSO-MSFLA, which can find optimal configuration of distribution network and guarantees to obtain the global optimization in minimum time.

TL;DR: The computational experiment shows that a multi-start evolutionary local search outperforms a GRASP/VND when applied to the multi-depot vehicle routing problem (MDVRP), that can be seen as a special case of the STTRPSD.

TL;DR: In this study, acoustic emission signals were first collected during grinding operations, next processed by autoregressive modeling or discrete wavelet decomposition for feature extraction, and then the best feature subsets are found by three different feature selection methods, including two proposed ant colony optimization (ACO)-based method and the famous sequential forward floating selection method.

TL;DR: The application of Particle Swarm Optimization (PSO) algorithm to solve both offline and online parameter estimation problem for bilinear systems is presented.

TL;DR: This paper investigates the task of multiagent reinforcement learning for control of traffic signals in two situations: agents act individually and agents can be ''tutored'', meaning that another agent with a broader sight will recommend a joint action.

TL;DR: In order to simplify the offline parameter estimation of induction motor, a method based on optimization using a particle swarm optimization (PSO) technique is presented.

TL;DR: Results from the inexact chance-constrained mixed-integer linear programming model indicate that a solution with a lower significance level would lead to a higher system reliability and system cost; conversely, a desire for reducing system cost would result in an increased risk of violating the constraints.

TL;DR: Multi-Objective optimization method based on adaptive simulated annealing genetic algorithm (ASAGA) is proposed to optimize the key components in HHV and shows that the proposed method effectively distinguishes theKey components' optimal parameters' position of HHV, enhances the performance and fuel consumption.

TL;DR: The experimental results show that these immigrants based genetic algorithms can quickly adapt to the environmental changes and produce high quality solutions following each change in the dynamic QoS multicast problem in MANETs.

TL;DR: A modified PSO (MPSO) mechanism is suggested to deal with the equality and inequality constraints in the ED problems and the results show that the MPSO produces optimal or nearly optimal solutions for the study systems.

TL;DR: A new approach based on computational intelligence (CI) techniques to find out the optimal placement and parameter setting of UPFC for enhancing power system security under single line contingencies (N-1 contingency) indicates that DE is an easy to use, fast, robust and powerful optimization technique compared with genetic algorithm (GA) and particle swarm optimization (PSO).

TL;DR: A new variant of Particle Swarm Optimization in which no a priori parameter tuning is necessary, and the algorithm's parameters are co-evolved with the particles, which demonstrates the scalability of the proposed variant to the realistic water distribution design problems, which are much larger.