scholarly journals OPTIMASI TITIK INTERKONEKSI DISTRIBUTED GENER-ATION (PLTM MUARA) GUNA MEMINIMALKAN RUGI – RUGI DAYA MENGGUNAKAN METODE ARTIFICIAL BEE COLONY (ABC) PADA PENYULANG PANJI

2018 ◽  
Vol 5 (2) ◽  
pp. 224
Author(s):  
I Made Arya Supartana ◽  
Rukmi Sari Hartati ◽  
I Wayan Sukerayasa
Keyword(s):  
Power Plant ◽  
Distributed Generation ◽  
Artificial Bee Colony ◽  
Optimization Technique ◽  
Power Losses ◽  
Small Power ◽  
Bee Colony ◽  
Before And After ◽  
Abc Method ◽  
Initial Power

Distributed Generation (DG) is a small power plant that can increase realibility, voltage profil, and reduce power losses in distribution network. DG interconnection locations that are less suitable can in/crease power losses. A possibility that can be done to reduce these power losses is by optimizing DG interconnection points. Optimization of the DG interconnection points on the feeder of Panji aims to minimize power losses, where the initial power losses occur at 48.2 kW. Optimization technique is using the Artificial Bee Colony (ABC) method with losses after simulation at 32.263 kW. When compared with the conditions before and after optimization there is a differ-ence in power losses of 16.035 kW, to a decrease in power losses of 33.2% from the previous loss.

scholarly journals A Hybrid Evolutionary Approach to Design Off-Grid Electrification Projects with Distributed Generation

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
J. Avilés ◽  
J. C. Mayo-Maldonado ◽  
O. Micheloud
Keyword(s):  
Distributed Generation ◽  
Network Topology ◽  
Optimization Technique ◽  
Optimal Number ◽  
Optimal Placement ◽  
Evolutionary Approach ◽  
Power Losses ◽  
Network Configuration ◽  
Optimal Network ◽  
Particle Swarm Optimization Technique

A hybrid evolutionary approach is proposed to design off-grid electrification projects that require distributed generation (DG). The design of this type of systems can be considered as an NP-Hard combinatorial optimization problem; therefore, due to its complexity, the approach tackles the problem from two fronts: optimal network configuration and optimal placement of DG. The hybrid scheme is based on a particle swarm optimization technique (PSO) and a genetic algorithm (GA) improved with a heuristic mutation operator. The GA-PSO scheme permits finding the optimal network topology, the optimal number, and capacity of the generation units, as well as their best location. Furthermore, the algorithm must design the system under power quality requirements, network radiality, and geographical constraints. The approach uses GPS coordinates as input data and develops a network topology from scratch, driven by overall costs and power losses minimization. Finally, the proposed algorithm is described in detail and real applications are discussed, from which satisfactory results were obtained.

scholarly journals MODIFIKASI BARU ALGORITMA KOLONI LEBAH BUATAN UNTUK MASALAH OPTIMASI GLOBAL

2018 ◽  
Vol 10 (1) ◽  
pp. 17
Author(s):  
Nursyiva Irsalinda ◽  
Sugiyarto Surono
Keyword(s):  
Food Source ◽  
Artificial Bee Colony ◽  
Selection Process ◽  
Optimization Technique ◽  
Food Sources ◽  
Abc Algorithm ◽  
Average Value ◽  
Bee Colony ◽  
Objective Function Values ◽  
Better Than

Artificial Bee Colony (ABC) algorithm is one of metaheuristic optimization technique based on population. This algorithm mimicking honey bee swarm to find the best food source. ABC algorithm consist of four phases: initialization phase, employed bee phase, onlooker bee phase and scout bee phase. This study modify the onlooker bee phase in selection process to find the neighborhood food source. Not all food sources obtained are randomly sought the neighborhood as in ABC algorithm. Food sources are selected by comparing their objective function values. The food sources that have value lower than average value in that iteration will be chosen by onlooker bee to get the better food source. In this study the modification of this algorithm is called New Modification of Artificial Bee Colony Algorithm (MB-ABC). MB-ABC was applied to 4 Benchmark functions. The results show that MB-ABC algorithm better than ABC algorithm

Artificial Bee Colony-Based Associative Classifier for Healthcare Data Diagnosis

2021 ◽  
pp. 237-253
Author(s):  
M. Nandhini ◽  
S. N. Sivanandam ◽  
S. Renugadevi
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Artificial Bee Colony ◽  
Optimization Technique ◽  
Rule Mining ◽  
Healthcare Data ◽  
Bee Colony ◽  
Small Set ◽  
Significant Class

Data mining is likely to explore hidden patterns from the huge quantity of data and provides a way of analyzing and categorizing the data. Associative classification (AC) is an integration of two data mining tasks, association rule mining, and classification which is used to classify the unknown data. Though association rule mining techniques are successfully utilized to construct classifiers, it lacks in generating a small set of significant class association rules (CARs) to build an accurate associative classifier. In this work, an attempt is made to generate significant CARs using Artificial Bee Colony (ABC) algorithm, an optimization technique to construct an efficient associative classifier. Associative classifier, thus built using ABC discovered CARs achieve high prognostic accurateness and interestingness value. Promising results were provided by the ABC based AC when experiments were conducted using health care datasets from the UCI machine learning repository.

scholarly journals Discrete and Continuous Optimization Based on Hierarchical Artificial Bee Colony Optimizer

2014 ◽  
Vol 2014 ◽  
pp. 1-20 ◽  
Author(s):  
Lianbo Ma ◽  
Kunyuan Hu ◽  
Yunlong Zhu ◽  
Ben Niu ◽  
Hanning Chen ◽  
...  
Keyword(s):  
Artificial Bee Colony ◽  
Complete Solution ◽  
Continuous Optimization ◽  
Benchmark Problems ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization ◽  
Discrete And Continuous Optimization ◽  
Upper Level ◽  
Abc Method

This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization (HABC), to tackle complex high-dimensional problems. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operator is applied to enhance the global search ability between species. Experiments are conducted on a set of 20 continuous and discrete benchmark problems. The experimental results demonstrate remarkable performance of the HABC algorithm when compared with other six evolutionary algorithms.

scholarly journals Integrated monte carlo-evolutionary programming technique for distributed generation studies in distribution system

2019 ◽  
Vol 8 (3) ◽  
pp. 978-984
Author(s):  
Nur Ainna Shakinah Abas ◽  
Ismail Musirin ◽  
Shahrizal Jelani ◽  
Mohd Helmi Mansor ◽  
Naeem M. S. Honnoon ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Distributed Generation ◽  
Distribution System ◽  
Optimization Technique ◽  
Evolutionary Programming ◽  
Test System ◽  
Voltage Profile ◽  
Power Losses ◽  
Programming Technique ◽  
Distributed Generations

This paper presents the optimal multiple distributed generations (MDGs) installation for improving the voltage profile and minimizing power losses of distribution system using the integrated monte-carlo evolutionary programming (EP). EP was used as the optimization technique while monte carlo simulation is used to find the random number of locations of MDGs. This involved the testing of the proposed technique on IEEE 69-bus distribution test system. It is found that the proposed approach successfully solved the MDGs installation problem by reducing the power losses and improving the minimum voltage of the distribution system.

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