Chaotic enhanced colliding bodies algorithms for size optimization of truss structures
Colliding bodies optimization (CBO) is a recently developed population-based metaheuristic algorithm that mimics the collision between two bodies, where the momentum conservation law is utilized to determine the new positions of the agents in the search space. To overcome some deficiencies in the CBO like slow convergence and getting trapped in local minima, an enhanced version of the algorithm, ECBO, is proposed. One of the efficient techniques to improve the performances of the metaheuristic algorithms is adding chaos to their structure. In this paper, chaos is incorporated into the ECBO through three types of embeddings and ten chaotic maps. Proposing different chaotic versions, finding the best version among chaotic versions and improving the efficiency of the standard CBO and ECBO are the main achievements of this study. The results of examining some mathematical and engineering problems show how some chaotic ECBO variants can enhance the performance of the standard ECBO.
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