A survey on particle swarm optimization with emphasis on engineering and network applications

Elbes, Mohammed; AlZu’bi, Shadi; Kanan, Tarek; Al-Fuqaha, Ala; Hawashin, Bilal

doi:10.1007/s12065-019-00210-z

Cited by 125 publications

(54 citation statements)

References 75 publications

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“…GA imitates the evolution of species. 17 For a given problem, a population composed of candidate solutions is generated. Each solution is evaluated according to a fitness function.…”

Section: Optimization Methodsmentioning

confidence: 99%

“…PSO 22 is a stochastic swarm intelligence based optimization algorithm that is frequently utilized because of its simplicity, accuracy, and fast convergence ability. 17 In the work of Nekouie and Moattar, 23 PSO based hybrid missing value estimation method is used on breast cancer diagnosis data. To overcome PSO's weakness of getting stuck in local optimum, chaotic reduced adaptive PSO (CRAPSO) is employed.…”

Section: Optimization Methodsmentioning

confidence: 99%

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ANFIS and Deep Learning based missing sensor data prediction in IoT

Guzel

Kök

Akay

et al. 2019

Concurrency and Computation

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Summary Internet of Things (IoT) consists of billions of devices that generate big data which is characterized by the large volume, velocity, and heterogeneity. In the heterogeneous IoT ecosystem, it is not so surprising that these sensor‐generated data are considered to be noisy, uncertain, erroneous, and missing due to the lack of battery power, communication errors, and malfunctioning devices. This paper presents Deep Learning (DL) and Adaptive‐Network based Fuzzy Inference System (ANFIS) based prediction models for missing sensor data problem in IoT ecosystem. First, we build ANFIS based models and optimize their parameters. Then, we construct DL based models by using Long Short Term Memory (LSTM) network structure and optimize its parameters by applying the grid search method. Finally, we evaluate all the proposed models with Intel Berkeley Lab dataset. Experimental results demonstrate that the proposed models can significantly improve the prediction accuracy and may be promising for missing sensor data prediction.

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“…GA imitates the evolution of species. 17 For a given problem, a population composed of candidate solutions is generated. Each solution is evaluated according to a fitness function.…”

Section: Optimization Methodsmentioning

confidence: 99%

Section: Optimization Methodsmentioning

confidence: 99%

ANFIS and Deep Learning based missing sensor data prediction in IoT

Guzel

Kök

Akay

et al. 2019

Concurrency and Computation

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“…Hence, solutions move and interact, rather than being evolved as in EAs, according to the dynamics outlined in Reference [25]. Several PSO variants have been designed to deal with a wide range of problems [26], including large scale ones [27,28], as well as challenging engineering applications [29], and hybrid versions were also designed thus generating effective PSO based multi-strategy approaches [30] and Estimation of Distribution Algorithms (EDAs) [31,32]. The EDA framework is quite interesting and has proven to be successful over different fields such as Robotics [33] and combinatorial domains [21].…”

Section: Background and Related Workmentioning

confidence: 99%

An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis

2019

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This article presents a novel hybrid classification paradigm for medical diagnoses and prognoses prediction. The core mechanism of the proposed method relies on a centroid classification algorithm whose logic is exploited to formulate the classification task as a real-valued optimisation problem. A novel metaheuristic combining the algorithmic structure of Swarm Intelligence optimisers with the probabilistic search models of Estimation of Distribution Algorithms is designed to optimise such a problem, thus leading to high-accuracy predictions. This method is tested over 11 medical datasets and compared against 14 cherry-picked classification algorithms. Results show that the proposed approach is competitive and superior to the state-of-the-art on several occasions.

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“…where Φ T i T j is the observed phase shift between the signals from T i and T j . This is a configuration of three equations and three unknowns and is solved using Particle Swarm Optimization (PSO) [43,44].…”

Section: Our Previous Localization Approachmentioning

confidence: 99%

Using phase shift fingerprints and inertial measurements in support of precise localization in urban areas

Elbes

Alkhatib

Al-Fuqaha

et al. 2019

Pers Ubiquit Comput

Self Cite

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Localization is an important primitive that is utilized in a number of important applications such as location-based mobile services, augmented reality, and autonomous mobile robotics. While the GPS technology is considered the de facto standard for outdoor localization, it is known to suffer from significant accuracy limitation in urban areas. In this work, we present a particle filter-based data fusion technique for localization in urban areas. The proposed localization technique provides more accurate location estimation results due to its ability to efficiently fuse together information collected from diverse sensor technologies. The novelty of our proposed approach stems from its ability to fuse data from diverse sources, namely, phase shift fingerprints collected from Low Power AM Radio (LPAM) towers and inertial measurement sensors. Our simulation results indicate that the proposed approach can achieve an accuracy of 0.5 m using a limited number of LPAM towers as low as 5. Also, the proposed approach requires the collection of a low number of LPAM phase shift fingerprints. Our simulations indicate that 30 fingerprints are enough to provide 0.5 m accuracy in a 100 × 100 m 2 deployment. Keywords Smart cities • Localization in urban areas • Data fusion • Fingerprinting • Channel state information 1 Introduction In recent years, several critical national infrastructures (such as the electric grid, intelligent transportation, water systems) Mohammed Elbes

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A survey on particle swarm optimization with emphasis on engineering and network applications

Cited by 125 publications

References 75 publications

ANFIS and Deep Learning based missing sensor data prediction in IoT

ANFIS and Deep Learning based missing sensor data prediction in IoT

An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis

Using phase shift fingerprints and inertial measurements in support of precise localization in urban areas

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