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- 저자 : Cai Zhennao (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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Qiao Shimeng,Yu Helong,Heidari Ali Asghar,El-Saleh Ayman A,Cai Zhennao,Xu Xingmei,Mafarja Majdi,Chen Huiling 한국CDE학회 2022 Journal of computational design and engineering Vol.9 No.5
The whale optimizer is a popular metaheuristic algorithm, which has the problems of weak global exploration, easy falling into local optimum, and low optimization accuracy when searching for the optimal solution. To solve these problems, this paper proposes an enhanced whale optimization algorithm (WOA) based on the worst individual disturbance (WD) and neighborhood mutation search (NM), named WDNMWOA, which employed WD to enhance the ability to jump out of local optimum and global exploration, adopted NM to enhance the possibility of individuals approaching the optimal solution. The superiority of WDNMWOA is demonstrated by representative IEEE CEC2014, CEC2017, CEC2019, and CEC2020 benchmark functions and four engineering examples. The experimental results show that thes WDNMWOA has better convergence accuracy and strong optimization ability than the original WOA.
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Shan Weifeng,He Xinxin,Liu Haijun,Heidari Ali Asghar,Wang Maofa,Cai Zhennao,Chen Huiling 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.2
Harris hawks optimization (HHO) has been accepted as one of the well-established swarm-based methods in the community of optimization and machine learning that primarily works based on multiple dynamic features and various exploratory and exploitative traits. Compared with other optimization algorithms, it has been observed that HHO can obtain high-quality solutions for continuous and constrained complex and real-world problems. While there is a wide variety of strategies in the HHO for dealing with diverse situations, there are chances for sluggish performance, where the convergence rate can gradually slow with time, and the HHO may stay stuck in the current relatively better place and may be unable to explore other better areas. To mitigate this concern, this paper combines the Cauchy mutation mechanism into the HHO algorithm named CMHHO. This idea can boost performance and provide a promising optimizer for solving complex optimization problems. The Cauchy mutation mechanism can speed up the convergence of the solution and help HHO explore more promising regions compared to its basic release. On 30 IEEE CEC2017 benchmark functions, the study compared the proposed CMHHO with various conventional and advanced metaheuristics to validate its performance and quality of solutions. It has been found through experiments that the overall optimization performance of CMHHO is far superior to all competitors. The CMHHO method is applied to four engineering challenges to investigate the capabilities of the proposed algorithm in solving real-world problems, and experimental results show that the suggested algorithm is more successful than existing algorithms.
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