Trajectory Optimization of an Industrial Robot Using Teaching–Learning-Based Optimization

Shinde, Vilas B.; Pawar, P. J.

doi:10.1007/978-981-19-9285-8_63

Cited by 1 publication

(1 citation statement)

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“…Song et al introduced the APSO/DU method as a solution to portfolio optimization problems [3]. Shinde et al employed the TLBO method to address the Trajectory Optimization of an Industrial Robot problem [4]. Kumar et al also utilized the TLBO method to tackle the Wind Farm Layout Optimization Problem [5].…”

Section: Introductionmentioning

confidence: 99%

Leaf in Wind Optimization: A New Metaheuristic Algorithm for Solving Optimization Problems

Fang,

Cao

2024

IEEE Access

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This study introduces a novel metaheuristic algorithm, Leaf in Wind Optimization, inspired by the natural phenomenon of falling leaves in the wind. The proposed method simulates the motion response of leaves in varying intensities of wind by establishing models for light wind-driven blades and strong wind-driven blades. The algorithm incorporates motion modes of linear translation and spiral rotation induced by wind, offering a hybrid search framework suitable for both strategies. This approach enables enhanced exploration and exploitation of the search space. The algorithm's performance was evaluated using three challenging benchmark test sets, CEC 2017, CEC 2019 and CEC 2022, as well as an engineering practical problem. Its effectiveness was assessed through comparison with 10 random optimization algorithms, namely: Tree Seed Algorithm, Multi-Verse Optimizer, Salp Swarm Algorithm, Artificial Ecosystem-based Optimization, Hunger Games Search, Fox Optimizer, Spider Wasp Optimizer, AOBLMOA, Enhanced Snake Optimizer, and IbI Logic Algorithm. In the comprehensive testing conducted, the proposed algorithm consistently outperformed other optimizers in approximately 82% of comparisons. Through examination of convergence curves and statistical data, it is evident that Leaf in Wind Optimization demonstrates superior potential compared to the alternative optimizers under consideration.

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Section: Introductionmentioning

confidence: 99%