MOBBO: A Multiobjective Brown Bear Optimization Algorithm for Solving Constrained Structural Optimization Problems.

Mehta, Pranav; Kumar, Sumit; Tejani, Ghanshyam G.; Mohammad khishe; Majumder, Himadri

The multiobjective (MO) optimizers show great promise in solving constrained engineering structural problems. This paper introduces a MO version of the Brown Bear Optimization (BBO) algorithm, inspired by the foraging behavior of brown bears. The proposed Multiobjective Brown Bear Optimization (MOBBO) algorithm is applied to five structural optimization problems, including 10‐bar, 25‐bar, 60‐bar, 72‐bar, and 942‐bar trusses, aiming to minimize both mass and maximum nodal deflection simultaneously. Comparative evaluations against six benchmark algorithms demonstrate MOBBO's superior convergence, solution diversity, and effectiveness in addressing highly constrained problems. The hypervolume (HV) and inverted generational distance (IGD) metrics place MOBBO in first rank according to the Friedman test, with an average standard deviation of 0.0002. Moreover, the spacing‐to‐extent (STE) and generational distance (GD) metrics rank MOBBO second. The final Friedman rank test highlights MOBBO's overall dominance, achieving a first rank. Best Pareto plots, diversity graphs, and box plot analyses further suggest MOBBO's superior performance and convergence compared to existing algorithms. Therefore, the MOBBO algorithm can be effectively applied to various MO optimization tasks in industry, offering refined global optimization solutions and contributing valuable insights to the field of MO algorithms.

OPTIMIZATION algorithms; BROWN bear; STRUCTURAL engineering; STRUCTURAL optimization; METAL oxide semiconductor field

Journal of Optimization, 2024, Vol 2024, p1

2356-752X

Academic Journal

10.1155/2024/5546940