Elephant clan optimization: A nature-inspired metaheuristic algorithm for the optimal design of structures

The current study proposes a new metaheuristic algorithm based on the clan behavior of elephants, called elephant clan optimization (ECO), to solve structural optimization problems. This method is a new version of the previously developed algorithm; namely, the elephant herding optimization (EHO). While the EHO algorithm has been inspired by the behavior of elephants, the theory behind this method is based on the herding behavior of the elephants, and also the selection of random members to replace the worst members, which is far from the real-life behavior of this animal. Since elephants are animals with powerful memories and a high capability for learning, it seems that by accurately modeling the real-life behavior of this animal, a more powerful algorithm can be developed. The proposed ECO algorithm attempts to simulate the most essential individual and collective behaviors of elephants. The performance of the ECO method is evaluated by solving several structural optimization problems, including the size optimization of truss structures. The findings of the study confirm the reliable performance of the proposed ECO algorithm to expedite the convergence rate and achieve superior solutions in comparison with the EHO. Moreover, the ECO method produces better or very competitive results by consuming less computational effort compared to well-known metaheuristic methods. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

The Elephant Clan Optimization (ECO) algorithm is a new metaheuristic algorithm that aims to simulate the individual and collective behaviors of elephants, achieving superior solutions in structural optimization problems compared to the Elephant Herding Optimization (EHO) algorithm. The ECO method provides competitive results with less computational effort compared to other well-known metaheuristic algorithms.