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Enhanced hyper-cube framework ant colony optimization for combinatorial optimization problems


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Ahmid, Ali, Dao, Thien-My and Le, Ngan Van. 2021. « Enhanced hyper-cube framework ant colony optimization for combinatorial optimization problems ». Algorithms, vol. 14, nº 10.

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Solving of combinatorial optimization problems is a common practice in real-life engineering applications. Trusses, cranes, and composite laminated structures are some good examples that fall under this category of optimization problems. Those examples have a common feature of discrete design domain that turn them into a set of NP-hard optimization problems. Determining the right optimization algorithm for such problems is a precious point that tends to impact the overall cost of the design process. Furthermore, reinforcing the performance of a prospective optimization algorithm reduces the design cost. In the current study, a comprehensive assessment criterion has been developed to assess the performance of meta-heuristic (MH) solutions in the domain of structural design. Thereafter, the proposed criterion was employed to compare five different variants of Ant Colony Optimization (ACO). It was done by using a well-known structural optimization problem of laminate Stacking Sequence Design (SSD). The initial results of the comparison study reveal that the Hyper-Cube Framework (HCF) ACO variant outperforms the others. Consequently, an investigation of further improvement led to introducing an enhanced version of HCFACO (or EHCFACO). Eventually, the performance assessment of the EHCFACO variant showed that the average practical reliability became more than twice that of the standard ACO, and the normalized price decreased more to hold at 28.92 instead of 51.17.

Item Type: Peer reviewed article published in a journal
Dao, Thien-My
Lê, Van Ngan
Affiliation: Génie mécanique, Génie mécanique
Date Deposited: 26 Oct 2021 20:15
Last Modified: 16 Oct 2023 18:12

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