The monarch butterfly optimization algorithm for solving feature selection problems

Alweshah, Mohammed; Khalaileh, Saleh Al; Gupta, Brij B.; Almomani, Ammar; Hammouri, Abdelaziz I.; Al-Betar, Mohammed Azmi

doi:10.1007/s00521-020-05210-0

Cited by 91 publications

(46 citation statements)

References 66 publications

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“…The proposed FS method is not tested on datasets that consist of more than 400 features. Alweshah et al [83] introduced a binary monarch butterfly optimization algorithm (bMBO) for selecting optimal features from high dimensional datasets. The proposed bMBO could show inconsistent results on gigabyte-size datasets.…”

Section: Literature Reviewmentioning

confidence: 99%

bSSA: Binary Salp Swarm Algorithm With Hybrid Data Transformation for Feature Selection

et al. 2021

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Section: Literature Reviewmentioning

confidence: 99%

bSSA: Binary Salp Swarm Algorithm With Hybrid Data Transformation for Feature Selection

et al. 2021

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“…Nowadays, FS is an essential step to preprocess high-dimensional datasets. It must be pointed that there are representative computational intelligence algorithms that have been applied to improve the FS in different studies such as [7], [9], [33], [34], [27], [46], and [47]. The optimization methods aim to obtain the optimal solution for FS (i.e., significant feature subset) within an appropriate time and cost.…”

Section: Related Workmentioning

confidence: 99%

An Improved Harris Hawks Optimization Algorithm With Simulated Annealing for Feature Selection in the Medical Field

et al. 2020

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Harris Hawks Optimization (HHO) algorithm is a new metaheuristic algorithm, inspired by the cooperative behavior and chasing style of Harris' Hawks in nature called surprise pounce. HHO demonstrated promising results compared to other optimization methods. However, HHO suffers from local optima and population diversity drawbacks. To overcome these limitations and adapt it to solve feature selection problems, a novel metaheuristic optimizer, namely Chaotic Harris Hawks Optimization (CHHO), is proposed. Two main improvements are suggested to the standard HHO algorithm. The first improvement is to apply the chaotic maps at the initialization phase of HHO to enhance the population diversity in the search space. The second improvement is to use the Simulated Annealing (SA) algorithm to the current best solution to improve HHO exploitation. To validate the performance of the proposed algorithm, CHHO was applied on 14 medical benchmark datasets from the UCI machine learning repository. The proposed CHHO was compared with the original HHO and some famous and recent metaheuristics algorithms, containing Grasshopper Optimization Algorithm (GOA), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Butterfly Optimization Algorithm (BOA), and Ant Lion Optimizer (ALO). The used evaluation metrics include the number of selected features, classification accuracy, fitness values, Wilcoxon's statistical test (P-value), and convergence curve. Based on the achieved results, CHHO confirms its superiority over the standard HHO algorithm and the other optimization algorithms on the majority of the medical datasets.

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“…When all possible subsets of the dataset are removed during the generation process, there is very high complexity and a high processing time of x 2 , where x is the number of characteristics in the dataset [23]. Therefore, physicists have sought to formulate approaches to solve the feature selection (FS) problem and provide solutions more efficiently than conventional techniques.…”

Section: Introductionmentioning

confidence: 99%

“…It has been shown that they can help minimize execution time and produce specific outcomes. Grey Wolf Optimizer (GWO) [31], Whale Optimization Algorithm [32], Monarch Butterfly Algorithm [23] Coyote Optimization Algorithm [33], Genetic Algorithm [34], Krill Herd Algorithm [35] Harmony Search [36], Aquila Optimizer [37], Particle Swarm Algorithm [38], and Parallel Membrane-inspired Framework [39] are examples of the metaheuristics that have been used to address feature selection problems.…”

Section: Introductionmentioning

confidence: 99%

Boosting Arithmetic Optimization Algorithm with Genetic Algorithm Operators for Feature Selection: Case Study on Cox Proportional Hazards Model

et al. 2021

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Feature selection is a well-known prepossessing procedure, and it is considered a challenging problem in many domains, such as data mining, text mining, medicine, biology, public health, image processing, data clustering, and others. This paper proposes a novel feature selection method, called AOAGA, using an improved metaheuristic optimization method that combines the conventional Arithmetic Optimization Algorithm (AOA) with the Genetic Algorithm (GA) operators. The AOA is a recently proposed optimizer; it has been employed to solve several benchmark and engineering problems and has shown a promising performance. The main aim behind the modification of the AOA is to enhance its search strategies. The conventional version suffers from weaknesses, the local search strategy, and the trade-off between the search strategies. Therefore, the operators of the GA can overcome the shortcomings of the conventional AOA. The proposed AOAGA was evaluated with several well-known benchmark datasets, using several standard evaluation criteria, namely accuracy, number of selected features, and fitness function. Finally, the results were compared with the state-of-the-art techniques to prove the performance of the proposed AOAGA method. Moreover, to further assess the performance of the proposed AOAGA method, two real-world problems containing gene datasets were used. The findings of this paper illustrated that the proposed AOAGA method finds new best solutions for several test cases, and it got promising results compared to other comparative methods published in the literature.

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The monarch butterfly optimization algorithm for solving feature selection problems

Cited by 91 publications

References 66 publications

bSSA: Binary Salp Swarm Algorithm With Hybrid Data Transformation for Feature Selection

bSSA: Binary Salp Swarm Algorithm With Hybrid Data Transformation for Feature Selection

An Improved Harris Hawks Optimization Algorithm With Simulated Annealing for Feature Selection in the Medical Field

Boosting Arithmetic Optimization Algorithm with Genetic Algorithm Operators for Feature Selection: Case Study on Cox Proportional Hazards Model

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