Elite opposition learning and exponential function steps-based dragonfly algorithm for global optimization

Song, Junmin; Li, Shangping

doi:10.1109/icinfa.2017.8079080

Cited by 19 publications

(19 citation statements)

References 15 publications

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“…Furthermore, for enhancing the performance of optimization by DA, reference [34] examined an improved version of DA. The proposed DA is based on exponential function adaptive steps and elite opposition-based learning strategy.…”

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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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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“…For enhancing the performance of optimization by DA, reference [26] proposed an improved version of DA. The proposed DA based on elite opposition-based learning strategy and exponential function adaptive steps.…”

Section: A Elite Opposition Learning and Exponential Function Steps-mentioning

confidence: 99%

OUP accepted manuscript

2020

Journal of Computational Design and Engineering

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Dragonfly algorithm (DA) is one of the most recently developed heuristic optimization algorithms by Mirjalili in 2016. It is now one of the most widely used algorithms. In some cases, it outperforms the most popular algorithms. However, this algorithm is not far from obstacles when it comes to complex optimization problems. In this work, along with the strengths of the algorithm in solving real-world optimization problems, the weakness of the algorithm to optimize complex optimization problems is addressed. This survey presents a comprehensive investigation of DA in the engineering area. First, an overview of the algorithm is discussed. Additionally, the different variants of the algorithm are addressed too. The combined versions of the DA with other techniques and the modifications that have been done to make the algorithm work better are shown. Besides, a survey on applications in engineering area that used DA is offered. The algorithm is compared with some other metaheuristic algorithms to demonstrate its ability to optimize problems comparing to the others. The results of the algorithm from the works that utilized the DA in the literature and the results of the benchmark functions showed that in comparison with some other algorithms DA has an excellent performance, especially for small to medium problems. Moreover, the bottlenecks of the algorithm and some future trends are discussed. Authors conduct this research with the hope of offering beneficial information about the DA to the researchers who want to study the algorithm and utilize it to optimize engineering problems.

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“…The pbest (personal best) and gbest (global best) concepts of PSO are introduced to guide the population. In addition, Song and Li [42] introduced an elite opposition learning and exponential function steps-based dragonfly algorithm. On the one hand, elite opposition-based learning mechanism is used to improve the global exploration capability.…”

Section: Among the Segmentation Techniques Above Threshold-basedmentioning

confidence: 99%

A Novel Method for Multilevel Color Image Segmentation Based on Dragonfly Algorithm and Differential Evolution

Jia

Lang

et al. 2019

IEEE Access

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Multilevel thresholding is a simple and powerful image segmentation method that has received widespread attention in recent years. However, the accuracy and stability of thresholding techniques are still not ideal and cannot meet the needs of engineering problems. Therefore, this paper presents a memetic algorithm of dragonfly algorithm (DA) and differential evolution (DE) for color image segmentation, which is known as improved DA (IDA). On the one hand, DA algorithm has a satisfied capability of avoiding convergence to the local optimum, thus it is served as a global search technique. On the other hand, the DE algorithm is adopted as a local search technique, which can increase the precision of solutions. In this paper, two thresholding techniques, namely, Otsu and minimum cross entropy (MCE) methods are used to determine the optimal threshold values. In order to evaluate the performance of the proposed method, we conduct a series of experiments on color images from the Berkeley database and the results are compared with five stateof-the-art meta-heuristic algorithms. Besides, a non-parametric Wilcoxon's rank sum test is also included for statistical analysis. From the experimental results, it is found that IDA-based method outperforms other compared methods in terms of average fitness values, standard deviation, peak signal to noise ratio, structural similarity index, and feature similarity index. The promising results indicate that the application of IDA-based thresholding technique is potential and meaningful.

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Elite opposition learning and exponential function steps-based dragonfly algorithm for global optimization

Cited by 19 publications

References 15 publications

Dragonfly Algorithm and Its Applications in Applied Science Survey

Dragonfly Algorithm and Its Applications in Applied Science Survey

OUP accepted manuscript

A Novel Method for Multilevel Color Image Segmentation Based on Dragonfly Algorithm and Differential Evolution

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