Optimum structural design of spatial truss structures via migration-based imperialist competitive algorithm

The current study presents a new hybrid algorithm generated by combining advantageous features of Imperialist Competitive Algorithm (ICA) and Biogeography-Based Optimization (BBO) to establish an effective search technique. Although the ICA performs fairly well at the exploration phase, it is less effective at the exploitation stage. In addition, its convergence speed is problematic in some instances. Meanwhile, the migration operator of BBO method strongly emphasizes the local search to find the optimum solution more precisely. The combination of these two algorithms generates a robust hybrid algorithm that enjoys both exploratory and exploitative functionalities. The proposed hybrid algorithm is called Migration-Based Imperialist Competitive Algorithm (MBICA). To validate its performance, MBICA is used to optimize a variety of benchmark truss structures. Compared to some other methods, this algorithm converges to better or at least identical solutions by reducing the required number of structural analyses. Finally, the results from the standard BBO, ICA, and other recently developed metaheuristic optimization methods were compared with those obtained in this study. (c) 2022 Sharif University of Technology. All rights reserved.

Automated summary

This study presents a new hybrid algorithm, Migration-Based Imperialist Competitive Algorithm (MBICA), which combines the advantageous features of Imperialist Competitive Algorithm (ICA) and Biogeography-Based Optimization (BBO) to establish an effective search technique. Compared to other methods, MBICA converges faster and achieves better solutions in structural optimization.