A bus network design problem for Tin Shui Wai, a suburban residential area in Hong Kong, is investigated, which considers the bus services from the origins inside this suburban area to the ...destinations in the urban areas. The problem aims to improve the existing bus services by reducing the number of transfers and the total travel time of the users. This has been achieved by the proposed integrated solution method which can solve the route design and frequency setting problems simultaneously. In the proposed solution method, a genetic algorithm, which tackles the route design problem, is hybridized with a neighborhood search heuristic, which tackles the frequency setting problem. A new solution representation scheme and specific genetic operators are developed so that the genetic algorithm can search all possible route structures, rather than selecting routes from the predefined set. To avoid premature convergence, a diversity control mechanism is incorporated in the solution method based on a new definition of hamming distance. To illustrate the robustness and quality of solutions obtained, computational experiments are performed based on 1000 perturbed demand matrices. The
t-test results show that the design obtained by the proposed solution method is robust under demand uncertainty, and the design is better than both the current design and the design obtained by solving the route design problem and the frequency setting problem sequentially. Compared with the current bus network design, the proposed method can generate a design which can simultaneously reduce the number of transfers and total travel time at least by 20.9% and 22.7% respectively. Numerical studies are also performed to illustrate the effectiveness of the diversity control mechanism introduced and the effects of weights on the two objective values.
A novel algorithm is proposed for optimal extraction of GaN HEMT small-signal model parameters. The proposed Quantum Genetic Algorithm (QGA) exploits the superposition, entanglement and interference ...of quantum states, which solves the problems of high number of iterations and slow convergence when obtaining optimal solutions using Genetic Algorithms (GA). Meanwhile, it is solved that the Particle Swarm Optimisation (PSO) algorithm produces premature convergence and easily falls into the local optimum solution. In order to avoid the influence of distributed parasitic effects in large size devices under high-frequency conditions, a suitable frequency range is determined and combined with direct extraction techniques to determine the range of parameter values. The model parameter values are optimised step by step using QGA. In order to verify the superiority of QGA, QGA and PSO algorithms are both used to optimise GaN HEMT small-signal model parameters. By comparing the modelled S-parameter effects of the QGA and the PSO algorithm, it can be found that the QGA has better consistency with the measured data.
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•GA is used to optimize RBF, MLP and NARX to predict the SOC of HCCI engine.•The best MLP is a 25-20-20 network with 1 and 2 delays for inputs and feedback.•Optimal number of neurons ...and bandwidth of Gaussian kernel for RBF are 0.2 and 800.•The best NARX is a 2-layer network with 10 and 5 neurons in the 1st and 2nd layers.•Optimized NARX has the best performance and least computational cost.
The combustion process in Homogeneous Charge Compression Ignition Engines (HCCI) is one of the new methods of futuristic combustion technologies. Since there is no direct operator for the start of the combustion (SOC) of these engines, air-fuel mixture properties at the moment of entering the combustion chamber, specifies the ignition timing. In HCCI engines, the ignition timing is the most crucial factor in determining other engine operating characteristics such as power output, pollution, and fuel consumption. To control SOC, there should be an accurate predictive model based on the entering air-fuel mixture properties. The Artificial Neural Networks (ANN) approach can be considered as a solution with less computational costs than traditional physics-based modeling. In this investigation, a multi-input single-output model was developed for predicting the SOC of the HCCI engine for a wide range of engine operation. Three popular architectures namely the Nonlinear Autoregressive Network with Exogenous Inputs (NARXNET), Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) were used, for this purpose. The networks were trained using experimental data taken from a one-cylinder Ricardo engine. The network architecture was optimized using a Genetic Algorithm (GA) method. By using GA, the proposed networks also have the optimum network structures, improved model predictive behaviors, and simulation costs of the learning process. After optimization, the regression ratio between the outputs of MLP and the corresponding experimental data was increased from 0.8965 to 0.96166. This value was improved from 0.7623 to 0.83991 for RBF. By using GA, the time needed to train the NARX was reduced from 3.12 s to 0.46 s. By comparing the model predictions with the experimental data, it was shown that the selected neural network architectures are powerful approaches for non-linear modeling the SOC of the HCCI engine.
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•Genetic algorithm achieves new polymer designs with high bandgap and high glass transition temperature.•Machine learning prediction models assist rapid evaluation of fitness ...function.•Chemical fragments leading high performance of polymers are highlighted.
Data driven or machine learning (ML) based methods have been recently used in materials science to provide quick material property predictions. Although powerful and robust, these predictive models are still limited in terms of their applicability towards the design of materials with target property or performance objectives. Here, we employ a nature-mimicking optimization method, the genetic algorithm, in tandem with ML-based predictive models to design polymers that meet practically useful, but extreme, property criteria (i.e., glass transition temperature, Tg>500 K and bandgap, Eg>6 eV). Analogous to nature, the characteristic properties of a polymer are assumed to be determined by the constituting types and sequence of chemical building blocks (or fragments) in the monomer unit. Evolution of polymers by natural operations of crossover, mutation, and selection over 100 generations leads to creation of 132 new (as compared to 4 already known cases) and chemically unique polymers with high Tg and Eg. Chemical guidelines on what fragments make up polymers with extreme thermal and electrical performance metrics have been selected and revealed by the algorithm. The approach presented here is general and can be extended to design polymers with different property objectives.
Genetic algorithm (GA) is an important and effective method to solve the optimization problem, which has been widely used in most practical applications. However, the premature convergence of GA has ...unexpected effect on the algorithm’s performance, the main reason is that the evolution of outstanding individuals multiply rapidly will lead to premature loss of population’s diversity. To solve the above problem, a method to qualify the population diversity and similarity between adjacent generations is proposed. Then, according to the evaluation of population diversity and the fitness of individual, the adaptive adjustment of crossover and mutation probability is realized. The results of several benchmark functions show that the proposed algorithm can search the optimal solution of almost all benchmark functions and effectively maintain the diversity of the population. Compared with the existing algorithms, it has greatly improved the convergence speed and the global optimal solution.
There are emerging transportation problems known as the Traveling Salesman Problem with Drone (TSPD) and the Flying Sidekick Traveling Salesman Problem (FSTSP) that involve using a drone in ...conjunction with a truck for package delivery. This study presents a hybrid genetic algorithm for solving TSPD and FSTSP by incorporating local search and dynamic programming. Similar algorithms exist in the literature. Our algorithm, however, considers more sophisticated chromosomes and less computationally complex dynamic programming to enable broader exploration by the genetic algorithm and efficient exploitation through dynamic programming and local search. The key contribution of this paper is the discovery of how decision-making processes for solving TSPD and FSTSP should be divided among the layers of genetic algorithm, dynamic programming, and local search. In particular, our genetic algorithm generates the truck and the drone sequences separately and encodes them in a type-aware chromosome, wherein each customer is assigned to either the truck or the drone. We apply local search to each chromosome, which is decoded by dynamic programming for fitness evaluation. Our new algorithm is shown to outperform existing algorithms on most benchmark instances in both quality and time. Our algorithms found the new best solutions for 538 TSPD instances out of 920 and 74 FSTSP instances out of 132.
•We present a novel hybrid genetic search algorithm for solving the combined truck-drone routing problems.•The proposed algorithm consists of dynamic programming, local searches, and a genetic algorithm.•Our algorithm outperforms existing algorithms.•Our algorithm finds the new best solutions for 538 TSPD and 74 FSTSP instances.
Support Vector Machines, one of the new techniques for pattern classification, have been widely used in many application areas. The kernel parameters setting for SVM in a training process impacts on ...the classification accuracy. Feature selection is another factor that impacts classification accuracy. The objective of this research is to simultaneously optimize the parameters and feature subset without degrading the SVM classification accuracy. We present a genetic algorithm approach for feature selection and parameters optimization to solve this kind of problem.
We tried several real-world datasets using the proposed GA-based approach and the Grid algorithm, a traditional method of performing parameters searching. Compared with the Grid algorithm, our proposed GA-based approach significantly improves the classification accuracy and has fewer input features for support vector machines.
Current courier networks in metropolitan areas are characterised by utilising fleets of vans that perform collection and distribution routes independently. This results in long stem distances, low ...load factors and high environmental costs. Joint delivery systems have the potential to reduce the distances of pick-up and drop-off routes. In this context, parcel lockers can be utilised to transfer goods between vans, electrical vehicles and bikes to improve the efficiency and sustainability of courier networks. This paper presents a model for designing joint delivery networks in urban areas by utilising parcel lockers. This model has a two-level structure: the lower level dealing with multi-depot capacitated vehicle routing problems (MDCVRP) for a set of depots and lockers whilst the upper level being a (minimum-cost) parcel network flow problem (PNFP) considering goods delivered between depots and lockers and the selection of lockers' positions and sizes. A hybrid algorithm integrating a Genetic Algorithm with the Lin-Kernighan Heuristic has been developed. The GA focuses on finding solutions for the PNFP. Once the paths of parcel flow are determined, the LKH optimises vehicle flow. This paper is the first to consider the use of parcel lockers for business-to-business networks in the form of MDCVRP.
Feature selection plays a key role in reducing the dimensionality of a feature vector by discarding redundant and irrelevant ones. In this paper, a Genetic Algorithm-based hierarchical feature ...selection (HFS) model has been designed to optimize the local and global features extracted from each of the handwritten word images under consideration. In this context, two recently developed feature descriptors based on
shape
and
texture
of the word images have been taken into account. Experimentation is conducted on an in-house dataset of 12,000 handwritten word samples written in Bangla script. This database comprises names of 80 popular cities of West Bengal, a state of India. Proposed model not only reduces the feature dimension by nearly 28%, but also enhances the performance of the handwritten word recognition (HWR) technique by 1.28% over the recognition performance obtained with unreduced feature set. Moreover, the proposed HFS-based HWR system performs better in comparison with some recently developed methods on the present dataset.
•This paper addresses Multiobjective Vehicle Routing Problem with Time Windows.•A Nondominated Sorting Genetic Algorithm II approach is proposed for the problem.•The proposed approach uses ...objective-specific greedy variation operators.•Computational results demonstrate the effectiveness of the proposed approach.
Vehicle routing problem with time windows (VRPTW) is a pivotal problem in logistics domain as it possesses multiobjective characteristics in real-world applications. Literature contains a general multiobjective VRPTW (MOVRPTW) with five objectives along with MOVRPTW benchmark instances that are derived from real-world data. In this paper, we have proposed a nondominated sorting genetic algorithm II (NSGA-II) based approach with objective-specific variation operators to address the MOVRPTW. In the proposed NSGA-II approach, the crossover and mutation operators are designed by exploiting the problem characteristics as well as the attributes of each objective. The performance of the proposed approach is evaluated on the standard benchmark instances of the problem and compared with the state-of-the-art approach available in literature. The computational results demonstrate the superiority of our approach over the state-of-the-art approach for the MOVRPTW.
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