An Improved Flower Pollination Algorithm for Optimizing Layouts of Nodes in Wireless Sensor Network

Nguyen, Trong-The; Pan, Jeng‐Shyang; Dao, Thi-Kien

doi:10.1109/access.2019.2921721

Cited by 130 publications

(67 citation statements)

References 33 publications

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“…The FPA is an optimization algorithm proposed by Yang [38], which was inspired by the flow pollination process of the flowering plants. The FPA was employed in various optimization applications, for example to estimate solar PV parameter [39,40], solving sudoku puzzles [41], feature selection [42], antenna design [43], and other applications [44][45][46][47]. Moreover, SSA is also an optimization algorithm proposed by Mirjalili et al [48] inspired by the behavior of salp chains.…”

Section: Introductionmentioning

confidence: 99%

Optimization Method for Forecasting Confirmed Cases of COVID-19 in China

Al-qaness

Ewees

Fan

et al. 2020

JCM

375

316

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In December 2019, a novel coronavirus, called COVID-19, was discovered in Wuhan, China, and has spread to different cities in China as well as to 24 other countries. The number of confirmed cases is increasing daily and reached 34,598 on 8 February 2020. In the current study, we present a new forecasting model to estimate and forecast the number of confirmed cases of COVID-19 in the upcoming ten days based on the previously confirmed cases recorded in China. The proposed model is an improved adaptive neuro-fuzzy inference system (ANFIS) using an enhanced flower pollination algorithm (FPA) by using the salp swarm algorithm (SSA). In general, SSA is employed to improve FPA to avoid its drawbacks (i.e., getting trapped at the local optima). The main idea of the proposed model, called FPASSA-ANFIS, is to improve the performance of ANFIS by determining the parameters of ANFIS using FPASSA. The FPASSA-ANFIS model is evaluated using the World Health Organization (WHO) official data of the outbreak of the COVID-19 to forecast the confirmed cases of the upcoming ten days. More so, the FPASSA-ANFIS model is compared to several existing models, and it showed better performance in terms of Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), Root Mean Squared Relative Error (RMSRE), coefficient of determination (R 2 ), and computing time. Furthermore, we tested the proposed model using two different datasets of weekly influenza confirmed cases in two countries, namely the USA and China. The outcomes also showed good performances.

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Section: Introductionmentioning

confidence: 99%

Optimization Method for Forecasting Confirmed Cases of COVID-19 in China

Al-qaness

Ewees

Fan

et al. 2020

JCM

375

316

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“…It also shows from another side that power load is closely related to temperature and date types. The efficiency of network can be further improved by adapting the optimization methods [58][59][60][61][62]. Figure 11.…”

Section: Prediction Resultsmentioning

confidence: 99%

New Hybrid Algorithms for Prediction of Daily Load of Power Network

Pan

Chu

et al. 2019

Applied Sciences

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Two new hybrid algorithms are proposed to improve the performances of the meta-heuristic optimization algorithms, namely the Grey Wolf Optimizer (GWO) and Shuffled Frog Leaping Algorithm (SFLA). Firstly, it advances the hierarchy and position updating of the mathematical model of GWO, and then the SGWO algorithm is proposed based on the advantages of SFLA and GWO. It not only improves the ability of local search, but also speeds up the global convergence. Secondly, the SGWOD algorithm based on SGWO is proposed by using the benefit of differential evolution strategy. Through the experiments of the 29 benchmark functions, which are composed of the functions of unimodal, multimodal, fixed-dimension and composite multimodal, the performances of the new algorithms are better than that of GWO, SFLA and GWO-DE, and they greatly balances the exploration and exploitation. The proposed SGWO and SGWOD algorithms are also applied to the prediction model based on the neural network. Experimental results show the usefulness for forecasting the power daily load. Appl. Sci. 2019, 9, 4514 2 of 22 the process. The algorithm is an effective way to solve global optimization problems, and it has the characteristics of generality, stability, and fast convergence. It includes two criteria, exploration and exploitation. Exploitation reflects the ability of finding the best around a good range, while exploration reflects the ability of searching for new range. At the beginning, it should search the whole range as much as possible, then through using exploitation it searches more carefully around the good solution. But they are contradictory. Too small exploration leads to convergence too fast and easy falling into local optimum; however, too small exploitation makes the algorithm converge too slowly.The No Free Lunch (NFL) theorem considers that there is no meta-heuristic algorithm applying for all optimization problems [1,2]. In other words, an algorithm shows very promising results on a set of issues, but it doesn't perform well on another set of issues. So it needs putting forward a new algorithm to get high performance in certain specific areas.Over the past decades, a large number of meta-heuristics are inspired by natural behaviors [3-5], such as, Genetic Algorithm (GA) [6][7][8], Differential Evolution (DE) Algorithm [9-12], Grey Wolf Optimizer (GWO) Algorithm [13-17], Particle Swarm Optimization (PSO) Algorithm [18-21], Artificial Bee Colony (ABC) Algorithm [22-24], Cat Swarm Optimization (CSO) [25-27], Artificial Fish Swarm Algorithm (AFSA) [28,29], Ant Colony Optimization (ACO) Algorithm [30-34], Shuffled Frog Leaping Algorithm (SFLA) [35-40], Biogeography Based Optimization (BBO) Algorithm [41-43], QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm [44][45][46][47] and so on. Because they all have some defects, many researchers also introduce hybrid algorithms to improve the defects [48][49][50][51][52][53][54][55].The rest of the paper is organized as follows: some related research works are described in the Secti...

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“…CH is selected from among NM. The functions of CHs are not only to collect the information from the NM but also to aggregate captured data that forwarded to the BS [12,13]. Figure 1 shows an example of the clustering fashion of WSN.…”

Section: Introductionmentioning

confidence: 99%

Identifying correctness data scheme for aggregating data in cluster heads of wireless sensor network based on naive Bayes classification

Chu

Dao

Pan

et al. 2020

J Wireless Com Network

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Wireless sensor network (WSN) has been paid more attention by scholars due to the practical communication of a system of devices to transfer information gathered from a monitored field through wireless links. Precise and accurate data of aggregating messages from sensor nodes is a vital demand for a success WSN application. This paper proposes a new scheme of identifying the correctness data scheme for aggregating data in cluster heads in hierarchical WSN based on naive Bayes classification. The collecting environmental information includes temperature, humidity, sound, and pollution levels, from sensor nodes to cluster heads that classify data fault and aggregate and transfer them to the base station. The collecting data is classified based on the classifier to aggregate in the cluster head of WSN. Compared with some existing methods, the proposed method offers an effective way of forwarding the correct data in WSN applications.

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An Improved Flower Pollination Algorithm for Optimizing Layouts of Nodes in Wireless Sensor Network

Cited by 130 publications

References 33 publications

Optimization Method for Forecasting Confirmed Cases of COVID-19 in China

Optimization Method for Forecasting Confirmed Cases of COVID-19 in China

New Hybrid Algorithms for Prediction of Daily Load of Power Network

Identifying correctness data scheme for aggregating data in cluster heads of wireless sensor network based on naive Bayes classification

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