Fuzzy cognitive maps learning algorithm based on modified covariance matrix adaptation evolution strategy

Abstract

Fuzzy cognitive maps (FCMs) are an efficient soft computing tool commonly used for modeling and analyzing complex systems, and have significant advantages in knowledge representation and fast reasoning. At present, many researchers have devoted themselves to solving the learning problem of FCMs. However, most of the existing learning algorithms are prone to local convergence and are not suitable for large-scale FCMs learning problems. Moreover, the learning problem of FCMs is often a complex non-convex optimization problem, which is easy to cause the final learning result of the algorithm to be only a local optimal solution. To address these limitations, this paper proposes a novel algorithm for learning FCMs by combining multimodal optimization and modified covariance matrix adaptation evolution strategy (CMA-ES), termed as MCMA-ES. First, the learning problem of FCMs is modeled as a multimodal optimization problem (MMOP), and then a multimodal optimization algorithm is proposed by combining the detect multimodal clustering (DMC) and CMA-ES, and the cubic chaos factor is combined to enhance the capability of local search. Finally the proposed algorithm is combined with the decomposition strategy to learn FCMs. Experiments are carried out on 5 benchmark datasets, and MCMA-ES is also applied to 15 groups of large-scale gene regulatory system reconstruction problems. The experimental results show that MCMA-ES has high learning generalization and accuracy.