Sizing optimization of truss structures using hybrid whale optimization algorithm

The whale optimization algorithm (WOA) is a swarm-based algorithm that mainly mimics the social behaviors of humpback whales in finding food resources and hunting techniques. Although the effectiveness of the WOA algorithm has been proved through its success in handling different optimization and engineering problems, it has notable disadvantages such as slow convergence and local optima trap when dealing with rugged search space. In this chapter, a new hybrid variant of WOA, called HWOA, is proposed for the sizing optimization of truss structure engineering problems with discrete and continuous design variables. In HWOA, the adaptive β-hill climbing optimizer as a local search-based procedure is integrated within the framework of the WOA to speed up convergence and enhance the exploitation ability and thus make a balance between exploration and exploitation abilities. Four classical truss structures datasets (i.e., 200-bar, 72-bar, 25-bar, and 10-bar) with their variants are used for evaluation purposes. The performance is evaluated by comparing the proposed HWOA with several state-of-the-art methods using the same dataset instances. The simulation results demonstrated that the proposed HWOA algorithm performs better than or is similar to other algorithms in five cases of the four problems, while the results of the proposed algorithm are competitive with other competitors in the remaining five cases.