A Novel Center of Mass Optimization (CMO) Algorithm for Truss Design Problems

This paper introduces a novel optimization algorithm rooted in the mass center equations of particle systems. The proposed Center of Mass Optimization (CMO) algorithm is distinguished by its easy implementation, parameter independence, and rapid, accurate solutions. In the proposed CMO, a random walk operator is introduced to enhance the exploitation capability of the CMO and help the search agents jump out of the local optimal. Mutation and elitism selection operators are also used to boost the overall performance of the proposed algorithm. Some mathematical benchmark optimization problems and two engineering truss optimization examples are investigated to evaluate the performance of the proposed method. The results are compared with those of well-known optimization algorithms such as DE, ABC, CBO, PSO, EO, LHHA, and SMA. The results of Wilcoxon rank-sum and ANOVA tests indicate that the performance of the proposed algorithm is robust and reliable for a wide range of complex mathematical and engineering optimization problems.