Path Planning of Welding Robots Based on Developed Whale Optimization Algorithm

Li, Darui; Xiao, Ping; Zhai, Rongjie; Sun, Yihan; Hu, Wenbin; Ji, Wenchao

doi:10.1109/crc52766.2021.9620117

Cited by 3 publications

(1 citation statement)

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“…By continuously evolving the biological characteristics of its own population from an initial state, an optimal solution can be obtained, such as particle swarm optimization (PSO) [2], genetic algorithm (GA) [3], whale optimization algorithm (WOA) [4], grey wolf optimizer (GWO) [5], salp swarm algorithm (SSA) [6], ant colony optimization (ACO) [7], or shuffled frog leaping algorithm (SFLA) [8]. These efficient and robust algorithms have been successfully applied to solve various problems such as complex engineering problems [9][10][11], neural network [12][13][14][15], shortest path optimization [16,17], feature selection [18][19][20], and power scheduling [21].…”

Section: Introductionmentioning

confidence: 99%

CTOA: Toward a Chaotic-Based Tumbleweed Optimization Algorithm

Shao

Pan

2023

Mathematics

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Metaheuristic algorithms are an important area of research in artificial intelligence. The tumbleweed optimization algorithm (TOA) is the newest metaheuristic optimization algorithm that mimics the growth and reproduction of tumbleweeds. In practice, chaotic maps have proven to be an improved method of optimization algorithms, allowing the algorithm to jump out of the local optimum, maintain population diversity, and improve global search ability. This paper presents a chaotic-based tumbleweed optimization algorithm (CTOA) that incorporates chaotic maps into the optimization process of the TOA. By using 12 common chaotic maps, the proposed CTOA aims to improve population diversity and global exploration and to prevent the algorithm from falling into local optima. The performance of CTOA is tested using 28 benchmark functions from CEC2013, and the results show that the circle map is the most effective in improving the accuracy and convergence speed of CTOA, especially in 50D.

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