Genetic algorithm based optimization technique for underwater sensor network positioning and deployment

2015 ◽  
Author(s):  
Sidharth Iyer ◽  
D. Vijay Rao
Keyword(s):  
Genetic Algorithm ◽  
Sensor Network ◽  
Optimization Technique ◽  
Underwater Sensor Network

scholarly journals Energy Optimization for Underwater Sensor Network using Nature Inspired Technique

2019 ◽  
Vol 8 (9S) ◽  
pp. 161-164 ◽  
Keyword(s):  
Sensor Network ◽  
Optimization Technique ◽  
Energy Optimization ◽  
Present Position ◽  
Herding Behavior ◽  
Female Authority ◽  
Underwater Sensor Network

There is lots of energy optimization technique is used in underwater sensor network but in this paper, nature-inspired technique, called Elephant Herding Optimization (EHO), and is proposed for solving optimization tasks. The EHO method is inspired by the herding behavior of the elephant group. In nature, the elephants having a place with various factions live respectively under the initiative of female authority, and the male elephants will leave their family bunch when they grow up. These two practices can be displayed into two after administrators: group refreshing administrator and isolating administrator. In EHO, the elephants in every faction are refreshed by its present position and female authority through group refreshing administrator. It is found from the outcomes that the EHO based vitality streamlining approach shows the outcomes compelling than established methodology as far as numerous parameters. In this work, the advancement dimension of the vitality to over 11% is accomplished utilizing Elephant Herd Optimization that is likewise utilized in numerous other areas for designing enhancement.

scholarly journals Spark-Based Parallel Genetic Algorithm for Simulating a Solution of Optimal Deployment of an Underwater Sensor Network

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2717 ◽  
Author(s):  
Peng Liu ◽  
Shuai Ye ◽  
Can Wang ◽  
Zongwei Zhu
Keyword(s):  
Genetic Algorithm ◽  
Sensor Network ◽  
Large Scale ◽  
Working Environment ◽  
Underwater Sensor Networks ◽  
Single Node ◽  
Running Time ◽  
Node Deployment ◽  
Underwater Sensor Network ◽  
Optimal Deployment

Underwater sensor networks have wide application prospects, but the large-scale sensing node deployment is severely hindered by problems like energy constraints, long delays, local disconnections, and heavy energy consumption. These problems can be solved effectively by optimizing sensing node deployment with a genetic algorithm. However, the genetic algorithm (GA) needs many iterations in solving the best location of underwater sensor deployment, which results in long running time delays and limited practical application when dealing with large-scale data. The classical parallel framework Hadoop can improve the GA running efficiency to some extent while the state-of-the-art parallel framework Spark can release much more parallel potential of GA by realizing parallel crossover, mutation, and other operations on each computing node. Giving full allowance for the working environment of the underwater sensor network and the characteristics of sensors, this paper proposes a Spark-based parallel GA to calculate the extremum of the Shubert multi-peak function, through which the optimal deployment of the underwater sensor network can be obtained. Experimental results show that while faced with a large-scale underwater sensor network, compared with single node and Hadoop framework, the Spark-based implementation not only significantly reduces the running time but also effectively avoids the problem of premature convergence because of its powerful randomness.

Genetic algorithm- and finite element-based design and analysis of nonprismatic piezolaminated beam for optimal vibration energy harvesting

2015 ◽  
Vol 230 (14) ◽  
pp. 2532-2548 ◽  
Author(s):  
Alok Ranjan Biswal ◽  
Tarapada Roy ◽  
Rabindra Kumar Behera
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Energy Harvesting ◽  
Output Power ◽  
Governing Equation ◽  
Optimization Technique ◽  
Vibration Energy ◽  
Fe Model ◽  
Vibration Energy Harvesting ◽  
Real Coded Genetic Algorithm

The current article deals with finite element (FE)- and genetic algorithm (GA)-based vibration energy harvesting from a tapered piezolaminated cantilever beam. Euler–Bernoulli beam theory is used for modeling the various cross sections of the beam. The governing equation of motion is derived by using the Hamilton's principle. Two noded beam elements with two degrees of freedom at each node have been considered in order to solve the governing equation. The effect of structural damping has also been incorporated in the FE model. An electric interface is assumed to be connected to measure the voltage and output power in piezoelectric patch due to charge accumulation caused by vibration. The effects of taper (both in the width and height directions) on output power for three cases of shape variation (such as linear, parabolic and cubic) along with frequency and voltage are analyzed. A real-coded genetic algorithm-based constrained (such as ultimate stress and breakdown voltage) optimization technique has been formulated to determine the best possible design variables for optimal harvesting power. A comparative study is also carried out for output power by varying the cross section of the beam, and genetic algorithm-based optimization scheme shows the better results than that of available conventional trial and error methods.

Numerical Simulation of Gas Lift Optimization Using Artificial Intelligence for a Middle Eastern Oil Field

2021 ◽  
Author(s):  
Mohammed Ahmed Al-Janabi ◽  
Omar F. Al-Fatlawi ◽  
Dhifaf J. Sadiq ◽  
Haider Abdulmuhsin Mahmood ◽  
Mustafa Alaulddin Al-Juboori
Keyword(s):  
Genetic Algorithm ◽  
Sensitivity Analysis ◽  
Middle Eastern ◽  
Optimization Technique ◽  
Injection Rate ◽  
Oil Field ◽  
Oil Fields ◽  
Reservoir Pressure ◽  
Artificial Lift ◽  
Gas Lift

Abstract Artificial lift techniques are a highly effective solution to aid the deterioration of the production especially for mature oil fields, gas lift is one of the oldest and most applied artificial lift methods especially for large oil fields, the gas that is required for injection is quite scarce and expensive resource, optimally allocating the injection rate in each well is a high importance task and not easily applicable. Conventional methods faced some major problems in solving this problem in a network with large number of wells, multi-constrains, multi-objectives, and limited amount of gas. This paper focuses on utilizing the Genetic Algorithm (GA) as a gas lift optimization algorithm to tackle the challenging task of optimally allocating the gas lift injection rate through numerical modeling and simulation studies to maximize the oil production of a Middle Eastern oil field with 20 production wells with limited amount of gas to be injected. The key objective of this study is to assess the performance of the wells of the field after applying gas lift as an artificial lift method and applying the genetic algorithm as an optimization algorithm while comparing the results of the network to the case of artificially lifted wells by utilizing ESP pumps to the network and to have a more accurate view on the practicability of applying the gas lift optimization technique. The comparison is based on different measures and sensitivity studies, reservoir pressure, and water cut sensitivity analysis are applied to allow the assessment of the performance of the wells in the network throughout the life of the field. To have a full and insight view an economic study and comparison was applied in this study to estimate the benefits of applying the gas lift method and the GA optimization technique while comparing the results to the case of the ESP pumps and the case of naturally flowing wells. The gas lift technique proved to have the ability to enhance the production of the oil field and the optimization process showed quite an enhancement in the task of maximizing the oil production rate while using the same amount of gas to be injected in the each well, the sensitivity analysis showed that the gas lift method is comparable to the other artificial lift method and it have an upper hand in handling the reservoir pressure reduction, and economically CAPEX of the gas lift were calculated to be able to assess the time to reach a profitable income by comparing the results of OPEX of gas lift the technique showed a profitable income higher than the cases of naturally flowing wells and the ESP pumps lifted wells. Additionally, the paper illustrated the genetic algorithm (GA) optimization model in a way that allowed it to be followed as a guide for the task of optimizing the gas injection rate for a network with a large number of wells and limited amount of gas to be injected.

scholarly journals Spectrum Sensing for Cognitive Radio Using Genetic Algorithm

2018 ◽  
Vol 14 (09) ◽  
pp. 190 ◽  
Author(s):  
Shewangi Kochhar ◽  
Roopali Garg
Keyword(s):  
Genetic Algorithm ◽  
Cognitive Radio ◽  
False Alarm ◽  
Spectrum Sensing ◽  
Optimization Technique ◽  
Primary User ◽  
Time Frame ◽  
Probability Of False Alarm ◽  
Sensing Time ◽  
Better Than

<p>Cognitive Radio has been skillful technology to improve the spectrum sensing as it enables Cognitive Radio to find Primary User (PU) and let secondary User (SU) to utilize the spectrum holes. However detection of PU leads to longer sensing time and interference. Spectrum sensing is done in specific “time frame” and it is further divided into Sensing time and transmission time. Higher the sensing time better will be detection and lesser will be the probability of false alarm. So optimization technique is highly required to address the issue of trade-off between sensing time and throughput. This paper proposed an application of Genetic Algorithm technique for spectrum sensing in cognitive radio. Here results shows that ROC curve of GA is better than PSO in terms of normalized throughput and sensing time. The parameters that are evaluated are throughput, probability of false alarm, sensing time, cost and iteration.</p>

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