Parameter determination for a rail system simulator by an intelligent genetic algorithm

On-Demand electric vehicle (EV) systems are expected to have a significantly increasing role in the future of transportation systems in urban areas, to cope with the tremendous increases in urban population and decrease global carbon emissions. An inconvenience in Mobility-on-Demand Electric Vehicle (MoD-EV) systems is the need for some customers to charge EVs before reaching their destinations, which may cause delays in the trip time. Local and in-route charging options are available but the system operator needs to manage the charging assignments of the different EVs as charging all EVs locally may result in large delays. Given a connected system, we propose a routing strategy that aims to decrease these charging delays for the customers by sending the customers to different charging stations either at the pick-up location or nearby charging stations to minimize their average total trip time while respecting the charging constraints and avoid roads congestion. The problem is then formulated by modeling the routing between multiple MoD-EV stations as a multi-server queuing system with an objective of minimizing the expected overall trip duration for all customers, relative to their actual trip time without charging as a convex optimization problem. Optimal routing decisions and actual trip times are then derived analytically and simulation results show the significant gains of our proposed model as compared to shortest path and random routing schemes.

On-Demand electric vehicle (EV) systems are expected to play a significantly increasing role in near future urban transportation systems, to cope with the massive increases in urban population and reduce global carbon emissions. One inconvenience in MoD-EV systems is the need of some customers to perform in-routing charging, which may cause delays in the trip time. Moreover, the customer choice of which station to charge at is an operational issue for the MoD-EV service operator due to the different pricing for the charging at different stations. Given a connected system linking these EVs and charging stations to the operator, we propose a routing scheme that aims to reduce these inconveniences for both the customers and the operator. By modeling the routing problem between multiple MoD-EV stations with in-route charging as a multi-server queuing system, we formulate the joint problem of minimizing the average overall trip time for all customers, relative to their actual trip time without in-route charging, and the average overall cost of charging as a dual-objective stochastic convex optimization problem. Optimal routing decisions are then derived analytically for any arbitrary weighting of the two problem objectives. Simulation results show the significant merits of our proposed solution as compared to shortest time and random routing decisions. They also illustrate the trade-offs between the delay experienced by the customer and the charging cost for the operator.

Mobility-On-Demand (MoD) systems using electric vehicles (EVs) are expected to play a significantly increasing role in near future urban transportation systems, to cope with the massive increases in urban population and reduce carbon emissions. One inconvenience in MoD- EV systems is the need of some customers to perform in-routing charging. In this paper, we propose a routing scheme that aims to reduce the inconvenience of in-route charging in MoD-EV systems. By modeling the routing problem between multiple MoD-EV stations with in-route charging as a multi-server queuing system, we formulate our objective as a stochastic convex optimization problem that minimizes the average overall trip time for all customers relative to their actual trip time without in-route charging. Optimal routing decisions that achieve this goal are then derived analytically. Simulation results show the significant merits of our proposed solution as compared to shortest and random routing decisions.

A new genetic algorithm (GA), called intelligent GA, is proposed by the inclusion of a fractional factorial design in the crossover operator to determine the best combination of design variables. That is, in the proposed GA, the chromosomes of children are generated via an intelligent crossover process with factorial experiments after completing the selection of GA operators. This gene mating process differs from the traditional GA, where the child chromosome is generally generated via randomization. Accordingly, the traditional GA incorporating the technique of fractional factorial design into the crossover operator markedly enhances the performance in terms of evolutionary efficiency. Another GA, called Taguchi-GA, that incorporates the Taguchi orthogonal arrays into the crossover operator is also developed to compare the performance of the presented intelligent GA. Four representative test cases, including two multimodal functions, a nonlinear dynamic control function, and a profile fitting of a high lift airfoil are performed via the traditional GA, micro-GA, Taguchi-GA, and present GA to assess the capacity and efficiency of the proposed GA. The computational solutions of the convergence history and optimal objective function for the test cases demonstrate that the proposed intelligent GA outperforms the other three GAs. Moreover, the presented new intelligent GA and the other three GAs are applied to the aerodynamic optimization of two airplanes, namely, an advanced defense airplane and an F-16A fighter. The redesigned wing planform is obtained by computations made with an aerodynamic flow solver, and the present GA depicts the optimal swept angle, aspect ratio, and taper ratio. The drag distribution and convergence history show that the proposed intelligent GA has a high evolutionary efficiency when applied to practical engineering problems.

Generation Scheduling problem by Intelligent Genetic Algorithm

Unit commitment (UC) is one of the most important optimization tasks of the daily generation scheduling in power system. However, getting the global optimal solution to meet all the constraints is a comparatively tough problem. The binary coding and stochastic operators in traditional genetic algorithm (GA) are not suitable for solving large-scale UC problem. In this paper, the hybrid intelligent messy genetic algorithm (HIMGA) is proposed to solve the problem due to the characteristics of the daily generation scheduling in power system. The proposed algorithm, using operation status of unit as the genotype and combining heuristic self-adapted intelligent operators, is not only very simple but it also reduces the scale of the UC problems, improves the diversity of evolution population, enhances the searching efficiency and develops the convergence of the algorithm. The simulation results prove the correctness and validity of the proposed method.

ESVM: Evolutionary support vector machine for automatic feature selection and classification of microarray data

Optimization of Nine-point Five-state Speed Controller for Asynchronous Motor Based on Intelligent Genetic Algorithm

Machines and beam like structures used in various industries require continuous monitoring for the fault identification for ensuring uninterrupted service. Different non-destructive techniques (NDT) are generally used for this purpose, but they are costly and time consuming. Vibration based methods can be useful to detect cracks in structures using various artificial intelligence (AI) techniques. The modal parameters from the dynamic response of the structure are used for the purpose. In the current analysis, the vibration characteristics of a glass fiber reinforced composite cracked cantilever beam having different crack locations and depths have been studied. Numerical and finite element methods have been used to extract the diagnostic indices (natural frequencies, mode shapes) from cracked and intact beam structure. An intelligent Genetic Algorithm (GA) based controller has been designed to automate the fault identification and location process. Single point crossover and in some cases mutation procedure have been followed to find out the optimal solution from the search space. The controller has been trained in offline mode using the simulation and experimental results (initial data pool) under various healthy and faulty conditions of the structure. The outcome from the developed controller shows that the system could not only detect the cracks but also predict their locations and severities. Good agreement between the simulation, experimental and GA controller results confirms the effectiveness of the proposed controller.

This article compares the decision‐tree algorithm (C5.0), neural decision‐tree algorithm (NDT) and fuzzy decision‐tree algorithm (FIDs) for addressing reservoir operations regarding water supply during normal periods. The conventional decision‐tree algorithm, such as ID3 and C5.0, executes rapidly and can easily be translated into if‐then‐else rules. However, the C5.0 algorithm cannot discover dependencies among attributes and cannot treat the non‐axis‐parallel class boundaries of data. The basic concepts of the two algorithms presented are: (1) NDT algorithm combines the neural network technologies and conventional decision‐tree algorithm capabilities, and (2) FIDs algorithm extends to apply fuzzy sets for all attributes with membership function grades and generates a fuzzy decision tree. In order to obtain higher classification rates in FIDs, the flexible trapezoid fuzzy sets are employed to define membership functions. Furthermore, an intelligent genetic algorithm is utilized to optimize the large number of variables in fuzzy decision‐tree design. The applicability of the presented algorithms is demonstrated through a case study of the Shihmen Reservoir system. A network flow optimization model for analyzing long‐term supply demand is employed to generate the input‐output patterns. Findings show superior performance of the FIDs model in contrast with C5.0, NDT and current reservoir operating rules.

Wind Turbine airfoil was optimized by a new genetic method so that its performance increases. A new method was proposed to optimize thin airfoils for use in wind turbine rotors. It is a common practice in the classic methods based on the genetic algorithms for airfoil shape optimizations, to use the computational fluid dynamics solvers. The main disadvantage of this method is the considerable running time for these algorithms. This disadvantage was significantly reduced by using the new technique. Comparing the results of the proposed algorithm with the classic ones approves its higher performance and lower computation time. Another advantage of this method is the reduced number of the design variables needed for optimization by inverse design method. The proposed modifications on the genetic algorithm offer a higher performance wind turbine blades faster than classic algorithms simplicity. However, the classical direct approaches mostly perform a local search. The inverse approach The inverse approach is far more powerful since the designer has much more precise control over the final performance of the airfoil. However, while every airfoil shape produces a particular set of performance characteristics, not every set of performance characteristics can be used to generate a realistic airfoil shape. The designer must be aware of what is practical, the trade-offs required between different types of performance, and physical constraints. As a tradeoffs required between the different types of performance, and physical constraints. Consequently, classical design and optimizing methods mainly adopt trial and error approach and strongly rely on designer's future experience rather than the current needs. A global optimization method based on genetic algorithm or intelligent genetic algorithm is expected to shorten and simplify the iterative design process and improve the design output. A similar approach has been previously used for aerodynamic optimization of turbo machinery cascades (1), and very good results were obtained. Glaurt (2) neglected the drag of airfoil and offered a popular theory for optimizing the airfoil. This theory was further developed by Stewart (3) and improved by Miller (4). Wilson (5) offered a method base on calculating sensitivity of flow field parameters.

In recent years, much work has been done on implementing a variety of algorithms in nonlinear programming software. In this paper, we analyze the performance of several state-of-the-art optimization codes on large-scale nonlinear optimization problems. Extensive numerical results are presented on different classes of problems, and features of each code that make it efficient or inefficient for each class are examined.Keywordsinterior-point methodslarge-scale optimizationnonlinear programming

Solids-based carbon dioxide (CO2)adsorption, where CO2 is adsorbed under high pressure/low temperature and desorbed at low pressure/high temperature, is a low energy consumption capture technology with high carbon uptake efficiency. Chemical looping combustion (CLC), where a solid oxygen carrier (OC) transports oxygen between two high-temperature reactors – a fuel and an air reactor –, is a low energy penalty, fuel-flexible technology with inherent CO2 capture for power/heat and syngas production. In this work, state-of-the-art equation-oriented (EO) mathematical models of a fixed-bed (FxB) contactor for CO2 adsorption and moving-bed (MB) reactors for gas-, and solid-fueled CLC processes are developed. The solid-fueled MB model is a multi-zonal model for the CLC of coal. Large-scale nonlinear optimization problems are solved for simultaneous optimization of design and operating conditions for the CLC processes. Further, a Bayesian parameter estimation and model building framework is proposed and used to quantify the parametric and model form uncertainty of CO2 adsorption isotherm and CLC OC reduction kinetic models based on experimental data. The model form discrepancy is described by a set of orthogonal functions which are naturally suitable and efficient for uncertainty propagation to the unit/process model in which the discrepancy-augmented models are implemented. By using the joint posterior distribution of the model and discrepancy function parameters and inserting discrepancy functions in the isotherm and kinetic models coupled with the EO unit/process reactor models, the effect of the uncertainty inherent to bench-scale adsorption/kinetic models on key process variables of the large-scale systems are evaluated.

An application of neural network modeling is described for generating hypotheses about the relationships between response properties of neurons and information processing in the auditory system. The goal is to study response properties that are useful for extracting sound localization information from directionally selective spectral filtering provided by the pinna. For studying sound localization based on spectral cues provided by the pinna, a feedforward neural network model with a guaranteed level of fault tolerance is introduced. Fault tolerance and uniform fault tolerance in a neural network are formally defined and a method is described to ensure that the estimated network exhibits fault tolerance. The problem of estimating weights for such a network is formulated as a large-scale nonlinear optimization problem. Numerical experiments indicate that solutions with uniform fault tolerance exist for the pattern recognition problem considered. Solutions derived by introducing fault tolerance constraints have better generalization properties than solutions obtained via unconstrained back-propagation.

In power system any unpredictable variations of consumption load will cause fluctuation in frequency and the transferred power between the lines. Load frequency control is one of the main problems in the power system which synchronizes with automatic generation control. Checking the different factors that cause unbalance in power system is necessary and one of the important factors of them is the load that randomly varies. In this paper, a three area power system in deregulated environments with participation of synchronized generation of wind with Doubly Fed Induction Generator (DFIG), hydro and thermal power plants are studied. The load in this power system is considered as a random variable which periodically and randomly has frequency of 0.25 around nominal amount. Fluctuations are caused due to considering load as a random variable and using wind-powered turbine with DFIG in power system. To improve the response of power system, the optimal fuzzy logic, PID controller and FACTS devices will be used. Imperialist Competitive Algorithm (ICA) has been used for optimizing the parameters of a fuzzy logic controller. PID Controller parameters have been optimized with three intelligent algorithms, ICA, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) Algorithm and the results will be compared with each other. With comparison the result it is observed that in the case of using optimal fuzzy logic controller (OFLC) we have the fastest response with the minimum over shoot and under shoot, also in this case the transferred power between the areas is settled in the lowest value. The results show a significant reduction in losses in transmission lines.