<p>This paper suggests an enhancement to the multi-objective thermal exchange optimization (MOTEO) algorithm that is referred to as the multi-strategy infused improved MOTEO algorithm (IMOTEO) to be used in engineering applications of truss structure optimization. The IMOTEO algorithm has the most recent features of the variation of the polynomials, the population-based incremental learning selection according to the genetic algorithm elite strategy. These adjustments increase the exploration and exploitation capacity of the algorithm to perform well to solve multi-objective optimization problems of high complexity. The suggested algorithm is also used to optimize mass and maximum nodal displacement of five planar and spatial truss structures. IMOTEO performance is compared with eight state-of-the-art multi-objective optimization algorithms on the basis of metrics of hypervolume, generational distance, inverted generational distance, spacing, and extent. The results indicate that IMOTEO is superior to the compared algorithms in the sense of greater diversity and convergence in the solution space. In addition, the Friedman rank test of a statistical test declares the consistency of IMOTEO at rank one compared to the other algorithms. These findings, thus, established that the addition of multi-strategy elements in the MOTEO framework enhances the performance of the framework in solving complex structural optimization problems substantially. An algorithm such as IMOTEO can be potentially useful in addressing multi-objective optimization problems in the engineering field of truss structure design, among others.</p> Graphical abstract <p></p>