scholarly journals Use Linear Weighted Genetic Algorithm to Optimize the Scheduling of Fog Computing Resources

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ruisheng Li
Keyword(s):  
Genetic Algorithm ◽  
Genetic Manipulation ◽  
Fog Computing ◽  
Optimal Solution ◽  
Preference Weight ◽  
Service Cost ◽  
Elite Strategy ◽  
Traditional Algorithm ◽  
Nondominated Sorting ◽  
Single Objective

This paper establishes a mathematical model for the resource management and scheduling of the fog node cluster and establishes the optimization goals of delay, communication load, and service cost. According to the idea of genetic algorithm for single-objective optimization, this paper proposes a linear weighted genetic algorithm based on linear weighting. The optimization weight is established according to the user’s preference for the target. We normalize the optimization objective function and merge it into one target, and then we proceed with genetic manipulation to get a better solution. The experimental results show that when the user specifies the preference weight, the optimal solution can be obtained by the genetic algorithm based on linear weighting, and the algorithm execution efficiency is high. With the increase of the single-objective weight, the optimization effect of this objective is better. When the preference weight tends to be average, its overall optimization effect is not ideal. When the user does not specify the preference weight, a set of optimal solutions can be obtained through the improved nondominated sorting genetic algorithm with elite strategy. Compared with the traditional algorithm, in addition to the overall optimization effect of the target being better, the algorithm itself also has higher efficiency.

scholarly journals Multipopulation Genetic Algorithm Based on GPU for Solving TSP Problem

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Boqun Wang ◽  
Hailong Zhang ◽  
Jun Nie ◽  
Jie Wang ◽  
Xinchen Ye ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solution ◽  
Coarse Grained ◽  
Mutation Rates ◽  
Adaptive Mutation ◽  
Original Population ◽  
Elite Strategy ◽  
Mutation Strategy ◽  
Local Optimal Solution ◽  
Crossover Method

A GPU-based Multigroup Genetic Algorithm was proposed, which parallelized the traditional genetic algorithm with a coarse-grained architecture island model. The original population is divided into several subpopulations to simulate different living environments, thus increasing species richness. For each subpopulation, different mutation rates were adopted, and the crossover results were optimized by combining the crossover method based on distance. The adaptive mutation strategy based on the number of generations was adopted to prevent the algorithm from falling into the local optimal solution. An elite strategy was adopted for outstanding individuals to retain their superior genes. The algorithm was implemented with CUDA/C, combined with the powerful parallel computing capabilities of GPUs, which greatly improved the computing efficiency. It provided a new solution to the TSP problem.

Fuzzy Parallel Machines Scheduling Problem Based on Genetic Algorithm

2011 ◽  
Vol 204-210 ◽  
pp. 856-861
Author(s):  
Yuan Xie
Keyword(s):  
Genetic Algorithm ◽  
Parallel Machines ◽  
Optimal Solution ◽  
Scheduling Problem ◽  
Due Dates ◽  
Nsga Ii ◽  
Optimal Solution Set ◽  
Parallel Machines Scheduling ◽  
Nondominated Sorting ◽  
Completion Times

A kind of unrelated parallel machines scheduling problem is discussed. The memberships of fuzzy due dates denote the grades of satisfaction with respect to completion times with jobs. Objectives of scheduling are to maximize the minimum grade of satisfaction while makespan is minimized in the meantime. Two kind of genetic algorithms are employed to search optimal solution set of the problem. Both Niched Pareto Genetic Algorithm (NPGA) and Nondominated Sorting Genetic Algorithm (NSGA-II) can find the Pareto optimal solutions. Numerical simulation illustrates that NSGA-II has better results than NPGA.

A Multi-Objective Optimization Method for Coordinated Control

2011 ◽  
Vol 97-98 ◽  
pp. 942-946
Author(s):  
Yun Feng Gao ◽  
Hua Hu ◽  
Tao Wang ◽  
Xiao Guang Yang
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solution ◽  
Optimization Method ◽  
Transmission Model ◽  
Traffic Signal Control ◽  
Cell Transmission Model ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Weighted Combination ◽  
Single Objective

In this paper, to overcome the limitations of the weighted combination and single objective optimization methods, we presented a multi-objective optimization and simulation methodology for network-wide traffic signal control. A multi-objective genetic algorithm based on Non-dominated Sorting Genetic Algorithm II was given to solve the model directly to obtain Pareto optimal solution set. The objectives were evaluated by Enhanced Cell Transmission Model used to describe traffic dynamics on signalized urban road network. The results showed that the single objective optimization method made some of the objectives worsen when the objective to be optimized reaching optimal, and that the weighted combination optimization method gained a compromised solution, but the multi-objective optimization method gave consideration to more objectives, making the number of optimal or suboptimal ones is more than that of worse ones.

scholarly journals Inclusion of Hydraulic Controls in Rehabilitation Models of Drainage Networks to Control Floods

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 514
Author(s):  
Leonardo Bayas-Jiménez ◽  
F. Javier Martínez-Solano ◽  
Pedro L. Iglesias-Rey ◽  
Daniel Mora-Melia ◽  
Vicente S. Fuertes-Miquel
Keyword(s):  
Genetic Algorithm ◽  
Drainage System ◽  
Optimal Solution ◽  
Hydraulic Control ◽  
Drainage Systems ◽  
Storm Water Management Model ◽  
Drainage Networks ◽  
Extreme Rain ◽  
Rain Events ◽  
The Cost

A problem for drainage systems managers is the increase in extreme rain events that are increasing in various parts of the world. Their occurrence produces hydraulic overload in the drainage system and consequently floods. Adapting the existing infrastructure to be able to receive extreme rains without generating consequences for cities’ inhabitants has become a necessity. This research shows a new way to improve drainage systems with minimal investment costs, using for this purpose a novel methodology that considers the inclusion of hydraulic control elements in the network, the installation of storm tanks and the replacement of pipes. The presented methodology uses the Storm Water Management Model for the hydraulic analysis of the network and a modified Genetic Algorithm to optimize the network. In this algorithm, called the Pseudo-Genetic Algorithm, the coding of the chromosomes is integral and has been used in previous studies of hydraulic optimization. This work evaluates the cost of the required infrastructure and the damage caused by floods to find the optimal solution. The main conclusion of this study is that the inclusion of hydraulic controls can reduce the cost of network rehabilitation and decrease flood levels.

scholarly journals Business E-NeGotiAtion: A Method Using a Genetic Algorithm for Online Dispute Resolution in B2B Relationships

2021 ◽  
Vol 16 (5) ◽  
pp. 1186-1216
Author(s):  
Nikola Simkova ◽  
Zdenek Smutny
Keyword(s):  
Genetic Algorithm ◽  
Dispute Resolution ◽  
Single Case ◽  
Computer Mediated Communication ◽  
Optimal Solution ◽  
Mediated Communication ◽  
Online Dispute Resolution ◽  
B2b Relationships ◽  
Definition Of ◽  
E Mail

An opportunity to resolve disputes as an out-of-court settlement through computer-mediated communication is usually easier, faster, and cheaper than filing an action in court. Artificial intelligence and law (AI & Law) research has gained importance in this area. The article presents a design of the E-NeGotiAtion method for assisted negotiation in business to business (B2B) relationships, which uses a genetic algorithm for selecting the most appropriate solution(s). The aim of the article is to present how the method is designed and contribute to knowledge on online dispute resolution (ODR) with a focus on B2B relationships. The evaluation of the method consisted of an embedded single-case study, where participants from two countries simulated the realities of negotiation between companies. For comparison, traditional negotiation via e-mail was also conducted. The evaluation confirms that the proposed E-NeGotiAtion method quickly achieves solution(s), approaching the optimal solution on which both sides can decide, and also very importantly, confirms that the method facilitates negotiation with the partner and creates a trusted result. The evaluation demonstrates that the proposed method is economically efficient for parties of the dispute compared to negotiation via e-mail. For a more complicated task with five or more products, the E-NeGotiAtion method is significantly more suitable than negotiation via e-mail for achieving a resolution that favors one side or the other as little as possible. In conclusion, it can be said that the proposed method fulfills the definition of the dual-task of ODR—it resolves disputes and builds confidence.

A two-sided matching decision-making approach based on regret theory under intuitionistic fuzzy environment

2021 ◽  
pp. 1-18
Author(s):  
Xiang Jia ◽  
Xinfan Wang ◽  
Yuanfang Zhu ◽  
Lang Zhou ◽  
Huan Zhou
Keyword(s):  
Decision Making ◽  
Hamming Distance ◽  
Optimal Solution ◽  
Intuitionistic Fuzzy Sets ◽  
Regret Theory ◽  
Multi Objective ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Environment ◽  
Objective Model ◽  
Single Objective

This study proposes a two-sided matching decision-making (TSMDM) approach by combining the regret theory under the intuitionistic fuzzy environment. At first, according to the Hamming distance of intuitionistic fuzzy sets and regret theory, superior and inferior flows are defined to describe the comparative preference of subjects. Hereafter, the satisfaction degrees are obtained by integrating the superior and inferior flows of the subjects. The comprehensive satisfaction degrees are calculated by aggregating the satisfaction degrees, based on which, a multi-objective TSMDM model is built. Furthermore, the multi-objective TSMDM model is converted to a single-objective model, the optimal solution of the latter is derived. Finally, an illustrative example and several analyses are provided to verify the feasibility and the effectiveness of the proposed approach.

scholarly journals Carbon Emission Reduction of Apparel Material Distribution Based on Multi-Objective Genetic Algorithm (NSGA-II)

2019 ◽  
Vol 11 (9) ◽  
pp. 2571
Author(s):  
Xujing Zhang ◽  
Lichuan Wang ◽  
Yan Chen
Keyword(s):  
Genetic Algorithm ◽  
Carbon Emissions ◽  
Optimal Solution ◽  
Material Distribution ◽  
Low Carbon ◽  
Nsga Ii ◽  
Management Objectives ◽  
Multi Objective Genetic Algorithm ◽  
Distribution Process ◽  
The Cost

Low-carbon production has become one of the top management objectives for every industry. In garment manufacturing, the material distribution process always generates high carbon emissions. In order to reduce carbon emissions and the number of operators to meet enterprises’ requirements to control the cost of production and protect the environment, the paths of material distribution were analyzed to find the optimal solution. In this paper, the model of material distribution to obtain minimum carbon emissions and vehicles (operators) was established to optimize the multi-target management in three different production lines (multi-line, U-shape two-line, and U-shape three-line), while the workstations were organized in three ways: in the order of processes, in the type of machines, and in the components of garment. The NSGA-II algorithm (non-dominated sorting genetic algorithm-II) was applied to obtain the results of this model. The feasibility of the model and algorithm was verified by the practice of men’s shirts manufacture. It could be found that material distribution of multi-line layout produced the least carbon emissions when the machines were arranged in the group of type.

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