An efficient cluster-based tribes optimization algorithm for functional-link-based neurofuzzy inference systems

This study presents an efficient cluster-based tribes optimization algorithm (CTOA) for designing a functional-link-based neurofuzzy inference system (FLNIS) for prediction applications. The proposed CTOA learning algorithm was used to optimize the parameters of the FLNIS model. The proposed CTOA adopts a self-clustering algorithm to divide the swarm into multiple tribes, and uses different displacement strategies to update each particle. The CTOA also uses a tribal adaptation mechanism to generate or remove particles and reconstruct tribal links. The tribal adaptation mechanism can improve the quality of the tribe and the tribe adaptation. In CTOA, the displacement strategy and the tribal adaptation mechanism depend on the tribal leaders to strengthen the local search ability. Finally, the proposed FLNIS-CTOA method was applied to several prediction problems. The results of this study demonstrate the effectiveness of the proposed CTOA learning algorithm.

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► This study presents a cluster-based tribes optimization algorithm (CTOA) for designing the neurofuzzy inference system.
► CTOA adopts a self-clustering algorithm to divide the swarm into multiple tribes for evolution.
► In CTOA, the displacement strategy adopts the tribal leaders, enabling them to strengthen the local search ability.
► In CTOA, the tribal adaptation mechanism includes particle removal and generation to improve the quality of the tribe.
► CTOA can provide superior results compared to that using other methods in predictive applications.