Purpose
The purpose of this paper is to propose a new population-based metaheuristic optimization algorithm, assimilation-accommodation mixed continuous ant colony optimization (ACACO), to improve the accuracy of Takagi-Sugeno-Kang-type fuzzy systems design.
Design/methodology/approach
The original N solution vectors in ACACO are sorted and categorized into three groups according to their ranks. The Research Learning scheme provides the local search capability for the best-ranked group. The Basic Learning scheme uses the ant colony optimization (ACO) technique for the worst-ranked group to approach the best solution. The operations of assimilation, accommodation, and mutation in Mutual Learning scheme are used for the middle-ranked group to exchange and accommodate the partial information between groups and, globally, search information. Only the N top-best-performance solutions are reserved after each iteration of learning.
Findings
The proposed algorithm outperforms some reported ACO algorithms for the fuzzy system design with the same number of rules. The performance comparison with various previously published neural fuzzy systems also shows its superiority even with a smaller number of fuzzy rules to those neural fuzzy systems.
Research limitations/implications
Future work will consider the application of the proposed ACACO to the recurrent fuzzy network.
Originality/value
The originality of this work is to mix the work of the well-known psychologist Jean Piaget and the continuous ACO to propose a new population-based optimization algorithm whose superiority is demonstrated.
Hybrid bat-ant colony optimization algorithm for rule-based feature selection in health care
