A modified symbiotic organisms search (mSOS) algorithm for optimization of pin-jointed structures

The paper introduces a modified symbiotic organisms search (mSOS) algorithm to optimization of pin-jointed structures including truss and tensegrity ones. This approach is refined from the original SOS with five modifications in the following three phases: mutualism, commensalism and parasitism. In the mutualism one, benefit factors are suggested as 1 to equally represent the level of benefit to each organism, whilst the best organism is replaced by a randomly selected one to increase the global search capability. With the aim of improving the convergence speed, randomly created coefficients in the commensalism phase are restricted in the range [0.4, 0.9]. Additionally, an elitist technique is applied to this phase to filter the best organisms for the next generation as well. Finally, the parasitism phase is eliminated to simplify the implementation and reduce the time-consuming process. To verify the effectiveness and robustness of the proposed algorithm, five examples relating to truss weight minimization with discrete design variables are performed. Additionally, two examples regarding minimization a function of eigenvalues and force densities of tensegrity structures with continuous design variables are considered further. Optimal results acquired in all illustrated examples reveal that the proposed method requires fewer number of analyses than the original SOS and the DE, but still gaining high-quality solutions. Furthermore, the mSOS also outperforms numerous other algorithms in available literature in terms of optimal solutions, especially for problems with a large number of design variables.