Akademik Veri Yönetim Sistemi
MATERIALS TESTING, 2024 (SCI-Expanded)
The primary objective of numerous optimization problems is to enhance a single metric whose lowest or highest value accurately reflects the response quality of a system. However, in some instances, relying solely on one metric is not practical, leading to the consideration of multi-objective (MO) optimization problems that aim to improve multiple performance indicators simultaneously. This approach requires the use of a multi-objective optimization method adept at handling the intricacies of scenarios with various indices. Consequently, researchers have not explored multi-objective truss optimization as extensively as single-objective (SO) scenarios. The novel multi-objective Lichtenberg algorithm with two archives (MOLA-2arc) has been developed to address this. The efficacy of MOLA-2arc is evaluated against eight other MO algorithms, including the multi-objective bat algorithm (MOBA), multi-objective crystal structure algorithm (MOCRY), multi-objective cuckoo search (MOCS), multi-objective firefly algorithm (MOFA), multi-objective flower pollination algorithm (MOFPA), multi-objective harmony search (MOHS), multi-objective jellyfish search (MOJS) algorithm, and the original multi-objective Lichtenberg algorithm (MOLA). The challenge is to minimize structural mass and compliance while adhering to stress limitations. The outcomes demonstrate that MOLA-2arc shows notable improvements over its predecessor, MOLA, and surpasses all other competing algorithms in this study.