Dynamic clustering using combinatorial particle swarm optimization

Combinatorial Particle Swarm Optimization (CPSO) is a relatively recent technique for solving combinatorial optimization problems. CPSO has been used in different applications, e.g., partitional clustering and project scheduling problems, and it has shown a very good performance. In partitional clustering problem, CPSO needs to determine the number of clusters in advance. However, in many clustering problems, the correct number of clusters is unknown, and it is usually impossible to estimate. In this paper, an improved version, called CPSOII, is proposed as a dynamic clustering algorithm, which automatically finds the best number of clusters and simultaneously categorizes data objects. CPSOII uses a renumbering procedure as a preprocessing step and several extended PSO operators to increase population diversity and remove redundant particles. Using the renumbering procedure increases the diversity of population, speed of convergence and quality of solutions. For performance evaluation, we have examined CPSOII using both artificial and real data. Experimental results show that CPSOII is very effective, robust and can solve clustering problems successfully with both known and unknown number of clusters. Comparing the obtained results from CPSOII with CPSO and other clustering techniques such as KCPSO, CGA and K-means reveals that CPSOII yields promising results. For example, it improves 9.26 % of the value of DBI criterion for Hepato data set.     

A reliable and power efficient flow-control method to eliminate crosstalk faults in network-on-chips

This paper proposes a power-efficient flow-control method to tackle the problem of crosstalk faults in Network-on-Chips (NoCs). The method, called FRR (Flit Reordering/Rotation), combines three coding mechanisms to entirely eliminate opposite direction transitions (OD transitions) as the source of crosstalk faults in NoC communication channels. The first mechanism, called flit-reordering, reorders flits of every packet to find a flit sequence which produces the lowest number of OD transitions on NoC channels. The second mechanism called flit-rotation, logically rotates the content of every flit of the packet with respect to previously sent flit to achieve even more reduction in the number of OD transitions. Finally, the third mechanism called flit-insertion, investigates flits of the packet to find the OD transitions which are not removed by first and second mechanisms. This mechanism inserts null-flits between the required flits to completely eliminate appearance of OD transitions on NoC channels. Evaluation of FRR method is done in two ways: (1) VHDL-based simulations are carried out for 16- and 32-bit channels when maximum reorderings and maximum rotations in the first and second mechanisms are limited to 2, 4, and 8. (2) An analytical model is developed to calculate and compare the expected number of OD transitions in an unprotected NoC as well as an FRR-enabled NoC. Both simulation and analytical results confirm that the FRR method completely removes crosstalk faults from NoC channels. In addition, VHDL simulations show that the FRR method provides a remarkable power saving, since the method reduces the number of transitions in NoC channels by at least 32.8%.     

Free overfalls in flat-based circular and U-shaped channels

This paper presents a theoretical model based on the free vortex theorem that is capable of predicting the pressure head distribution at the brink of free overfalls in open channels. This approach is coupled with the momentum equations to obtain the end-depth-ratio (EDR) from which the discharge can be estimated. In order to illustrate its flexibility, the theory is successfully applied to flat-based circular and U-shaped channels. Using previous experimental data, the proposed method is validated.     

Developing an expert system for predicting alluvial channel geometry using ANN

Cross section geometry of stable alluvial channels usually is estimated by simple inaccurate empirical equations, because of the complexity of the phenomena and unknown physical processes of regime channels. So, the main purpose of this study is to evaluate the potential of simulating regime channel treatments using artificial neural networks (ANNs). The process of training and testing of this new model is done using a set of available published filed data (371 data numbers). Several statistical and graphical criterions are used to check the accuracy of the model in comparison with previous empirical equations. The multilayer perceptron (MLP) artificial neural network was used to construct the simulation model based on the training data using back-propagation algorithm. The results show a considerably better performance of the ANN model over the available empirical or rational equations. The constructed ANN models can almost perfectly simulate the width, depth and slope of alluvial regime channels, which clearly describes the dominant geometrical parameters of alluvial rivers. The results demonstrate that the ANN can precisely simulate the regime channel geometry, while the empirical, regression or rational equations can’t do this. The presented methodology in this paper is a new approach in establishing alluvial regime channel relations and predicting cross section geometry of alluvial rivers also it can be used to design stable irrigation and water conveyance channels.     

A hybrid genetic algorithm for multi-depot homogenous locomotive assignment with time windows

This paper presents a hybrid genetic algorithm to solve a multi-depot homogenous locomotive assignment problem with time windows. The locomotive assignment problem is to assign a set of homogeneous locomotives locating in a set of dispersed depots to a set of pre-schedules trains that are supposed to be serviced in pre-specified hard/soft time windows. A mathematical model is presented, using vehicle routing problem with time windows (VRPTW) for formulation of the problem. A cluster-first, route-second approach is used to inform the multi-depot locomotive assignment to a set of single depot problems and after that we solve each problem independently. Each single depot problem is solved heuristically by a hybrid genetic algorithm that in which Push Forward Insertion Heuristic (PFIH) is used to determine the initial solution and λ-interchange mechanism is used for neighborhood search and improving method. A medium sized numerical example with different scenarios is presented and examined to more clarification of the approach as well as to check capabilities of the model and algorithm. Also some of the results are compared with the solutions produced by branch & bound technique to determine validity and quality of the model. The experiments with a set of 15 completely random generated instance problems indicate that this algorithm is efficient and solves the problem in a polynomial time.     

Design of robust adaptive controller and feedback error learning for rehabilitation in Parkinson's disease: a simulation study

Deep brain stimulation (DBS) is an efficient therapy to control movement disorders of Parkinson''s tremor. Stimulation of one area of basal ganglia (BG) by DBS with no feedback is the prevalent opinion. Reduction of additional stimulatory signal delivered to the brain is the advantage of using feedback. This results in reduction of side effects caused by the excessive stimulation intensity. In fact, the stimulatory intensity of controllers is decreased proportional to reduction of hand tremor. The objective of this study is to design a new controller structure to decrease three indicators: (i) the hand tremor; (ii) the level of delivered stimulation in disease condition; and (iii) the ratio of the level of delivered stimulation in health condition to disease condition. For this purpose, the authors offer a new closed‐loop control structure to stimulate two areas of BG simultaneously. One area (STN: subthalamic nucleus) is stimulated by an adaptive controller with feedback error learning. The other area (GPi: globus pallidus internal) is stimulated by a partial state feedback (PSF) controller. Considering the three indicators, the results show that, stimulating two areas simultaneously leads to better performance compared with stimulating one area only. It is shown that both PSF and adaptive controllers are robust regarding system parameter uncertainties. In addition, a method is proposed to update the parameters of the BG model in real time. As a result, the parameters of the controllers can be updated based on the new parameters of the BG model.Inspec keywords: adaptive control, medical control systems, diseases, medical disorders, patient rehabilitation, neurophysiology, surgeryOther keywords: robust adaptive controller design, feedback error learning, Parkinson''s disease rehabilitation, deep brain stimulation, DBS, movement disorders, Parkinson''s tremor, stimulatory signal Reduction, side effects, excessive stimulation intensity, hand tremor, controller structure, closed‐loop control structure, subthalamic nucleus, adaptive controller, partial state feedback controller, robust regarding system parameter uncertainties, BG model     

A method for designing power supply chain networks accounting for failure scenarios and preventive maintenance

A power grid is vulnerable and failures are inevitable. Failures decrease the power supply with an adverse effect on meeting the demand for electricity. Therefore, there is a need for a method to design power grid networks that result in the least possible disruption to the power supply when a failure occurs. In the literature, the focus has been on the design of the generation system without considering the transmission system or failures in the transmission system. Since power grids are integrated generation and transmission systems, each system will affect the other, so both generation and transmission systems need to be considered, as they are in this article. Methods developed for the structural modelling and analysis of supply chains are shown to be useful. The focus in this article is on describing a method using the supply chain construct for designing power grids that are relatively insensitive to failure in the integrated generation and transmission system. The efficacy of the method is illustrated using data from the Tehran Regional Electric Company. One of the findings is that targeted failures have a higher impact on decreasing the performance of the power grid than random failures. However, the focus is on the method rather than the results per se.     

Comparison between hybridized algorithm of GA–SA and ABC,GA, DE and PSO for vertical-handover in heterogeneous wireless networks

Genetic algorithms (GAs) and simulated annealing (SA) have emerged as leading methods for search and optimization problems in heterogeneous wireless networks. In this paradigm, various access technologies need to be interconnected; thus, vertical handovers are necessary for seamless mobility. In this paper, the hybrid algorithm for real-time vertical handover using different objective functions has been presented to find the optimal network to connect with a good quality of service in accordance with the user’s preferences. As it is, the characteristics of the current mobile devices recommend using fast and efficient algorithms to provide solutions near to real-time. These constraints have moved us to develop intelligent algorithms that avoid slow and massive computations. This was to, specifically, solve two major problems in GA optimization, i.e. premature convergence and slow convergence rate, and the facilitation of simulated annealing in the merging populations phase of the search. The hybrid algorithm was expected to improve on the pure GA in two ways, i.e., improved solutions for a given number of evaluations, and more stability over many runs. This paper compares the formulation and results of four recent optimization algorithms: artificial bee colony (ABC), genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO). Moreover, a cost function is used to sustain the desired QoS during the transition between networks, which is measured in terms of the bandwidth, BER, ABR, SNR, and monetary cost. Simulation results indicated that choosing the SA rules would minimize the cost function and the GA–SA algorithm could decrease the number of unnecessary handovers, and thereby prevent the ‘Ping-Pong’ effect.