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- 저자 : Elmannai Hela (검색결과 3 건)
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Advanced orthogonal learning and Gaussian barebone hunger games for engineering design
Zhou Xinsen,Gui Wenyong,Heidari Ali Asghar,Cai Zhennao,Elmannai Hela,Hamdi Monia,Liang Guoxi,Chen Huiling 한국CDE학회 2022 Journal of computational design and engineering Vol.9 No.5
The hunger games search (HGS) algorithm is a recently proposed population-based optimization algorithm that mimics a common phenomenon of animals searching for food due to hunger stimuli and has a simple and easy-to- understand structure. However, the original HGS still suffers from shortcomings, such as low population diversity and the tendency to fall into local optima. To remedy these shortcomings, an improved HGS, called OCBHGS, is proposed, which introduces three main strategies, namely the chaotic initialization strategy, the Gaussian barebone mechanism, and the orthogonal learning strategy. Firstly, chaotic mapping is used for initialization to improve the quality of the initialized population. Secondly, the embedding of the Gaussian barebone mechanism effectively improves the diversity of the population, facilitates the communication between members, and helps the population avoid falling into local optima. Finally, the orthogonal learning strategy can extend the domain exploration and improve the solution accuracy of the algorithm. We conducted extensive experiments in the CEC2014 competition benchmark function, comparing OCBHGS with nine other metaheuristics and 12 improved algorithms. Also, the experimental results were evaluated using Wilcoxon signed-rank tests to analyze the experimental results comprehensively. In addition, OCBHGS was used to solve three constrained real-world engineering problems. The experimental results show that OCBHGS has a significant advantage in convergence speed and accuracy. As a result, OCBHGS ranks first in overall performance compared to other optimizers.
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Qi Ailiang,Zhao Dong,Yu Fanhua,Liu Guangjie,Heidari Ali Asghar,Chen Huiling,Algarni Abeer D.,Elmannai Hela,Gui Wenyong 한국CDE학회 2022 Journal of computational design and engineering Vol.9 No.6
The slime mould algorithm (SMA) has become a classical algorithm applied in many fields since it was presented. Nevertheless, when faced with complex tasks, the algorithm converges slowly and tends to fall into the local optimum. So, there is still room for improvement in the performance of SMA. This work proposes a novel SMA variant (SDSMA), combining the adaptive Lévy diversity mechanism and directional crossover mechanism. Firstly, the adaptive Lévy diversity mechanism can improve population diversity. Then, the directional crossover mechanism can enhance the balance of exploration and exploitation, thus helping SDSMA to increase the convergence speed and accuracy. SDSMA is compared with SMA variants, original algorithms, improved algorithms, improved-SMAs, and others on the benchmark function set to verify its performance. Meanwhile, the Wilcoxon signed-rank test, the Friedman test, and other analytical methods are considered to analyze the experimental results. The analysis results show that SDSMA with two strategies significantly improves the performance of SMA. Meanwhile, the computational cost of SDSMA is smaller than that of SMA on benchmark function. Finally, the proposed algorithm is applied to three real-world engineering design problems. The experiments prove that SDSMA is an effective aid tool for computationally complex practical tasks.
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Hao Shuhui,Huang Changcheng,Heidari Ali Asghar,Chen Huiling,Li Lingzhi,Algarni Abeer D.,Elmannai Hela,Xu Suling 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.2
If found and treated early, fast-growing skin cancers can dramatically prolong patients’ lives. Dermoscopy is a convenient and reliable tool during the fore-period detection stage of skin cancer, so the efficient processing of digital images of dermoscopy is particularly critical to improving the level of a skin cancer diagnosis. Notably, image segmentation is a part of image preprocessing and essential technical support in the process of image processing. In addition, multi-threshold image segmentation (MIS) technology is extensively used due to its straightforward and effective features. Many academics have coupled different meta-heuristic algorithms with MIS to raise image segmentation quality. Nonetheless, these meta-heuristic algorithms frequently enter local optima. Therefore, this paper suggests an improved salp swarm algorithm (ILSSA) method that combines iterative mapping and local escaping operator to address this drawback. Besides, this paper also proposes the ILSSA-based MIS approach, which is triumphantly utilized to segment dermoscopic images of skin cancer. This method uses two-dimensional (2D) Kapur’s entropy as the objective function and employs non-local means 2D histogram to represent the image information. Furthermore, an array of benchmark function test experiments demonstrated that ILSSA could alleviate the local optimal problem more effectively than other compared algorithms. Afterward, the skin cancer dermoscopy image segmentation experiment displayed that the proposed ILSSA-based MIS method obtained superior segmentation results than other MIS peers and was more adaptable at different thresholds.
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