scholarly journals IMPLEMENTASI ALGORITMA GREY WOLF OPTIMIZER (GWO) DI TOKO CITRA TANI JEMBER

2019 ◽  
Vol 19 (2) ◽  
pp. 65
Author(s):  
Vidiyanti Lestari ◽  
Ahmad Kamsyakawuni ◽  
Kiswara Agung Santoso
Keyword(s):  
Simplex Method ◽  
Primary Data ◽  
Grey Wolf Optimizer ◽  
Multiple Constraints ◽  
Grey Wolf ◽  
Population Parameters ◽  
Storage Media ◽  
Optimal Value ◽  
Comparison Of The Results ◽  
Two Parameters

Generally, optimization is defined as the process of determining the minimum or maximum value that depends on the function of the goal, even now there are many problems regarding optimization. One of them is the problem regarding the selection of goods to be included in a limited storage medium called Knapsack problem. Knapsack problems have different types and variations. This study will solve the problem of bounded knapsack multiple constraints by implementing the Grey Wolf Optimizer (GWO) algorithm. The problem of bounded knapsack multiple constraints has more than one subject with the items that are inserted into the dimension storage media can be partially or completely inserted, but the number of objects is limited. The aim of this study is to determine the results of using the Grey Wolf Optimizer (GWO) algorithm for solving the problem of multiple constraints bounded knapsack and compare the optimal solutions obtained by the simplex method using the Solver Add-In in Microsoft Excel. The data used in this study is primary data. There are two parameters to be tested, namely population parameters and maximum iteration. The test results of the two parameters show that the population parameters and maximum iterations have the same effect, where the greater the value of the population parameters and the maximum iteration, the results obtained are also getting closer to the optimal value. In addition, based on the results of the final experiment it is known that the comparison of the results of the GWO algorithm and the simplex method has a fairly small percentage deviation which indicates that the GWO algorithm produces results that are close to the optimal value. Keywords: GWO algorithm, Knapsack, Multiple Constraints Bounded Knapsack.

scholarly journals PENERAPAN DRAGONFLY OPTIMIZATION ALGORITHM (DOA) PADA PERMASALAHAN MULTIPLE CONSTRAINTS BOUNDED KNAPSACK

2019 ◽  
Vol 19 (1) ◽  
pp. 39
Author(s):  
Laylatul Febriana Nilasari ◽  
Kiswara Agung Santoso ◽  
Abduh Riski
Keyword(s):  
Knapsack Problem ◽  
Optimization Algorithm ◽  
Small Deviation ◽  
Knapsack Problems ◽  
Primary Data ◽  
Multiple Constraints ◽  
Population Parameters ◽  
Storage Media ◽  
Best Value ◽  
Almost All

Optimization is very useful in almost all fields in running a business effectively and efficiently to achieve the desired results. This study solves the problem of multiple constraints bounded knapsack by implementing DOA. The problem of multiple constraints bounded knapsack has more than ones constraint with objects that are entered into the storage media, the dimensions can be partially or completely included, but the number of objects is limited. The purpose of this study is to determine the results of using DOA to solve multiple constraits bounded knapsack and the effectiveness of DOA compared to the results of the Simplex method. The data used in this study are primary data. There are ten parameters to be tested, namely population parameters, maximum iteration, s, a, c, f, e and range. The trial results of the ten parameters show that the best value of the parameters is neither too large nor too small. If the best value is too large then the position of the dragonfly will be randomized so that it is not clear the position of the dragonfly and if it is too small the best value then the change is not visible. In addition, based on the results of the final experiment it can be seen that DOA is less effective in solving multiple constraints bounded knapsack problems, because of many experiments there is no solution similar to Simplex. DOA approach to optimal, seen from a small deviation. Keywords: DOA, Knapsack, Multiple constraints bounded knapsack problem.

scholarly journals Algoritma Elephant Herding Optimization: Permasalahan Multiple Constraints Knapsack 0-1

2018 ◽  
Vol 18 (1) ◽  
pp. 13
Author(s):  
Yulia Dewi Regita ◽  
Kiswara Agung Santoso ◽  
Ahmad Kamsyakawuni
Keyword(s):  
Knapsack Problem ◽  
Simplex Method ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Secondary Data ◽  
Multiple Constraints ◽  
Microsoft Excel ◽  
Storage Media ◽  
Simulation Results ◽  
Selection Of

Optimization problems are often found in everyday life, such as when determining goods to be a limited storage media. This causes the need for the selection of goods in order to obtain profits with the requirements met. This problem in mathematics is usually called a knapsack. Knapsack problem itself has several variations, in this study knapsack type used is multiple constraints knapsack 0-1 which is solved using the Elephant Herding Optimization (EHO) algorithm. The aim of this study is to obtain an optimal solution and study the effectiveness of the algorithm comparing it to the Simplex method in Microsoft Excel. This study uses two data, consisting of primary and secondary data. Based on the results of parameter testing, the proven parameters are nClan, nCi,α,β and MaxGen have a significant effect. The final simulation results have also shown a comparison of the EHO algorithm with the Simplex method having a very small percentage deviation. This shows that the EHO algorithm is effective for completing optimization multiple constraints knapsack 0-1. Keywords: EHO Algorithm, Multiple Constraints Knapsack 0-1 Problem.

KERNEL-BASED EXPONENTIAL GREY WOLF OPTIMIZER FOR RAPID CENTROID ESTIMATION IN DATA CLUSTERING

2016 ◽  
Vol 78 (11) ◽  
Author(s):  
Amolkumar Narayan Jadhav ◽  
Gomathi N.
Keyword(s):  
Data Clustering ◽  
Objective Evaluation ◽  
Multidimensional Data ◽  
Grey Wolf Optimizer ◽  
Similar Data ◽  
Grey Wolf ◽  
Data Set ◽  
Wide Range ◽  
Centroid Estimation ◽  
Two Parameters

Clustering finds variety of application in a wide range of disciplines because it is mostly helpful for grouping of similar data objects together. Due to the wide applicability, different algorithms have been presented in the literature for segmenting large multidimensional data into discernible representative clusters. Accordingly, in this paper, Kernel-based exponential grey wolf optimizer (KEGWO) is developed for rapid centroid estimation in data clustering. Here, KEGWO is newly proposed to search the cluster centroids with a new objective evaluation which considered two parameters called logarithmic kernel function and distance difference between two top clusters. Based on the new objective function and the modified KEGWO algorithm, centroids are encoded as position vectors and the optimal location is found for the final clustering. The proposed KEGWO algorithm is evaluated with banknote authentication Data Set, iris dataset and wine dataset using four metrics such as, Mean Square Error, F-measure, Rand co-efficient and jaccord coefficient. From the outcome, we proved that the proposed KEGWO algorithm outperformed the existing algorithms.   

scholarly journals Optimization of Complex System Reliability using Hybrid Grey Wolf Optimizer

2021 ◽  
Vol 4 (2) ◽  
pp. 241-256
Author(s):  
Ganga Negi ◽  
◽  
Anuj Kumar ◽  
Sangeeta Pant ◽  
Mangey Ram ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Optimization Technique ◽  
Cuckoo Search ◽  
Optimization Methods ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Reliability Allocation ◽  
Search Optimization ◽  
Cuckoo Search Optimization ◽  
Comparison Of The Results

Reliability allocation to increase the total reliability has become a successful way to increase the efficiency of the complex industrial system designs. A lot of research in the past have tackled this problem to a great extent. This is evident from the different techniques developed so far to achieve the target. Stochastic metaheuristics like simulated annealing, Tabu search (TS), Particle Swarm Optimization (PSO), Cuckoo Search Optimization (CS), Genetic Algorithm (GA), Grey wolf optimization technique (GWO) etc. have been used in recent years. This paper proposes a framework for implementing a hybrid PSO-GWO algorithm for solving some reliability allocation and optimization problems. A comparison of the results obtained is done with the results of other well-known methods like PSO, GWO, etc. The supremacy/competitiveness of the proposed framework is demonstrated from the numerical experiments. These results with regard to the time taken for the computation and quality of solution outperform the previously obtained results by the other well-known optimization methods.

Hybrid Grey Wolf Optimizer Using Elite Opposition-Based Learning Strategy and Simplex Method

2017 ◽  
Vol 16 (02) ◽  
pp. 1750012 ◽  
Author(s):  
Sen Zhang ◽  
Qifang Luo ◽  
Yongquan Zhou
Keyword(s):  
Convergence Rate ◽  
Simplex Method ◽  
Learning Strategy ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Population Diversity ◽  
Flower Pollination Algorithm ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Opposition Based Learning

To overcome the poor population diversity and slow convergence rate of grey wolf optimizer (GWO), this paper introduces the elite opposition-based learning strategy and simplex method into GWO, and proposes a hybrid grey optimizer using elite opposition (EOGWO). The diversity of grey wolf population is increased and exploration ability is improved. The experiment results of 13 standard benchmark functions indicate that the proposed algorithm has strong global and local search ability, quick convergence rate and high accuracy. EOGWO is also effective and feasible in both low-dimensional and high-dimensional case. Compared to particle swarm optimization with chaotic search (CLSPSO), gravitational search algorithm (GSA), flower pollination algorithm (FPA), cuckoo search (CS) and bat algorithm (BA), the proposed algorithm shows a better optimization performance and robustness.

Experimental and Estimation of Flashover Voltage of Outdoor High Voltage Insulators with Silica Filler Based on Grey Wolf Optimizer

2019 ◽  
Vol 20 (4) ◽  
Author(s):  
Elham Hassan ◽  
Loai Nasrat ◽  
Salah Kamel
Keyword(s):  
High Voltage ◽  
Environmental Conditions ◽  
Grey Wolf Optimizer ◽  
Electrical Characteristics ◽  
Grey Wolf ◽  
Silica Filler ◽  
Optimal Value ◽  
Flashover Voltage ◽  
Silica Concentration ◽  
Composite Samples

Abstract Epoxy is used widely in high voltage outdoor insulator because of its light weight (this avoids the need to use heavy cranes for their handling and installation and this reduces cost), easy handling, easy blending with additives, and having hydrophobicity. This paper focuses on improving epoxy electrical characteristics (flashover voltage) for high voltage outdoor insulator by adding silica filler. Composite of epoxy with silica filler: silica is prepared with 0 %, 10 %, 20 %, 30 % and 40 % weight percentages concentration. The flashover voltage (FOV) is tested under various environmental conditions with different samples lengths. Grey wolf optimizer (GWO) is developed to predict the best optimal value of flashover voltage under various environmental conditions using laboratory measurements of flashover voltage. The obtained results from GWO are compared with experimental measured data. Results showed an improvement with silica concentration at different samples lengths in flashover voltage for composite samples over epoxy without any additions. A comparison between three different conditions showed higher flashover voltage for samples in dry condition than that of samples in (wet, salt wet) conditions.

scholarly journals Optimal control of fractional integro-differential systems based on a spectral method and grey wolf optimizer

2020 ◽  
Vol 10 (1) ◽  
pp. 55-65 ◽  
Author(s):  
Raheleh Khanduzi ◽  
Asyieh Ebrahimzadeh ◽  
Samaneh Panjeh Ali Beik
Keyword(s):  
Optimal Control ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Approximate Solutions ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Optimal Value ◽  
Local Methods ◽  
The Cost ◽  
Solution Accuracy

This paper elaborated an effective and robust metaheuristic algorithm with acceptable performance based on solution accuracy. The algorithm applied in solution of the optimal control of fractional Volterra integro-differential (FVID) equation which be substituted by nonlinear programming (NLP). Subsequently the FIVD convert the problem to a NLP by using spectral collocation techniques and thereafter we execute the grey wolf optimizer (GWO) to improve the speed and accuracy and find the solutions of the optimal control and state as well as the optimal value of the cost function. It is mentioned that the utilization of the GWO is simple, due to the fact that the GWO is global search algorithm, the method can be applied to find optimal solution of the NLP. The efficiency of the proposed scheme is shown by the results obtained in comparison with the local methods. Further, some illustrative examples introduced with their approximate solutions and the results of the present approach compared with those achieved using other methods.

scholarly journals Ship Track Prediction Based on DLGWO-SVR

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yingyu Chen ◽  
Shenhua Yang ◽  
Yongfeng Suo ◽  
Minjie Zheng
Keyword(s):  
Support Vector Regression ◽  
Traffic Management ◽  
Traffic Accidents ◽  
Population Diversity ◽  
Support Vector ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Trajectory Prediction ◽  
Marine Traffic ◽  
Comparison Of The Results

To improve the accuracy of ship track prediction, the improved Grey Wolf Optimizer (GWO) and Support Vector Regression (SVR) models are incorporated for ship track prediction. The hunting strategy of dimensional learning was used to optimize the move search process of GWO and balance exploration and exploitation while maintaining population diversity. Selection and updating procedures keep GWO from being stuck in locally optimal solutions. The optimal parameters obtained by modified GWO were substituted into the SVR model to predict ship trajectory. Dimension Learning Grey Wolf Optimizer and Support Vector Regression (DLGWO-SVR), Grey Wolf Optimized Support Vector Regression (GWO-SVR), and Differential Evolution Grey Wolf Optimized Support Vector Regression (DEGWO-SVR) model trajectory prediction simulations were carried out. A comparison of the results shows that the trajectory prediction model based on DLGWO-SVR has higher prediction accuracy and meets the requirements of ship track prediction. The results of ship track prediction can not only improve the efficiency of marine traffic management but also prevent the occurrence of traffic accidents and maintain marine safety.

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