An efficient imperialist competitive algorithm with likelihood assimilation for topology, shape and sizing optimization of truss structures

This article presents an efficient hybrid meta-heuristic algorithm for topology, layout and sizing optimization of truss structures. A new assimilation scheme is implemented in the imperialist competitive algorithm (ICA) in order to improve computational efficiency, the likelihood of occurrence and the neighborhood patterns are used, and the assimilation step of the ICA is enhanced. In this method, the probabilities are assigned to each alternative by the imperialist and its neighbors in the search space; then, the colonies construct new solutions (moving to the relevant imperialist) based on the likelihood of occurrence. Neighborhood patterns are proposed to gather information from the neighboring countries in order to extract features based on the local power variation. In this study, the extended abilities of the proposed algorithm are inspired from the dolphin echolocation (DE) algorithm and the cellular automata (CA) method, which the new algorithm is denoted as CA-ICEA. The optimization results obtained by ICA, DE and CA-ICEA methods are compared. Remarkably, the proposed algorithm outperforms its competitors in terms of optimum weights, their mean and standard deviation. (c) 2020 Elsevier Inc. All rights reserved.

Automated summary

This article introduces an efficient hybrid meta-heuristic algorithm for optimizing truss structures, which implements a new assimilation scheme in the imperialist competitive algorithm (ICA) to improve computational efficiency and achieve better optimization results. The algorithm outperforms competitors in terms of optimum weights, mean, and standard deviation.