Enhanced Hyper-Cube Framework Ant Colony Optimization for Combinatorial Optimization Problems
نویسندگان
چکیده
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 problems. Those have feature discrete design domain turn them into set NP-hard Determining the right algorithm for such precious point tends to impact overall cost process. Furthermore, reinforcing performance prospective reduces cost. In current study, comprehensive assessment criterion has been developed assess meta-heuristic (MH) solutions structural design. Thereafter, proposed was employed compare five different variants Ant Colony Optimization (ACO). It done by using well-known problem laminate Stacking Sequence Design (SSD). The initial results comparison study reveal Hyper-Cube Framework (HCF) ACO variant outperforms others. Consequently, an investigation further improvement led introducing enhanced version HCFACO (or EHCFACO). Eventually, EHCFACO showed average practical reliability became more than twice standard ACO, normalized price decreased hold at 28.92 instead 51.17.
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ژورنال
عنوان ژورنال: Algorithms
سال: 2021
ISSN: ['1999-4893']
DOI: https://doi.org/10.3390/a14100286