Gradient-Based Optimizer for Structural Optimization Problems

Issa, Mohamed; Mostafa, Yahia

doi:10.1007/978-3-030-99079-4_18

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…Notably, these algorithms exhibit proficiency in exploring diverse solutions, albeit at the cost of slower convergence rates for simpler functions, often necessitating substantial computational resources (Khanduja & Bhushan, 2021). Table 1 provides a Comparative Analysis of Optimization Algorithms: Gradient-Based vs. Metaheuristic Approaches Applications well-suited for Gradient-Based Optimization encompass diverse tasks such as training machine learning models, deep learning parameter tuning (Zhang, 2019), and select scientific simulations (Issa & Mostafa, 2022). In contrast, as discussed previously, Metaheuristic Algorithms prove particularly adept in addressing an array of complex objective functions and extensive search spaces (Agrawal et al, 2021).…”

Section: Gradient-based Algorithms Vs Metaheuristic Algorithms In Opt...mentioning

confidence: 99%

Metaheuristic Optimization Algorithms: an overview

Dr. Benaissa Brahim,

Kobayashi,

Al Ali

et al. 2024

HCMCOUJS - Advances in Computational Structures

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Metaheuristic optimization algorithms are known for their versatility and adaptability, making them effective tools for solving a wide range of complex optimization problems. They don't rely on specific problem types, gradients, and can explore globally while handling multi-objective optimization. They strike a balance between exploration and exploitation, contributing to advancements in optimization. However, it's important to note their limitations, including the lack of a guaranteed global optimum, varying convergence rates, and their somewhat opaque functioning. In contrast, metaphor-based optimization algorithms, while intuitively appealing, have faced controversy due to potential oversimplification and unrealistic expectations. Despite these considerations, metaheuristic algorithms continue to be widely used for tackling complex problems. This research paper aims to explore the fundamental components and concepts that underlie optimization algorithms, focusing on the use of search references and the delicate balance between exploration and exploitation. Visual representations of the search behavior of selected metaheuristic algorithms will also be provided.

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Section: Gradient-based Algorithms Vs Metaheuristic Algorithms In Opt...mentioning

confidence: 99%

Metaheuristic Optimization Algorithms: an overview

Dr. Benaissa Brahim,

Kobayashi,

Al Ali

et al. 2024

HCMCOUJS - Advances in Computational Structures

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A novel reinforcement learning-based reptile search algorithm for solving optimization problems

Ghetas,

Issa

2023

Neural Comput & Applic

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This work proposes a novel reptile search algorithm (RSA) to solve optimization problems called reinforcement reptile search algorithm (RLRSA). The basic RSA performs exploitation through highly walking in the first half of searching process while the exploration phase is executed through the hunting phase in the second half. Therefore, the algorithm is not able to balance exploration and exploitation and this behavior results in trapping in local optima. A novel learning method based on reinforcement learning and Q-learning model is proposed to balance the exploitation and exploration phases when the solution starts deteriorating. Furthermore, the random opposite-based learning (ROBL) is introduced to increase the diversity of the population and so enhance the obtained solutions. Twenty-three typical benchmark functions, including unimodal, multimodal and fixed-dimension multimodal functions, were employed to assess the performance of RLRSA. According to the findings, the RLRSA method surpasses the standard RSA approach in the majority of benchmark functions evaluated, specifically in 12 out of 13 unimodal functions, 9 out of 13 multimodal functions, and 8 out of 10 fixed multimodal functions. Furthermore, the RLRSA is applied to vessel solve pressure and tension/compression spring design problems. The results show that RLRSA significantly found the solution with minimum cost. The experimental results reveal the superiority of the RLRSA compared to RSA and other optimization methods in the literature.

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Reinforcement learning guided Spearman dynamic opposite Gradient-based optimizer for numerical optimization and anchor clustering

Sun,

Huo,

Jia

et al. 2023

Journal of Computational Design and Engineering

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As science and technology advance, the need for novel optimization techniques has led to an increase. The recently proposed metaheuristic algorithm, Gradient-based optimizer (GBO), is rooted in the gradient-based Newton's method. GBO has a more concrete theoretical foundation. However, GSR and LEO operators in GBO still have some shortcomings. The insufficient updating method and the simple selection process limit the search performance of the algorithm. In this paper, an improved version is proposed to compensate for the above shortcomings, called RL-SDOGBO. First, during the GSR phase, the Spearman rank correlation coefficient is used to determine weak solutions on which to perform dynamic opposite learning. This operation assists the algorithm to escape from local optima and enhance exploration capability. Second, to optimize the exploitation capability, reinforcement learning is used to guide the selection of solution update modes in the LEO operator. RL-SDOGBO is tested on 12 classical benchmark functions and 12 CEC2022 benchmark functions with seven representative metaheuristics, respectively. The impact of the improvements, the scalability and running time of the algorithm, and the balance of exploration and exploitation are analyzed and discussed. Combining the experimental results and some statistical results, RL-SDOGBO exhibits excellent numerical optimization performance and provides high-quality solutions in most cases. In addition, RL-SDOGBO is also used to solve the anchor clustering problem for small target detection, making it a more potential and competitive option.

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Gradient-Based Optimizer for Structural Optimization Problems

Cited by 3 publications

References 31 publications

Metaheuristic Optimization Algorithms: an overview

Metaheuristic Optimization Algorithms: an overview

A novel reinforcement learning-based reptile search algorithm for solving optimization problems

Reinforcement learning guided Spearman dynamic opposite Gradient-based optimizer for numerical optimization and anchor clustering

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