Y. Y. Wang
L. J. Li

This article introduces two swarm intelligent algorithms, a group search optimizer (GSO) and an artificial fish swarm algorithm (AFSA). A single intelligent algorithm always has both merits in its specific formulation and deficiencies due to its inherent limitations. Therefore, we propose a mixture of these algorithms to create a new hybrid optimization algorithm known as the group search-artificial fish swarm algorithm (GS-AFSA). This algorithm has been applied to three different discrete truss optimization problems. The optimization results are compared with those obtained using the standard GSO, the AFSA and the quick group search optimizer (QGSO). The proposed GS-AFSA eliminated the shortcomings of GSO regarding falling into the local optimum by taking advantage of AFSA’s stable convergence characteristics and achieving a better convergence rate and convergence accuracy than the GSO and the AFSA. Furthermore, the GS-AFSA has a superior convergence accuracy compared to the QGSO, all while solving a complicated structural optimization problem containing numerous design variables.