scholarly journals Penentuan Depot Plastik Menggunakan Metode MDVRP

2020 ◽  
Vol 18 (1) ◽  
pp. 36-45
Author(s):  
Dody Chandrahadinata ◽  
Dedi Sa’dudin Taptajani ◽  
Rian Wibawa
Keyword(s):  
Genetic Algorithm ◽  
Nearest Neighbor

Penelitian ini dilakukan berdasarkan dari masalah distribusi yang dialami perusahaan, dimana lokasi perusahaan yang jauh dengan retailer dan tidak ada rute yang jelas pada saat proses distribusi. Tujuan dari penelitian ini adalah untuk mengetahui lokasi pendistribusian plastik, lokasi sebaran depot plastik, dan rute optimal pendistribusian didaerah Garut. Variabel utama penelitian ini adalah jarak antar retailer plastik. Metode penelitian yang digunakan pada kali ini adalah metode MDVRP. Tahap penelitian terdiri dari Clustering yaitu dengan menggunakan metode nearest neighbor, routing dengan menggunakan Chebishev’s Theorism dan Scheduling menggunakan metode Genetic Algorithm. Hasil yang diperoleh adalah jumlah depot dengan batasan jarak dan lokasi optimal berdasarkan jarak, serta rute distribusi dari tiap depot dan rute distribusi dari wholeseller ke depot. Penelitian ini dapat dikembangkan untuk penelitian selanjutnya mengenai MDVRP yang dapat dilakukan dengan metode yang berbeda pada tiap prosesnya serta dengan perbedaan batasan penelitian yang dilakukan baik dari jumlah retailer, beban angkut, ataupun dengan penambahan waktu.

scholarly journals Analysis of gabor filter based features with PCA and GA for the detection of drusen in fundus images

2018 ◽  
Vol 7 (1) ◽  
pp. 115
Author(s):  
Sheela N. ◽  
Basavaraj L.
Keyword(s):  
Genetic Algorithm ◽  
Nearest Neighbor ◽  
Gabor Filter ◽  
Age Related Macular Degeneration ◽  
Misclassification Rate ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Automated Method ◽  
Age Related ◽  
Predictive Rate

Human eye can be affected by different types of diseases. Age-Related Macular Degeneration (AMD) is one of the such diseases, and it mainly occurs after 50 years of age. This disease is characterized by the occurrence of yellow spots called as Drusen. In this work, an automated method for the detection of drusen in Fundus image has been developed, and it has been tested on 70 images consisting of 30 normal images and 40 images with drusen. Performance of the Support Vector Machine (SVM) and K Nearest Neighbor (KNN) classifier has been evaluated using Data's reduction using Principle Component Analysis (PCA) and Data's selection using Genetic Algorithm (GA).Performance evaluation has been done in terms of accuracy, sensitivity, specificity, misclassification rate, positive predictive rate, negative predictive rate and Youden’s Index. The proposed method has achieved highest accuracy of 98.7% when data selection using Genetic Algorithm has been applied.

scholarly journals An Innovative Genetic Algorithm for the Quantum Circuit Compilation Problem

2019 ◽  
Vol 33 ◽  
pp. 7707-7714
Author(s):  
Riccardo Rasconi ◽  
Angelo Oddi
Keyword(s):  
Genetic Algorithm ◽  
Nearest Neighbor ◽  
Quantum Algorithms ◽  
Quantum Circuit ◽  
Quantum Circuits ◽  
Heuristic Function ◽  
Circuit Realization ◽  
Benchmark Instances ◽  
Technological Limitations ◽  
Selection Of

Quantum Computing represents the next big step towards speed boost in computation, which promises major breakthroughs in several disciplines including Artificial Intelligence. This paper investigates the performance of a genetic algorithm to optimize the realization (compilation) of nearest-neighbor compliant quantum circuits. Currrent technological limitations (e.g., decoherence effect) impose that the overall duration (makespan) of the quantum circuit realization be minimized, and therefore the makespanminimization problem of compiling quantum algorithms on present or future quantum machines is dragging increasing attention in the AI community. In our genetic algorithm, a solution is built utilizing a novel chromosome encoding where each gene controls the iterative selection of a quantum gate to be inserted in the solution, over a lexicographic double-key ranking returned by a heuristic function recently published in the literature.Our algorithm has been tested on a set of quantum circuit benchmark instances of increasing sizes available from the recent literature. We demonstrate that our genetic approach obtains very encouraging results that outperform the solutions obtained in previous research against the same benchmark, succeeding in significantly improving the makespan values for a great number of instances.

Concurrent Societies Based on Genetic Algorithm and Particle Swarm Optimization

2010 ◽  
Vol 14 (1) ◽  
pp. 110-118
Author(s):  
Hrvoje Markovic ◽  
◽  
Fangyan Dong ◽  
Kaoru Hirota
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Nearest Neighbor ◽  
Optimization Problems ◽  
Fitness Function ◽  
Particle Swarm ◽  
Absolute Error ◽  
Population Based ◽  
Test Problems ◽  
Swarm Optimization

A parallel multi-population based metaheuristic optimization framework, called Concurrent Societies, inspired by human intellectual evolution, is proposed. It uses population based metaheuristics to evolve its populations, and fitness function approximations as representations of knowledge. By utilizing iteratively refined approximations it reduces the number of required evaluations and, as a byproduct, it produces models of the fitness function. The proposed framework is implemented as two Concurrent Societies: one based on genetic algorithm and one based on particle swarm optimization both using k -nearest neighbor regression as fitness approximation. The performance is evaluated on 10 standard test problems and compared to other commonly used metaheuristics. Results show that the usage of the framework considerably increases efficiency (by a factor of 7.6 to 977) and effectiveness (absolute error reduced by more than few orders of magnitude). The proposed framework is intended for optimization problems with expensive fitness functions, such as optimization in design and interactive optimization.

scholarly journals Nearest Neighbor–Genetic Algorithm for Downscaling of Climate Change Data from GCMs

2016 ◽  
Vol 55 (3) ◽  
pp. 773-789
Author(s):  
Soojun Kim ◽  
Jaewon Kwak ◽  
Hung Soo Kim ◽  
Younghun Jung ◽  
Gilho Kim
Keyword(s):  
Climate Change ◽  
Genetic Algorithm ◽  
General Circulation ◽  
Nearest Neighbor ◽  
Coupled Model ◽  
Temporal Correlation ◽  
General Circulation Models ◽  
Temperature Fields ◽  
Change Scenario ◽  
Limited Applicability

AbstractThe spatial and temporal resolution of readily available climate change projections from general circulation models (GCM) has limited applicability. Consequently, several downscaling methods have been developed. These methods predominantly focus on a single meteorological series at specific sites. Spatial and temporal correlation of the precipitation and temperature fields is important for hydrologic applications. This research uses a nearest neighbor–genetic algorithm (NN–GA) method to analyze the Namhan River basin in the Korean Peninsula. Using the simulation results of the CNRM-CM for the RCP 8.5 climate change scenario, archived in the fifth phase of the Coupled Model Intercomparison Project (CMIP5), the GCM projections are downscaled through the NN–GA. The NN–GA simulations reproduce the features of the observed series in terms of site statistics as well as across variables and sites.

Sign in / Sign up

Export Citation Format

Share Document