Optimization of truss structures using multi-objective cheetah optimizer

Abstract

In this study, a multi-objective version of the recently proposed cheetah optimizer called multi-objective cheetah optimizer (MOCO) has been proposed. MOCO draws inspiration from the targeted hunting strategy employed by cheetahs, which involves a sequence of actions: searching for prey, patiently waiting for the right moment to attack, swiftly launching the attack, and then retreating from the prey and returning to their habitat. MOCO is the result of modification and enhancement from its single-objective counterpart, utilizing a Pareto dominance-based approach. This adaptation allows MOCO to efficiently handle multiple objectives, explores and exploits promising areas in the optimization landscape, and identifies non-dominated solutions, offering valuable tradeoff choices for decision-makers. To demonstrate its practical applications, the MOCO method has been employed to address five intricate structural design problems. These problems involve a pair of competing objectives: the minimization of structural weight and the reduction of maximum nodal displacement. To gauge the efficacy and efficiency of the proposed algorithm, a comparative analysis is conducted against three alternative state-of-the-art multi-objective algorithms. Furthermore, a rigorous evaluation is carried out utilizing hypervolume testing. The findings reveal that the MOCO algorithm surpasses the performance of the other algorithms, underscored by its capacity to uncover a diverse array of non-dominated solutions. To delve deeper into the experimental results, statistical analysis employing Friedman's rank test is employed. The solutions generated and the convergence patterns exhibited by the MOCO approach underscore its exceptional proficiency in resolving intricate design problems.