An efficient opposition based Lévy Flight Antlion optimizer for optimization problems

Dinkar, Shail Kumar; Deep, Kusum

doi:10.1016/j.jocs.2018.10.002

Cited by 60 publications

(24 citation statements)

References 47 publications

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“…Dinkar and Deep in [131] proposed an enhanced version of ALO called OB-LF-ALO which based on Opposition-based Learning(OBL) and Lévy flight (LF). They replaced the uniform distribution with LF random walk in conjunction with OBL.…”

Section: ) Lévy Alo and Opposition-based Alomentioning

confidence: 99%

Ant Lion Optimization: Variants, Hybrids, and Applications

2020

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Ant Lion Optimizer (ALO) is a recent novel algorithm developed in the literature that simulates the foraging behavior of a Ant lions. Recently, it has been applied to a huge number of optimization problems. It has many advantages: easy, scalable, flexible, and have a great balance between exploration and exploitation. In this comprehensive study, many publications using ALO have been collected and summarized. Firstly, we introduce an introduction about ALO. Secondly, we categorized the recent versions of ALO into 3 Categories mainly Modified, Hybrid and Multi-Objective. we also introduce the applications in which ALO has been applied such as power, Machine Learning, Image processing problems, Civil Engineering, Medical, etc. The review paper is ended by giving a conclusion of the main ALO foundations and providing some suggestions & possible future directions that can be investigated.

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Section: ) Lévy Alo and Opposition-based Alomentioning

confidence: 99%

Ant Lion Optimization: Variants, Hybrids, and Applications

2020

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“…On the basis of probability theory, there is a 50-50 chance that the distance between the current solution and optimal solution is farther than its corresponding opposite [51]. Hence, we can utilize the concept of OBL to obtain a higher chance for approaching the promising regions, and then have a good balance of exploration and exploitation [52]. In general, the initial solutions are created randomly which are absence of priori knowledge about the solution.…”

Section: Dragonfly Algorithm With Opposition-based Learning (Oblda) Bmentioning

confidence: 99%

Dragonfly Algorithm with Opposition-Based Learning for Multilevel Thresholding Color Image Segmentation

2019

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Multilevel thresholding is a very active research field in image segmentation, and has been successfully used in various applications. However, the computational time will increase exponentially as the number of thresholds increases, and for color images which contain more information this is even worse. To overcome the drawback while maintaining segmentation accuracy, a modified version of dragonfly algorithm (DA) with opposition-based learning (OBLDA) for color image segmentation is proposed in this paper. The opposition-based learning (OBL) strategy simultaneously considers the current solution and the opposite solution, which are symmetrical in the search space. With the introduction of OBL, the proposed algorithm has a faster convergence speed and more balanced exploration–exploitation compared with the original DA. In order to clearly demonstrate the outstanding performance of the OBLDA, the proposed method is compared with seven state-of-the-art meta-heuristic algorithms, through experiments on 10 test images. The optimal threshold values are calculated by the maximization of between-class variance and Kapur’s entropy. Meanwhile, some indicators, including peak signal to noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM), the average fitness values, standard deviation (STD), and computation time are used as evaluation criteria in the experiments. The promising results reveal that proposed method has the advantages of high accuracy and remarkable stability. Wilcoxon’s rank sum test and Friedman test are also performed to verify the superiority of OBLDA in a statistical way. Furthermore, various satellite images are also included for robustness testing. In conclusion, the OBLDA algorithm is a feasible and effective method for multilevel thresholding color image segmentation.

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“…The compared results pointed out that MALO has outperformed the traditional ALO and other methods in minimizing optimal generation of reactive power for transmission power grids. Another Lévy Flight based ALO (LFALO) method has been constructed in [36]. Thus, the study has replaced a uniform distributed random walk with random walk of the Lévy Flight distribution so as to enhance exploration.…”

Section: Review On Previous Improved Antlion Optimization Algorithmmentioning

confidence: 99%

“…We agree with the authors in [37] that the roulette wheel selection technique was not appropriate for ALO to choose the most effective solutions. In addition, we also adopt comments of authors in [36] that new solution generation function of ALO needs to be improved more effectively. Thus, in the paper we suggest modifying the two pointed shortcomings of ALO.…”

Section: Improved Antlion Optimization Algorithm (Ialo)mentioning

confidence: 99%

Finding Solutions for Optimal Reactive Power Dispatch Problem by a Novel Improved Antlion Optimization Algorithm

Cao

Đại

et al. 2019

Energies

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In this paper, a novel improved Antlion optimization algorithm (IALO) has been proposed for solving three different IEEE power systems of optimal reactive power dispatch (ORPD) problem. Such three power systems with a set of constraints in transmission power networks such as voltage limitation of all buses, limitations of tap of all transformers, maximum power transmission limitation of all conductors and limitations of all capacitor banks have given a big challenge for global optimal solution search ability of the proposed method. The proposed IALO method has been developed by modifying new solution generation technique of standard antlion optimization algorithm (ALO). By optimizing three single objective functions of systems with 30, 57 and 118 buses, the proposed method has been demonstrated to be more effective than ALO in terms of the most optimal solution search ability, solution search speed and search stabilization. In addition, the proposed method has also been compared to other existing methods and it has obtained better results than approximately all compared ones. Consequently, the proposed IALO method is deserving of a potential optimization tool for solving ORPD problem and other optimization problems in power system optimization fields.

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An efficient opposition based Lévy Flight Antlion optimizer for optimization problems

Cited by 60 publications

References 47 publications

Ant Lion Optimization: Variants, Hybrids, and Applications

Ant Lion Optimization: Variants, Hybrids, and Applications

Dragonfly Algorithm with Opposition-Based Learning for Multilevel Thresholding Color Image Segmentation

Finding Solutions for Optimal Reactive Power Dispatch Problem by a Novel Improved Antlion Optimization Algorithm

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