scholarly journals Hybrid Genetic Algorithm for Optimization of Food Composition on Hypertensive Patient

2018 ◽  
Vol 8 (6) ◽  
pp. 4673
Author(s):  
Aprilia Nur Fauziyah ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Genetic Algorithm ◽  
Hypertensive Patient ◽  
Hybrid Method ◽  
Variable Neighborhood Search ◽  
Hybrid Genetic Algorithm ◽  
Minimum Cost ◽  
Food Composition ◽  
Premature Convergence ◽  
Neighborhood Search

The healthy food with attention of salt degree is one of the efforts for healthy living of hypertensive patient. The effort is important for reducing the probability of hypertension change to be dangerous disease. In this study, the food composition is build with attention nutrition amount, salt degree, and minimum cost. The proposed method is hybrid method of Genetic Algorithm (GA) and Variable Neighborhood Search (VNS). The three scenarios of hybrid GA-VNS types had been developed in this study. Although hybrid GA and VNS take more time than pure GA or pure VNS but the proposed method give better quality of solution. VNS successfully help GA avoids premature convergence and improves better solution. The shortcomings on GA in local exploitation and premature convergence is solved by VNS, whereas the shortcoming on VNS that less capability in global exploration can be solved by use GA that has advantage in global exploration.

scholarly journals A Hybrid Genetic Algorithm and Variable Neighborhood Search for Task Scheduling Problem in Grid Environment

2012 ◽  
Vol 29 ◽  
pp. 3808-3814 ◽  
Author(s):  
Sara Kardani-Moghaddam ◽  
Farzad Khodadadi ◽  
Reza Entezari-Maleki ◽  
Ali Movaghar
Keyword(s):  
Genetic Algorithm ◽  
Task Scheduling ◽  
Variable Neighborhood Search ◽  
Hybrid Genetic Algorithm ◽  
Neighborhood Search ◽  
Scheduling Problem ◽  
Grid Environment

scholarly journals An Improved Genetic Algorithm with Adaptive Variable Neighborhood Search for FJSP

Algorithms ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 243
Author(s):  
Xiaolin Gu ◽  
Ming Huang ◽  
Xu Liang
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Method ◽  
Variable Neighborhood Search ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Neighborhood Search ◽  
Flexible Job Shop ◽  
Flexible Job Shop Scheduling ◽  
Improved Genetic Algorithm ◽  
Shop Scheduling

For solving the complex flexible job-shop scheduling problem, an improved genetic algorithm with adaptive variable neighborhood search (IGA-AVNS) is proposed. The improved genetic algorithm first uses a hybrid method combining operation sequence (OS) random selection with machine assignment (MA) hybrid method selection to generate the initial population, and it then groups the population. Each group uses an improved genetic operation for global search, then the better solutions from each group are stored in the elite library, and finally, the adaptive local neighborhood search is used in the elite library for detailed local searches. The simulation experiments are carried out by three sets of international standard examples. The experimental results show that the IGA-AVNS algorithm is an effective algorithm for solving flexible job-shop scheduling problems.

Optimization of Food Composition for Hypertensive Patient using Variable Neighborhood Search

2017 ◽  
Vol 8 (2) ◽  
pp. 327
Author(s):  
Aprilia Nur Fauziyah ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Variable Neighborhood Search ◽  
Minimum Cost ◽  
Food Composition ◽  
Neighborhood Search ◽  
Minimal Cost ◽  
Neighborhood Structure ◽  
Healthy Life ◽  
Hypertensive Patients ◽  
Structure Selection ◽  
Neighborhood Structures

<em>Hypertension is a major symptom that cause other diseases appear such as non-communicable diseases, cancer, and diabetes if the nutrients on hypertensive patients not controlled from the actual nutrition need. One of healthy life effort for the patients is consuming healthy food that considers level of salt in the foods. The problem to determine the food composition that considers level of salt and minimum cost of the food is solved using Variable Neighborhood Search. This study compares 3 neighborhood structures: insertion, exchange, and 2-opt. The use of 2-opt neighborhood structure gives the highest fitness averages of other neighborhood structure. Selection and arrangement of neighborhood structure in every k neighborhood have effect on the solution is obtained. The result of this study contains composition of foods with nutrients which are close to the needs of hypertension patients with attentions the sodium and minimal cost within a day.</em>

scholarly journals Solving the dual-resource constrained flexible job shop scheduling problem with learning effect by a hybrid genetic algorithm

2018 ◽  
Vol 10 (10) ◽  
pp. 168781401880409 ◽  
Author(s):  
Rui Wu ◽  
Yibing Li ◽  
Shunsheng Guo ◽  
Wenxiang Xu
Keyword(s):  
Genetic Algorithm ◽  
Variable Neighborhood Search ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Neighborhood Search ◽  
Flexible Job Shop ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

In this article, we investigate a novel dual-resource constrained flexible job shop scheduling problem with consideration of worker’s learning ability and develop an efficient hybrid genetic algorithm to solve the problem. To begin with, a comprehensive mathematical model with the objective of minimizing the makespan is formulated. Then, a hybrid algorithm which hybridizes genetic algorithm and variable neighborhood search is developed. In the proposed algorithm, a three-dimensional chromosome coding scheme is employed to represent the individuals, a mixed population initialization method is designed for yielding the initial population, and advanced crossover and mutation operators are proposed according to the problem characteristic. Moreover, variable neighborhood search is integrated to improve the local search ability. Finally, to evaluate the effectiveness of the proposed algorithm, computational experiments are performed. The results demonstrate that the proposed algorithm can solve the problem effectively and efficiently.

scholarly journals A Hybrid Genetic Algorithm for the Multiple Crossdocks Problem

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Zhaowei Miao ◽  
Ke Fu ◽  
Feng Yang
Keyword(s):  
Genetic Algorithm ◽  
Variable Neighborhood Search ◽  
Time Windows ◽  
Hybrid Genetic Algorithm ◽  
Search Method ◽  
Strong Sense ◽  
Neighborhood Search ◽  
Problem Structure ◽  
Penalty Cost

We study a multiple crossdocks problem with supplier and customer time windows, where any violation of time windows will incur a penalty cost and the flows through the crossdock are constrained by fixed transportation schedules and crossdock capacities. We prove this problem to beNP-hard in the strong sense and therefore focus on developing efficient heuristics. Based on the problem structure, we propose a hybrid genetic algorithm (HGA) integrating greedy technique and variable neighborhood search method to solve the problem. Extensive experiments under different scenarios were conducted, and results show that HGA outperforms CPLEX solver, providing solutions in realistic timescales.

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