ASSEMBLY SEQUENCE OPTIMIZATION BASED ON EOBSWOA-ACO ALGORITHM

Li Xiang; Wang Yong; Tian De

doi:10.19912/j.0254-0096.tynxb.2023-1695

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (2) : 565-575. DOI: 10.19912/j.0254-0096.tynxb.2023-1695

ASSEMBLY SEQUENCE OPTIMIZATION BASED ON EOBSWOA-ACO ALGORITHM

Li Xiang, Wang Yong, Tian De

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Abstract

Ssembly sequence planning （SP） is the key content of wind turbine design and manufacturing, which has an important impact on the production efficiency and cost. SP problem is a typical NP-complete problem, which needs to use effective methods to find the optimal or near-optimal assembly sequences. However, it is difficult to obtain the parameter values of the general intelligent optimization algorithms, which leads to the limitations of these algorithms on search efficiency and convergence accuracy. To tackle the problem, a multi-strategy hybrid whale-ant colony optimization algorithm for SP problem is proposed. In the calculation process, the parameters of the ant colony algorithm are optimized by the multi-strategy hybrid whale algorithm, which adds the elite reverse learning strategy and differential evolution (DE) algorithm, and then the ant colony algorithm is used to search the optimal or near-optimal assembly sequence. Computational experiments show that the multi-strategy hybrid whale-ant colony optimization algorithm reduces the complexity of parameter setting. Compared with the traditional ACO, the convergence speed and optimization ability of the algorithm are greatly improved in solving SP problems.

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assembly sequence planning / wind turbines / parameter / multi-strategy hybrid whale-ant colony optimization algorithm

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Li Xiang, Wang Yong, Tian De. ASSEMBLY SEQUENCE OPTIMIZATION BASED ON EOBSWOA-ACO ALGORITHM[J]. Acta Energiae Solaris Sinica. 2025, 46(2): 565-575 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1695

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