A comparison study of Hill Climbing, Simulated Annealing and Genetic Algorithm for node placement problem in WMNs

Sakamoto, Shinji; Kulla, Elis; Oda, Tetsuya; Ikeda, Makoto; Barolli, Leonard; Xhafa, Fatos

doi:10.3233/jhs-140487

Cited by 89 publications

(13 citation statements)

References 15 publications

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“…Although they observed that both Genetic and Hill-Climbing algorithms appear to be able to improve accuracy of term selection, they did not find evidence that their implementation of GA performs better than that for their Hill-Climbing algorithm. A recent study by Sakamoto et al [46] elected to compare GAs and HC in a completely different problem domain, which is simulating the node placements problem for achieving the network connectivity and user coverage.…”

Section: Contrasting Our Implementation Of Ga Optimization To Ramentioning

confidence: 99%

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Smart Information Retrieval: Domain Knowledge Centric Optimization Approach

et al. 2019

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In the age of the Internet of Things, online data have witnessed a significant growth in terms of volume and diversity, and research into information retrieval has become one of the important research themes in the Internet-oriented data science research. This paper introduces a novel domain knowledge centric methodology aimed at improving the accuracy of using machine learning methods for relation extraction from text data, which is critical to the accuracy and efficiency of information retrievalbased applications, including recommender systems and sentiment analysis. The proposed methodology makes a significant contribution to the processes of domain knowledge-based relation extraction including interrogating Linked Open Datasets to generate the relation classification training data, addressing the imbalanced classification in the training datasets, determining the probability threshold of the best learning algorithm, and establishing the optimum parameters for genetic algorithms, which were utilized to optimize the feature selection for the learning algorithms. The experimental evaluation of the proposed methodology reveals that the adopted machine-learning algorithms exhibit higher precision and recall in relation extraction in the reduced feature space optimized by our implementation. The considered machine learning includes support vector machine, perceptron algorithm uneven margin, and K-nearest neighbors. The outcome is verified by comparing against the random mutation hill-climbing optimization algorithm using Wilcoxon signed-rank statistical analysis.

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Section: Contrasting Our Implementation Of Ga Optimization To Ramentioning

confidence: 99%

“…In our RMHC implementation, we adopted a similar configuration to that used by Sakamoto et al [46]. The RMHC implementation works as in the following pseudo-code:…”

Section: Contrasting Our Implementation Of Ga Optimization To Ramentioning

confidence: 99%

Smart Information Retrieval: Domain Knowledge Centric Optimization Approach

et al. 2019

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“…The temperature decreases according to a particular function T new = €T old , where € is the parameter of cooling speed and €<1. The algorithm advances through the search space, [33,34].…”

Section: Simulated Annealingmentioning

confidence: 99%

“…If it is worse, it substitutes it with probability P. As iterations continue, the temperature parameter value is decreased following a cooling schedule, thus biasing SA towards accepting only better solutions. The steps of the overall algorithm for SA to solve the WMN design problem are as follows [33]:…”

Section: Simulated Annealing Approachmentioning

confidence: 99%

Solving the Wireless Mesh Network Design Problem using Genetic Algorithm and Simulated Annealing Optimization Methods

Girgis¹,

Mahmoud²,

Abdullatif³

et al. 2014

IJCA

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Mesh clients, mesh routers and gateways are components of Wireless Mesh Network (WMN). In WMN, gateways connect to Internet using wireline links and supply Internet access services for users. Multiple gateways are needed, which take time and cost budget to set up, due to the limited wireless channel bit rate. WMN is a highly developed technology that offers to end users a wireless broadband access. It offers a high degree of flexibility contrasted to conventional networks; however, this attribute comes at the expense of a more complex construction. Therefore, a challenge is the planning and optimization of WMNs. This paper concentrates on the challenge using a genetic algorithm and simulated annealing. The genetic algorithm and simulated annealing enable searching for a low-cost WMN configuration with constraints and determine the number of used gateways. Experimental results proved that the performance of the genetic algorithm and simulated annealing in minimizing WMN network costs while satisfying quality of service. The proposed models are presented to significantly outperform the existing solutions.

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“…There are different approaches and intelligent systems that can be used to deal with NP-hard problems. In our previous work, we considered Genetic Algorithms (GAs), Neural Networks (NNs), Particle Swarm Optimization (PSO), Hill Climbing (HC), Simulated Annealing (SA) and Tabu Search [12][13][14][15]. However, different intelligent algorithms have different efficiency for different problems.…”

Section: Introductionmentioning

confidence: 99%

Application of Fuzzy Logic for Selection of Actor Nodes in WSANs —Implementation of Two Fuzzy-Based Systems and a Testbed

Elmazi

Cuka

Ikeda

et al. 2019

Sensors

Self Cite

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The development of sensor networks and the importance of smart devices in the physical world has brought attention to Wireless Sensor and Actor Networks (WSANs). They consist of a large number of static sensors and also a few other smart devices, such as different types of robots. Sensor nodes have responsibility for sensing and sending information towards an actor node any time there is an event that needs immediate intervention such as natural disasters or malicious attacks in the network. The actor node is responsible for processing and taking prompt action accordingly. But in order to select an appropriate actor to do one task, we need to consider different parameters, which make the problem NP-hard. For this reason, we consider Fuzzy Logic and propose two Fuzzy Based Simulation Systems (FBSS). FBSS1 has three input parameters such as Number of Sensors per Actor (NSA), Remaining Energy (RE) and Distance to Event (DE). On the other hand, FBSS2 has one new parameter—Transmission Range (TR)—and for this reason it is more complex. We will explain in detail the differences between these two systems. We also implement a testbed and compare simulation results with experimental results.

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A comparison study of Hill Climbing, Simulated Annealing and Genetic Algorithm for node placement problem in WMNs

Cited by 89 publications

References 15 publications

Smart Information Retrieval: Domain Knowledge Centric Optimization Approach

Smart Information Retrieval: Domain Knowledge Centric Optimization Approach

Solving the Wireless Mesh Network Design Problem using Genetic Algorithm and Simulated Annealing Optimization Methods

Application of Fuzzy Logic for Selection of Actor Nodes in WSANs —Implementation of Two Fuzzy-Based Systems and a Testbed

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