Hybrid binary dragonfly enhanced particle swarm optimization algorithm for solving feature selection problems

Tawhid, Mohamed A.; Dsouza, Kevin Bradley

doi:10.3934/mfc.2018009

Cited by 34 publications

(18 citation statements)

References 14 publications

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“…Also, we would like to apply our proposed algorithm on solving unconstrained optimization problems [1], large scale problems and molecular potential energy function [42], [41], team formation problem [8], and minimax and integer programming problems [2], [39], [40]. Furthermore, we would like to use binary version to solve feature selection problems [37], [38].…”

Section: Discussionmentioning

confidence: 99%

An effective hybrid firefly algorithm with the cuckoo search for engineering optimization problems

Tawhid¹,

Ali²

2018

Mathematical Foundations of Computing

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Firefly and cuckoo search algorithms are two of the most widely used nature-inspired algorithms due to their simplicity and inexpensive computational cost when they applied to solve a wide range of problems. In this article, a new hybrid algorithm is suggested by combining the firefly algorithm and the cuckoo search algorithm to solve constrained optimization problems (COPs) and real-world engineering optimization problems. The proposed algorithm is called Hybrid FireFly Algorithm and Cuckoo Search (HFFACS) algorithm. In the HFFACS algorithm, a balance between the exploration and the exploitation processes is considered. The main drawback of the firefly algorithm is it is easy to fall into stagnation when the new solution is not better than its previous best solution for several generations. In order to avoid this problem, the cuckoo search with Lèvy flight is invoked to force the firefly algorithm to escape from stagnation and to avoid premature convergence. The proposed algorithm is applied to six benchmark constrained optimization problems and five engineering optimization problems and compared against four algorithms to investigate its performance. The numerical experimental results show the proposed algorithm is a promising algorithm and can obtain the optimal or near optimal solution within a reasonable time.

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Section: Discussionmentioning

confidence: 99%

An effective hybrid firefly algorithm with the cuckoo search for engineering optimization problems

Tawhid¹,

Ali²

2018

Mathematical Foundations of Computing

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“…For example, Elhariri et al [57] solve the problem of electromyography (EMG) signal classification with optimal features subset selection by using DA and support vector machines (SVM) classification. Tawhid et al [58] combine the BDA and enhanced PSO to propose a hybrid BDA-enhanced PSO (HBDESPO) algorithm for feature selections. In reference [59], a combination of wavelet packet-based features and improved binary dragonfly optimization-based feature selection method is proposed to classify different types of infant cry signals.…”

Section: Related Workmentioning

confidence: 99%

IBDA: Improved Binary Dragonfly Algorithm With Evolutionary Population Dynamics and Adaptive Crossover for Feature Selection

Kang

Sun

et al. 2020

IEEE Access

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Feature selection is an effective method to eliminate irrelevant, redundant and noisy features, which improves the performance of classification and reduces the computational burden in machine learning. In this paper, an improved binary dragonfly algorithm (IBDA) which extends from the conventional dragonfly algorithm (DA) is proposed as a search strategy to design a wrapper-based feature selection method. First, a novel evolutionary population dynamics (EPD) strategy is introduced in IBDA to enhance the exploitation ability while ensuring population diversity of the algorithm. Second, IBDA proposes a novel crossover operator which establishes connections between the crossover rates and iterations so that making the algorithm can adjust the crossover rates of solutions dynamically, thereby balancing the exploitation and exploration of the algorithm. Finally, a binary mechanism is proposed to make the algorithm suitable for the binary feature selection problems. Simulations are conducted on 27 classical datasets from the UC Irvine Machine Learning Repository, and the results demonstrate that the proposed IBDA has better performance than some other comparison algorithms. Moreover, the effectiveness and performance of the proposed improved factors are evaluated by tests.

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“…In reference [32], a hybrid version of the binary dragonfly algorithm with an enhanced particle swarm optimization algorithm for solving the feature selection problem was examined. The proposed approach called Hybrid Binary Dragonfly Enhanced Particle Swarm Optimization Algorithm (HBDESPO).…”

Section: Hybridization Versions Of Damentioning

confidence: 99%

Dragonfly Algorithm and Its Applications in Applied Science Survey

Rahman

Rashid

2019

Computational Intelligence and Neuroscience

107

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One of the most recently developed heuristic optimization algorithms is dragonfly by Mirjalili. Dragonfly algorithm has shown its ability to optimizing different real-world problems. It has three variants. In this work, an overview of the algorithm and its variants is presented. Moreover, the hybridization versions of the algorithm are discussed. Furthermore, the results of the applications that utilized the dragonfly algorithm in applied science are offered in the following area: machine learning, image processing, wireless, and networking. It is then compared with some other metaheuristic algorithms. In addition, the algorithm is tested on the CEC-C06 2019 benchmark functions. The results prove that the algorithm has great exploration ability and its convergence rate is better than the other algorithms in the literature, such as PSO and GA. In general, in this survey, the strong and weak points of the algorithm are discussed. Furthermore, some future works that will help in improving the algorithm's weak points are recommended. This study is conducted with the hope of offering beneficial information about dragonfly algorithm to the researchers who want to study the algorithm.

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Hybrid binary dragonfly enhanced particle swarm optimization algorithm for solving feature selection problems

Cited by 34 publications

References 14 publications

An effective hybrid firefly algorithm with the cuckoo search for engineering optimization problems

An effective hybrid firefly algorithm with the cuckoo search for engineering optimization problems

IBDA: Improved Binary Dragonfly Algorithm With Evolutionary Population Dynamics and Adaptive Crossover for Feature Selection

Dragonfly Algorithm and Its Applications in Applied Science Survey

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