A Novel Hybrid Flower Pollination Algorithm with Chaotic Harmony Search for Solving Sudoku Puzzles

Abdel-Raouf, Osama; El-Henawy, Ibrahim M.; Abdel-Baset, Mohamed

doi:10.5815/ijmecs.2014.03.05

Cited by 79 publications

(15 citation statements)

References 27 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

371

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

371

316

View full text Add to dashboard Cite

show abstract

“…We categorise them into 4 classes: (1) The works proposed by Yang and his co-authors [18,[22][23][24][25], (2) the works just applying the FPA for solving optimisation problems [27][28][29][30], (3) FPA extensions [31][32][33][34][35] and (4) the works analysing the FPA performances [36][37][38]. These contributions are analysed with greater detail in the next section.…”

Section: The Choice Between Local Pollination and Global Pollination Ismentioning

confidence: 99%

“…In this publication, Abdel-Raouf et al [31] propose the hybridisation of the FPA with the harmony search algorithm [44]. They use the resulting hybrid to solve Sudoku Puzzles.…”

Section: Publication 10 [31]mentioning

confidence: 99%

On the performances of the flower pollination algorithm – Qualitative and quantitative analyses

Draa

2015

Applied Soft Computing

106

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“…Then, the flower pollination algorithm was used in [77] for solving the economic load dispatch problem. The authors in [66] used the canonical flower pollination algorithm for solving the sudoku puzzles problem. In [79], the authors proposed a novel FPA based on dimension by dimension improvement.…”

Section: The Flower Pollination Algorithmmentioning

confidence: 99%