Opposed Pheromone Ant Colony Optimization for Property Identification of Nonlinear Structures

Opposed pheromone ant colony optimization (OPACO) is an enhanced algorithm that incorporates opposition-based learning within the structure of ant colony optimization (ACO). The proposed algorithm aims to enhance the optimization process by leveraging the concept of opposed pheromones. In this algorithm, ants follow pheromone trails while exhibiting both exploration and exploitation behavior. However, unlike traditional ACO algorithms, this approach introduces the notion of opposed pheromones, which represent contrasting solutions or directions. The opposed pheromones encourage diversification in the search space, enabling a more comprehensive exploration. By leveraging the contrasting pheromones to guide the ants toward different regions of the search space, the algorithm offers improved convergence speed and solution quality. The efficiency of the proposed method is illustrated specifically by focusing on the system identification of the nonlinear structure to find the mechanical properties such as mass and stiffness under seismic load. The nonlinearity of the structure is defined using the bilinear model of the utilized elements. The results indicate that the algorithm obtains better accuracy and efficiency in identifying the solutions compared to traditional ACO. By incorporating opposition-based learning and opposed pheromones, the algorithm offers an effective technique that advances the field of structural optimization.