A modified particle swarm optimization for economic dispatch with non-smooth cost functions

This paper presents a new approach to economic dispatch (ED) problems with non-smooth cost functions using a particle swarm optimization (PSO) technique. The practical ED problems have non-smooth cost functions with equality and inequality constraints, which makes the problem of finding the global optimum difficult when using any mathematical approaches. Since, standard PSO may converge at the early stage, in this paper, a modified PSO (MPSO) mechanism is suggested to deal with the equality and inequality constraints in the ED problems. To validate the results obtained by MPSO, standard particle swarm optimization (PSO) and guaranteed convergence particle swarm optimization (GCPSO) are applied for comparison. Also, the results obtained by MPSO, PSO and GCPSO are compared with the previous approaches reported in the literature. The results show that the MPSO produces optimal or nearly optimal solutions for the study systems.     

Multi agent architecture for dynamic incremental process planning in the flexible manufacturing system

Due to rapid changes of markets and pressures of competitions, industries are adopting their production ways to support diversity of customer’s needs and increase of new product developments. This paper deals with development of an agent-based architecture of dynamic systems for process planning in the manufacturing systems. In consideration of alternative manufacturing processes and machine tools, the process plans and the schedules of the manufacturing resources are generated incrementally and dynamically. A previously proposed negotiation protocol is customized and improved to generate suitable process plans for the target products real-timely and dynamically, based on the alternative manufacturing processes. The alternative manufacturing processes are presented by the process plan networks and the suitable process plans are searched and generated to cope with both the dynamic status and disturbances of the manufacturing systems. We initiatively combine the heuristic search algorithms of the process plan networks with the negotiation protocols, in order to generate suitable process plans.     

Genetic-based minimum classification error mapping for accurate identifying Peer-to-Peer applications in the internet traffic

In this paper, we propose a hybrid approach using genetic algorithm and neural networks to classify Peer-to-Peer (P2P) traffic in IP networks. We first compute the minimum classification error (MCE) matrix using genetic algorithm. The MCE matrix is then used during the pre-processing step to map the original dataset into a new space. The mapped data set is then fed to three different classifiers: distance-based, K-Nearest Neighbors, and neural networks classifiers. We measure three different indexes, namely mutual information, Dunn, and SD to evaluate the extent of separation of the data points before and after mapping is performed. The experimental results demonstrate that with the proposed mapping scheme we achieve, on average, 8% higher accuracy in classification of the P2P traffic compare to the previous solutions. Moreover, the genetic-based MCE matrix increases the classification accuracy more than what the basic MCE does.     

Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances

Uniaxial compressive strength (UCS) of rock is crucial for any type of projects constructed in/on rock mass. The test that is conducted to measure the UCS of rock is expensive, time consuming and having sample restriction. For this reason, the UCS of rock may be estimated using simple rock tests such as point load index (I _s(50)), Schmidt hammer (R _n) and p-wave velocity (V _p) tests. To estimate the UCS of granitic rock as a function of relevant rock properties like R _n, p-wave and I _s(50), the rock cores were collected from the face of the Pahang–Selangor fresh water tunnel in Malaysia. Afterwards, 124 samples are prepared and tested in accordance with relevant standards and the dataset is obtained. Further an established dataset is used for estimating the UCS of rock via three-nonlinear prediction tools, namely non-linear multiple regression (NLMR), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). After conducting the mentioned models, considering several performance indices including coefficient of determination (R ²), variance account for and root mean squared error and also using simple ranking procedure, the models were examined and the best prediction model was selected. It is concluded that the R ² equal to 0.951 for testing dataset suggests the superiority of the ANFIS model, while these values are 0.651 and 0.886 for NLMR and ANN techniques, respectively. The results pointed out that the ANFIS model can be used for predicting UCS of rocks with higher capacity in comparison with others. However, the developed model may be useful at a preliminary stage of design; it should be used with caution and only for the specified rock types.     

Extreme learning machine assessment for estimating sediment transport in open channels

The minimum velocity required to prevent sediment deposition in open channels is examined in this study. The parameters affecting transport are first determined and then categorized into different dimensionless groups, including “movement,” “transport,” “sediment,” “transport mode,” and “flow resistance.” Six different models are presented to identify the effect of each of these parameters. The feed-forward neural network (FFNN) is used to predict the densimetric Froude number (Fr) and the extreme learning machine (ELM) algorithm is utilized to train it. The results of this algorithm are compared with back propagation (BP), genetic programming (GP) and existing sediment transport equations. The results indicate that FFNN-ELM produced better results than FNN-BP, GP and existing sediment transport methods in both training (RMSE = 0.26 and MARE = 0.052) and testing (RMSE = 0.121 and MARE = 0.023). Moreover, the performance of FFNN-ELM is examined for different pipe diameters.     

Design of sliding mode and model reference adaptive control strategies for multivariable tape transport mechanism: a performance comparison

This paper presents sliding mode control and model reference adaptive control strategies for the tape transport mechanism. A nonlinear multivariable MIMO model of the process, consisting of take-up and supply reel servos for tape tension control and capstan servo for speed control is considered. The sliding mode control is applied for the nonlinear dynamic model of the process, while the model reference adaptive control deals with the linearized one. Moreover, in order to associate with the realistic model of system, design of controllers is accomplished with respect to parametric uncertainties. It is shown that both control strategies can guarantee asymptotic stability of the closed-loop system and tracking of desired outputs with the appropriate pace in the presence of uncertainties. Finally, simulation results demonstrate the effectiveness of the proposed controllers.     

Multi-stage genetic programming: A new strategy to nonlinear system modeling

This paper presents a new multi-stage genetic programming (MSGP) strategy for modeling nonlinear systems. The proposed strategy is based on incorporating the individual effect of predictor variables and the interactions among them to provide more accurate simulations. According to the MSGP strategy, an efficient formulation for a problem comprises different terms. In the first stage of the MSGP-based analysis, the output variable is formulated in terms of an influencing variable. Thereafter, the error between the actual and the predicted value is formulated in terms of a new variable. Finally, the interaction term is derived by formulating the difference between the actual values and the values predicted by the individually developed terms. The capabilities of MSGP are illustrated by applying it to the formulation of different complex engineering problems. The problems analyzed herein include the following: (i) simulation of pH neutralization process, (ii) prediction of surface roughness in end milling, and (iii) classification of soil liquefaction conditions. The validity of the proposed strategy is confirmed by applying the derived models to the parts of the experimental results that were not included in the analyses. Further, the external validation of the models is verified using several statistical criteria recommended by other researchers. The MSGP-based solutions are capable of effectively simulating the nonlinear behavior of the investigated systems. The results of MSGP are found to be more accurate than those of standard GP and artificial neural network-based models.     

Resource-efficient routing and scheduling of time-constrained streaming communication on networks-on-chip

Network-on-chip-based multiprocessor systems-on-chip are considered as future embedded systems platforms. One of the steps in mapping an application onto such a parallel platform involves scheduling the communication on the network-on-chip. This paper presents different scheduling strategies that minimize resource usage by exploiting all scheduling freedom offered by networks-on-chip. It also introduces a technique to take the dynamism in applications into account when scheduling the communication of an application on the network-on-chip while minimizing the resource usage. Our experiments show that resource-utilization is improved when compared to existing techniques.