A Novel Multi-Objective Moth-Flame Optimization Algorithm for Feature Selection

Ghada, M. A. Soliman; Tarek, Hesham; Emary, Eid; Motaz, M. H. Khorshid

doi:10.17485/ijst/2018/v11i38/128008

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…In this research we proposed a hybrid prediction algorithms based on using different metaheuristic algorithms such as (GWO, MFO [16], WOA [17], ALO, GA, PSO), modified metaheuristics such as (MFO2, MFO-3 proposed by Soliman, et al [18], and (WOA-2, WOA-3) proposed by Soliman, et al [19], developed (hybrid) algorithms such as ( Multi-objective MFO2, Multi-objective MFO3) proposed by Gh. et al, [20], and memetic (hybrid) WOA2SA, WOA3SA recently proposed by Soliman, et al…”

Section: Terrorist Group(s) Prediction Using Annsmentioning

confidence: 99%

Terrorism Prediction Using Artificial Neural Network

Soliman¹,

Abou-El-Enien²

2019

RIA

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The main purpose of this research is to develop a hybrid computational intelligent algorithm (framework) as a decision support (DS) tool for terrorism phenomenon that has been defeated for years by governments, countries, and different multiple institutions and hence it needs multiple and integrated research from different science disciplines with a hope of being eliminated in the future. The proposed hybrid prediction algorithm based on integrated different Operations Research (OR) and Decision support tools with Data Mining (DM) techniques especially prediction and classification algorithms as well as different directions of modification and improvements in a number of recent and popular metaheuristics inspired algorithms. The proposed system has been developed, implemented, and evaluated according to different set of assessment measures. Through this study, it was found that, the proposed system is capable of predicting the terrorist group (s) responsible of terror attacks on different regions (countries). The findings of this research may serve as an alarm tool to determine the terrorist groups' networks and so minimize the terrorist attacks.

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Section: Terrorist Group(s) Prediction Using Annsmentioning

confidence: 99%

Terrorism Prediction Using Artificial Neural Network

Soliman¹,

Abou-El-Enien²

2019

RIA

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ST-AL: a hybridized search based metaheuristic computational algorithm towards optimization of high dimensional industrial datasets

et al. 2022

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The rapid growth of data generated by several applications like engineering, biotechnology, energy, and others has become a crucial challenge in the high dimensional data mining. The large amounts of data, especially those with high dimensions, may contain many irrelevant, redundant, or noisy features, which may negatively affect the accuracy and efficiency of the industrial data mining process. Recently, several meta-heuristic optimization algorithms have been utilized to evolve feature selection techniques for dealing with the vast dimensionality problem. Despite optimization algorithms’ ability to find the near-optimal feature subset of the search space, they still face some global optimization challenges. This paper proposes an improved version of the sooty tern optimization (ST) algorithm, namely the ST-AL method, to improve the search performance for high-dimensional industrial optimization problems. ST-AL method is developed by boosting the performance of STOA by applying four strategies. The first strategy is the use of a control randomization parameters that ensure the balance between the exploration–exploitation stages during the search process; moreover, it avoids falling into local optimums. The second strategy entails the creation of a new exploration phase based on the Ant lion (AL) algorithm. The third strategy is improving the STOA exploitation phase by modifying the main equation of position updating. Finally, the greedy selection is used to ignore the poor generated population and keeps it from diverging from the existing promising regions. To evaluate the performance of the proposed ST-AL algorithm, it has been employed as a global optimization method to discover the optimal value of ten CEC2020 benchmark functions. Also, it has been applied as a feature selection approach on 16 benchmark datasets in the UCI repository and compared with seven well-known optimization feature selection methods. The experimental results reveal the superiority of the proposed algorithm in avoiding local minima and increasing the convergence rate. The experimental result are compared with state-of-the-art algorithms, i.e., ALO, STOA, PSO, GWO, HHO, MFO, and MPA and found that the mean accuracy achieved is in range 0.94–1.00.

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A Novel Multi-Objective Moth-Flame Optimization Algorithm for Feature Selection

Cited by 2 publications

References 6 publications

Terrorism Prediction Using Artificial Neural Network

Terrorism Prediction Using Artificial Neural Network

ST-AL: a hybridized search based metaheuristic computational algorithm towards optimization of high dimensional industrial datasets

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