PID-based search algorithm: A novel metaheuristic algorithm based on PID algorithm

Gao, Yuansheng

doi:10.1016/j.eswa.2023.120886

Cited by 33 publications

(2 citation statements)

References 67 publications

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“…Like other ANNs, the performance of KELM is influenced by its hyperparameters. As a result of developments in the past few years, metaheuristic algorithms [7] are now considered a more mature optimization technique and are effectively utilized for Black-box problems like ANN hyperparametric optimization. The Arithmetic Optimization Algorithm (AOA) is a recently developed metaheuristic algorithm introduced in 2021 by Abualigah et al [8] Inspired by basic mathematical operators, it is characterized by a simple structure and high optimization search capability.…”

Section: Introductionmentioning

confidence: 99%

Kernel extreme learning machine optimized by arithmetic optimization algorithm

Li,

Wang,

Peng

et al. 2024

Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024)

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The kernel function is introduced by the Kernel Extreme Learning Machine on the basis of the original Extreme Learning Machine, which effectively improves the capability of prediction of the Extreme Learning Machine, but the hyperparameters of it still affect the model performance to a large extent. As an effective metaheuristic algorithm, the Arithmetic Optimization Algorithm is frequently utilized to address optimization problems. This work introduces the Kernel Extreme Learning Machine optimized by the Arithmetic Optimization Algorithm (AOA-KELM) in an effort to significantly improve the efficiency of the Kernel Extreme Learning Machine. Regularization coefficients and Kernel parameters are the hyperparameters under optimization. On the UCI dataset, the performance of the proposed algorithm is verified through comparison with the Extreme Learning Machine-Grey Wolf Optimizer, Deep Extreme Learning Machine, Backpropagation Algorithm, and Support Vector Machine. The results obtained from the numerical analysis validate the superiority of the proposed method, since the proposed algorithm significantly outperforms its competitors.

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Section: Introductionmentioning

confidence: 99%

Kernel extreme learning machine optimized by arithmetic optimization algorithm

Li,

Wang,

Peng

et al. 2024

Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024)

View full text Add to dashboard Cite

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“…In light of the significance of enhancing meta-heuristic optimization performance, researchers continually propose novel and more efficient meta-heuristic techniques annually. For example, an algorithm inspired by the proportional, integral, and differential theory (Gao, 2023), and an algorithm inspired by the reproduction of snakes (Hashim & Hussien, 2022). They have all been examined in a variety of test function sets and proved to have excellent performance.…”

mentioning

confidence: 99%

Dynamic path planning for mobile robots based on artificial potential field enhanced improved multiobjective snake optimization (APF‐IMOSO)

Li,

Ma,

Weng

2024

Journal of Field Robotics

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With the widespread adoption of mobile robots, effective path planning has become increasingly critical. Although traditional search methods have been extensively utilized, meta‐heuristic algorithms have gained popularity owing to their efficiency and problem‐specific heuristics. However, challenges remain in terms of premature convergence and lack of solution diversity. To address these issues, this paper proposes a novel artificial potential field enhanced improved multiobjective snake optimization algorithm (APF‐IMOSO). This paper presents four key enhancements to the snake optimizer to significantly improve its performance. Additionally, it introduces four fitness functions focused on optimizing path length, safety (evaluated via artificial potential field method), energy consumption, and time efficiency. The results of simulation and experiment in four scenarios including static and dynamic highlight APF‐IMOSO's advantages, delivering improvements of 8.02%, 7.61%, 50.71%, and 12.74% in path length, safety, energy efficiency, and time‐savings, respectively, over the original snake optimization algorithm. Compared with other advanced meta‐heuristics, APF‐IMOSO also excels in these indexes. Real robot experiments show an average path length error of 1.19% across four scenarios. The results reveal that APF‐IMOSO can generate multiple viable collision‐free paths in complex environments under various constraints, showcasing its potential for use in dynamic path planning within the realm of robot navigation.

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