scholarly journals Optimal Deployment in Emergency Medicine with Genetic Algorithm Exemplified by Lifeguard Assignments*

2021 ◽  
Author(s):  
Jonas Chromik ◽  
Bert Arnrich
Keyword(s):  
Genetic Algorithm ◽  
Emergency Medicine ◽  
Optimal Deployment

scholarly journals Optimal Modeling of Wireless LANs: A Decision-Making Multiobjective Approach

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Tomás de Jesús Mateo Sanguino ◽  
Jhon Carlos Mendoza Betancourt
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Wireless Lans ◽  
Evolutionary Strategy ◽  
Problem Solving Skills ◽  
Critical Design ◽  
Simultaneous Design ◽  
Multiobjective Approach ◽  
Optimal Deployment ◽  
Optimal Modeling

Communication infrastructure planning is a critical design task that typically requires handling complex concepts on networking aimed at optimizing performance and resources, thus demanding high analytical and problem-solving skills to engineers. To reduce this gap, this paper describes an optimization algorithm—based on evolutionary strategy—created as an aid for decision-making prior to the real deployment of wireless LANs. The developed algorithm allows automating the design process, traditionally handmade by network technicians, in order to save time and cost by improving the WLAN arrangement. To this end, we implemented a multiobjective genetic algorithm (MOGA) with the purpose of meeting two simultaneous design objectives, namely, to minimize the number of APs while maximizing the coverage signal over a whole planning area. Such approach provides efficient and scalable solutions closer to the best network design, so that we integrated the developed algorithm into an engineering tool with the goal of modelling the behavior of WLANs in ICT infrastructures. Called WiFiSim, it allows the investigation of various complex issues concerning the design of IEEE 802.11-based WLANs, thereby facilitating design of the study and design and optimal deployment of wireless LANs through complete modelling software. As a result, we comparatively evaluated three target applications considering small, medium, and large scenarios with a previous approach developed, a monoobjective genetic algorithm.

scholarly journals Goal-Programming-Driven Genetic Algorithm Model for Wireless Access Point Deployment Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Chen-Shu Wang ◽  
Ching-Ter Chang
Keyword(s):  
Genetic Algorithm ◽  
Goal Programming ◽  
Access Point ◽  
Wireless Networking ◽  
Complex Problem ◽  
Decision Makers ◽  
Wireless Access ◽  
Optimal Deployment ◽  
Capacity Loading ◽  
Wireless Access Point

Appropriate wireless access point deployment (APD) is essential for ensuring seamless user communication. Optimal APD enables good telecommunication quality, balanced capacity loading, and optimal deployment costs. APD is a typical NP-complex problem because improving wireless networking infrastructure has multiple objectives (MOs). This paper proposes a method that integrates a goal-programming-driven model (PM) and a genetic algorithm (GA) to resolve the MO-APD problem. The PM identifies the target deployment subject of four constraints: budget, coverage, capacity, and interference. The PM also calculates dynamic capacity requirements to replicate real wireless communication. Three experiments validate the feasibility of the PM. The results demonstrate the utility and stability of the proposed method. Decision makers can easily refer to the PM-identified target deployment before allocating APs.

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.

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