Feature selection methods are used to identify and remove irrelevant and redundant attributes from the original feature vector that do not have much contribution to enhance the performance of a ...predictive model. Meta-heuristic feature selection algorithms, used as a solution to this problem, need to have a good trade-off between exploitation and exploration of the search space. Genetic Algorithm (GA), a popular meta-heuristic algorithm, lacks exploitation capability, which in turn affects the local search ability of the algorithm. Basically, GA uses mutation operation to take care of exploitation which has certain limitations. As a result, GA gets stuck in local optima. To encounter this problem, in the present work, we have intelligently blended the Great Deluge Algorithm (GDA), a local search algorithm, with GA. Here GDA is used in place of mutation operation of the GA. Application of GDA yields a high degree of exploitation through the use of perturbation of candidate solutions. The proposed method is named as Deluge based Genetic Algorithm (DGA). We have applied the DGA on 15 publicly available standard datasets taken from the UCI dataset repository. To show the classifier independent nature of the proposed feature selection method, we have used 3 different classifiers namely K-Nearest Neighbour (KNN), Multi-layer Perceptron (MLP) and Support Vector Machine (SVM). Comparison of DGA has been performed with other contemporary algorithms like the basic version of GA, Particle Swarm Optimisation (PSO), Simulated Annealing (SA) and Histogram based Multi-Objective GA (HMOGA). From the comparison results, it has been observed that DGA performs much better than others in most of the cases. Thus, our main contributions in this paper are introduction of a new variant of GA for FS which uses GDA to strengthen its exploitational ability and application of the proposed method on 15 well-known UCI datasets using KNN, MLP and SVM classifiers.
Cloud computing is a formidable paradigm to provide resources for handling the services from Industrial Internet of Things (IIoT), such as meteorological industry. Generally, the meteorological ...services, with complex interdependent logics, are modeled as workflows. When any of the computing nodes for hosting the meteorological workflows fail, all sorts of consequences (e.g., data loss, makespan enlargement, performance degradation, etc.) could arise. Thus recovering the failed tasks as well as optimizing the makespan and the load balance of the computing nodes is still a critical challenge. To address this challenge, a dynamic resource provisioning method (DRPM) with fault tolerance for the data-intensive meteorological workflows is proposed in this article. Technically, the Virtual Layer 2 (VL2) network topology is exploited to build meteorological cloud infrastructure. Then, the nondominated sorting genetic algorithm II (NSGA-II) is employed to minimize the makespan and improve the load balance. Finally, comprehensive experimental analysis of DRPM are proceeded.
► We model a Power System Stabilizer to minimize the overshoot of low frequency oscillations. ► Different Evolutionary techniques are applied for the design process. ► Three test systems are ...considered and the simulation results are compared wth the conventional techniques.
This paper presents the design and implementation of Power System Stabilizers in a multimachine power system based on innovative evolutionary algorithm overtly as Breeder Genetic Algorithm with Adaptive Mutation. For the analysis purpose a Conventional Power System Stabilizer was also designed and implemented in the same system. Simulation results on multimachine systems subjected to small perturbation and three phase fault radiates the effectiveness and robustness of the proposed Power System Stabilizers over a wide range of operating conditions and system configurations. The results have shown that Adaptive Mutation Breeder Genetic Algorithms are well suited for optimal tuning of Power System Stabilizers and they work better than conventional Genetic Algorithm, since they have been designed to work on continuous domain. This proposed Power System Stabilizer is demonstrated through a weakly connected three multi-machine test systems.
A Genetic Algorithm for a Green Vehicle Routing Problem de Oliveira da Costa, Paulo Roberto; Mauceri, Stefano; Carroll, Paula ...
Electronic notes in discrete mathematics,
February 2018, 2018-02-00, Letnik:
64
Journal Article
Odprti dostop
We propose a Genetic Algorithm (GA) to address a Green Vehicle Routing Problem (G-VRP). Unlike classic formulations of the VRP, this study aims to minimise the CO2 emissions per route. The G-VRP is ...of interest to policy makers who wish to reduce greenhouse gas emissions. The GA is tested on a suite of benchmark, and real-world instances which include road speed and gradient data. Our solution approach incorporates elements of local and population search heuristics. Solutions are compared with routes currently used by drivers in a courier company. Reductions in emissions are achieved without incurring additional operational costs.
A new feature selection approach that is based on the integration of a genetic algorithm and particle swarm optimization is proposed. The overall accuracy of a support vector machine classifier on ...validation samples is used as a fitness value. The new approach is carried out on the well-known Indian Pines hyperspectral data set. Results confirm that the new approach is able to automatically select the most informative features in terms of classification accuracy within an acceptable CPU processing time without requiring the number of desired features to be set a priori by users. Furthermore, the usefulness of the proposed method is also tested for road detection. Results confirm that the proposed method is capable of discriminating between road and background pixels and performs better than the other approaches used for comparison in terms of performance metrics.
Scheduling is an important decision-making problem in production planning and the resulting decisions have a direct impact on reducing waste, including energy and idle capacity. Batch scheduling ...problems occur in various industries from automotive to food and energy. This paper introduces the parallel p-batch scheduling problem with batch delivery, content-dependent loading/unloading times and energy-aware objective function. The problem has been motivated by a real system used for freezing products in a food processing company. A mixed-integer linear programming model (MILP) has been developed and explained through a numerical example. As it is not practical to solve large-size instances via a mathematical model, the discrete differential evolution algorithm has been improved (iDDE) and hybridised with the genetic algorithm (GA). A release-oriented vector generation procedure and a heuristic batch formation mechanism have been developed to efficiently solve the problem. The performance of the proposed approach (iDDEGA) has been compared with CPLEX, iDDE and GA through a comprehensive computational study. A case study was conducted based on real data collected from the freezing process of the company, which also verified the practical use and advantages of the proposed methodology.
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Materials informatics employs machine learning (ML) models to map the relationship between a targeted property and various materials descriptors, providing new avenues to accelerate ...the discovery of new materials. However, the possible ML models and materials descriptors are numerous, and a rational recipe to rapidly choose the best combination of the two is needed. In the present study, we propose a systematic framework that utilizes a genetic algorithm (GA) to efficiently select the ML model and materials descriptors from a huge number of alternatives and demonstrated its efficiency on two phase formation problems in high entropy alloys (HEAs). The optimized classification model allows an accuracy for identifying solid-solution and non-solid-solution HEAs to be up to 88.7% and further for recognizing body-centered-cubic (BCC), face-centered-cubic (FCC), and dual-phase HEAs to reach 91.3%. Furthermore, by employing an active learning approach, several HEAs with largest classification uncertainties were selected, experimentally synthesized and phase-identified, and augmented to the initial dataset to iteratively improve the ML model. The method serves as a general algorithm to select materials descriptors and ML models for various material problems including classification and optimization of targeted properties.
In this paper, based on the lithium-ion battery parameter identification by Immune Genetic Algorithm, An Extended Kalman Particle Filter approach is proposed to estimate the state of charge. Immune ...Genetic Algorithm was designed to identify the second-order equivalent circuit model parameters of lithium-ion battery. Combining Extended Kalman Filter with Particle Filter, Extended Kalman Particle Filter is designed to estimate the lithium-ion battery state of charge. This method is especially for the nonlinear and time variant lithium-ion battery system, and it can improve the calculation accuracy and stability of State of Charge estimation. An Immune Genetic Extended Kalman Particle Filter approach is validated by some experimental scenarios on the test bench. Experimental results show that Immune Genetic Extended Kalman Particle Filter has better adaptability, robustness and accuracy than Extended Kalman Filter under both UDDS and ECE conditions. Both theoretical and experimental results illustrate that Extended Kalman Particle Filter is a good candidate to estimate the lithium-ion battery state of charge.
•An immune genetic algorithm is designed to be firstly applied into paraments identification of lithium-ion battery model.•A noise variance estimation algorithm is firstly utilized to improve extended Kalman particle filter.•An immune-genetic-based extended Kalman particle filter approach is developed to estimate SOC for lithium-ion battery.•The IG-EKPF approach is a good candidate of SOC estimation based on the validation of engineering practice.
In the present work, the Inconel 625 material is utilized for processing on a wire electrical discharge machine. The zinc-coated brass wire is used as a tool electrode. The Taguchi mixed orthogonal ...L27 experimental design is developed to perform the experiments. The parameter was induced on normal and cryogenic material such as pulse on time (Ton), pulse off time (Toff), discharge current (Ip), wire feed (Wf), and wire tension (WT). The characteristics responses i.e., material removal rate (MRR), surface roughness (Ra), and overcut (OC) were analyzed. The genetic algorithm (GA) was applied for the evaluation and measuring of fact, which determined its performance, and predicted facts were considered for particle swarm optimization (PSO) technique multiple objective problems. After the comparison, the facts predicted were analyzed for the response, which revealed the surface integrity and accuracy on the machined surface. The solution implied that applied PSO gives highly significant results on optimal setting parameters of WEDM machining. The 0.419 μm surface roughness has been obtained by a cryogenic process as compared to normal (0.880 μm). The material removal rate for the cryogenic process was found to have decreased in PSO by 21.18 % and in GA by 2.78 % in comparison to the normal process. The obtained results give the utmost measure for the suitability of time reduction and cost-cutting in productivity rate.
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.
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