scholarly journals A Variable-Length Chromosome Genetic Algorithm for Time-Based Sensor Network Schedule Optimization

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3990
Author(s):  
Van-Phuong Ha ◽  
Trung-Kien Dao ◽  
Ngoc-Yen Pham ◽  
Minh-Hoang Le
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Sensor Network ◽  
Optimal Solution ◽  
Stochastic Approach ◽  
Variable Length ◽  
Sensor Nodes ◽  
Fixed Length ◽  
Network Schedule ◽  
Application Requirements

Scheduling sensor nodes has an important role in real monitoring applications using sensor networks, lowering the power consumption and maximizing the network lifetime, while maintaining the satisfaction to application requirements. Nevertheless, this problem is usually very complex and not easily resolved by analytical methods. In a different manner, genetic algorithms (GAs) are heuristic search strategies that help to find the exact or approximate global optimal solution efficiently with a stochastic approach. Genetic algorithms are advantageous for their robustness to discrete and noisy objective functions, as they are only evaluated at independent points without requirements of continuity or differentiability. However, as explained in this paper, a time-based sensor network schedule cannot be represented by a chromosome with fixed length that is required in traditional genetic algorithms. Therefore, an extended genetic algorithm is introduced with variable-length chromosome (VLC) along with mutation and crossover operations in order to address this problem. Simulation results show that, with help of carefully defined fitness functions, the proposed scheme is able to evolve the individuals in the population effectively and consistently from generation to generation towards optimal ones, and the obtained network schedules are better optimized in comparison with the result of algorithms employing a fixed-length chromosome.

scholarly journals Application of Genetic Algorithm in Time-Based Wireless Sensor Network Schedule Optimization

2021 ◽  
Vol 31.2 (149) ◽  
pp. 29-34
Keyword(s):  
Genetic Algorithm ◽  
Sensor Network ◽  
Fitness Function ◽  
Optimal Solution ◽  
Complex Problem ◽  
Wireless Sensor ◽  
Network Scheduling ◽  
Schedule Optimization ◽  
Wide Range ◽  
Network Schedule

In recent years, wireless sensor networks (WSN) have been particularly interested, studied and applied very strongly. A sensor network is generally limited in resources and energy, which greatly restrict its applicability. Sensor network optimization in practice is a very diverse with a wide range of applications, whereas sensor network scheduling is important in lowering energy consumption and maximizing network lifetime. However, optimization of sensor network schedule a very complex problem with many constraints that is not trivial to solve by analytical methods. This article discusses a heuristical approach using a genetic algorithm to find an optimal solution for network scheduling. The evaluation of fitness function, as well as selection with crossover and mutation operations help to evolve individuals in the population through generations in an optimal direction.

Based on Genetic Algorithm for Wireless Sensor Network Node Self-Localization

2012 ◽  
Vol 482-484 ◽  
pp. 1225-1228
Author(s):  
Hua Zhang
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Continuous Process ◽  
Optimal Solution ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Network Node ◽  
Positioning Accuracy ◽  
Optimization Simulation

Wireless sensor network node positioning technology is one of the key technologies. Due to self-localization of sensor nodes in the process of positioning accuracy is not high, In this paper， the genetic algorithm approach to take, through the evolution of control, making the location of the nodes for continuous progress toward the optimal solution, in order to achieve continuous process of node positioning optimization. Simulation results show that the evolution of the genetic algorithm control, can reduce errors, improve positioning accuracy.

scholarly journals Optimization strategy of wireless charger node deployment based on improved cuckoo search algorithm

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yang Wang ◽  
Feifan Wang ◽  
Yujun Zhu ◽  
Yiyang Liu ◽  
Chuanxin Zhao
Keyword(s):  
Sensor Network ◽  
Sensor Node ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Sensor Nodes ◽  
Optimization Strategy ◽  
Multi Objective ◽  
Received Power

AbstractIn wireless rechargeable sensor network, the deployment of charger node directly affects the overall charging utility of sensor network. Aiming at this problem, this paper abstracts the charger deployment problem as a multi-objective optimization problem that maximizes the received power of sensor nodes and minimizes the number of charger nodes. First, a network model that maximizes the sensor node received power and minimizes the number of charger nodes is constructed. Second, an improved cuckoo search (ICS) algorithm is proposed. This algorithm is based on the traditional cuckoo search algorithm (CS) to redefine its step factor, and then use the mutation factor to change the nesting position of the host bird to update the bird’s nest position, and then use ICS to find the ones that maximize the received power of the sensor node and minimize the number of charger nodes optimal solution. Compared with the traditional cuckoo search algorithm and multi-objective particle swarm optimization algorithm, the simulation results show that the algorithm can effectively increase the receiving power of sensor nodes, reduce the number of charger nodes and find the optimal solution to meet the conditions, so as to maximize the network charging utility.

scholarly journals A novel pseudoderivative-based mutation operator for real-coded adaptive genetic algorithms

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 139
Author(s):  
Maxinder S Kanwal ◽  
Avinash S Ramesh ◽  
Lauren A Huang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Mutation Rate ◽  
Optimal Solution ◽  
Global Optimum ◽  
Optimization Techniques ◽  
Adaptive Mutation ◽  
Local Optima ◽  
3 Dimensional ◽  
Successive Generations

Recent development of large databases, especially those in genetics and proteomics, is pushing the development of novel computational algorithms that implement rapid and accurate search strategies. One successful approach has been to use artificial intelligence and methods, including pattern recognition (e.g. neural networks) and optimization techniques (e.g. genetic algorithms). The focus of this paper is on optimizing the design of genetic algorithms by using an adaptive mutation rate that is derived from comparing the fitness values of successive generations. We propose a novel pseudoderivative-based mutation rate operator designed to allow a genetic algorithm to escape local optima and successfully continue to the global optimum. Once proven successful, this algorithm can be implemented to solve real problems in neurology and bioinformatics. As a first step towards this goal, we tested our algorithm on two 3-dimensional surfaces with multiple local optima, but only one global optimum, as well as on the N-queens problem, an applied problem in which the function that maps the curve is implicit. For all tests, the adaptive mutation rate allowed the genetic algorithm to find the global optimal solution, performing significantly better than other search methods, including genetic algorithms that implement fixed mutation rates.

A New Testability Optimization Allocation Approach

2013 ◽  
Vol 328 ◽  
pp. 444-449 ◽  
Author(s):  
Gang Liu ◽  
Fang Li
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Iterative Process ◽  
Resource Constraints ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Numerical Example ◽  
Set Up ◽  
Optimization Allocation ◽  
Allocation Approach

This paper describes a methodology based on improved genetic algorithms (GA) and experiments plan to optimize the testability allocation. Test resources were reasonably configured for testability optimization allocation, in order to meet the testability allocation requirements and resource constraints. The optimal solution was not easy to solve of general genetic algorithm, and the initial parameter value was not easy to set up and other defects. So in order to more efficiently test and optimize the allocation, migration technology was introduced in the traditional genetic algorithm to optimize the iterative process, and initial parameters of algorithm could be adjusted by using AHP approach, consequently testability optimization allocation approach based on improved genetic algorithm was proposed. A numerical example is used to assess the method. and the examples show that this approach can quickly and efficiently to seek the optimal solution of testability optimization allocation problem.

scholarly journals Using computation to enhance diagnosis and therapy: a novel mutation operator for real-coded adaptive genetic algorithms

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 139
Author(s):  
Maxinder S Kanwal ◽  
Avinash S Ramesh ◽  
Lauren A Huang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Mutation Rate ◽  
Novel Mutation ◽  
Fitness Function ◽  
Genetic Disorders ◽  
Optimal Solution ◽  
Global Optimum ◽  
Adaptive Mutation ◽  
Local Optima

The fields of molecular biology and neurobiology have advanced rapidly over the last two decades. These advances have resulted in the development of large proteomic and genetic databases that need to be searched for the prediction, early detection and treatment of neuropathologies and other genetic disorders. This need, in turn, has pushed the development of novel computational algorithms that are critical for searching genetic databases. One successful approach has been to use artificial intelligence and pattern recognition algorithms, such as neural networks and optimization algorithms (e.g. genetic algorithms). The focus of this paper is on optimizing the design of genetic algorithms by using an adaptive mutation rate based on the fitness function of passing generations. We propose a novel pseudo-derivative based mutation rate operator designed to allow a genetic algorithm to escape local optima and successfully continue to the global optimum. Once proven successful, this algorithm can be implemented to solve real problems in neurology and bioinformatics. As a first step towards this goal, we tested our algorithm on two 3-dimensional surfaces with multiple local optima, but only one global optimum, as well as on the N-queens problem, an applied problem in which the function that maps the curve is implicit. For all tests, the adaptive mutation rate allowed the genetic algorithm to find the global optimal solution, performing significantly better than other search methods, including genetic algorithms that implement fixed mutation rates.

Research on Multimodal Transport Shortest Path Based on the Genetic Algorithm

2015 ◽  
Vol 744-746 ◽  
pp. 1813-1816
Author(s):  
Shou Wen Ji ◽  
Shi Jin ◽  
Kai Lv
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Shortest Path ◽  
Optimal Solution ◽  
Path Model ◽  
Time Analysis ◽  
Multimodal Transportation ◽  
Network Time ◽  
Multimodal Transport ◽  
Crossover And Mutation

This paper focuses on the research of multimodal transportation optimization model and algorithm, designs an intermodal shortest time path model and gives a solution to algorithm, constructs a multimodal transport network time analysis chart. By using genetic algorithms, the transportation scheme will be optimized. And based on each path’s code, the population will be evolved to obtain the optimal solution by using crossover and mutation rules.

User’s Preference Aggregation Based on Parallel Interactive Genetic Algorithms

2010 ◽  
Vol 34-35 ◽  
pp. 1159-1164 ◽  
Author(s):  
Yi Nan Guo ◽  
Yong Lin ◽  
Mei Yang ◽  
Shu Guo Zhang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimal Solution ◽  
Small Population ◽  
Population Diversity ◽  
Design System ◽  
Preference Aggregation ◽  
Ip Multicast ◽  
Interactive Genetic Algorithm ◽  
Interactive Genetic Algorithms

In traditional interactive genetic algorithms, high-quality optimal solution is hard to be obtained due to small population size and limited evolutional generations. Aming at above problems, a parallel interactive genetic algorithm based on knowledge migration is proposed. During the evolution, the number of the populations is more than one. Evolution information can be exchanged between every two populations so as to guide themselves evolution. In order to realize the freedom communication, IP multicast is adopted as the transfer protocol to find out the similar users instead of traditional TCP/IP communication mode. Taken the fashion evolutionary design system as test platform, the results indicate that the IP multicast-based parallel interactive genetic algorithm has better population diversity. It also can alleviate user fatigue and speed up the convergence.

TinyCubus: An Adaptive Cross-Layer Framework for Sensor Networks (TinyCubus: Ein Adaptives Cross-Layer Framework für Sensornetze)

2005 ◽  
Vol 47 (2) ◽  
Author(s):  
Pedro José Marrón ◽  
Daniel Minder ◽  
Andreas Lachenmann ◽  
Kurt Rothermel
Keyword(s):  
Sensor Networks ◽  
Data Management ◽  
Sensor Network ◽  
Sensor Nodes ◽  
Cross Layer ◽  
Relevant Research ◽  
Management Framework ◽  
Network Applications ◽  
Application Requirements ◽  
Over Time

SummuryWith the proliferation of sensor networks and sensor network applications, the overall complexity of such systems is continuously increasing. Sensor networks are now heterogeneous in terms of their hardware characteristics and application requirements even within a single network. In addition, the requirements of currently supported applications are expected to change over time. All of this makes developing, deploying, and optimizing sensor network applications an extremely difficult task. In this paper, we present the architecture of TinyCubus, a flexible and adaptive cross-layer framework for TinyOS-based sensor networks that aims at providing the necessary infrastructure to cope with the complexity of such systems. TinyCubus consists of a cross-layer framework that enables optimizations through cross-layer interactions, a configuration engine that distributes components efficiently by considering the roles of the sensor nodes and provides support to install components dynamically, and a data management framework that selects and adapts both system and data management components. Finally, relevant research challenges associated with the development of each framework are identified and discussed in the paper.

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