Interactive fuzzy Bayesian search algorithm: A new reinforced swarm intelligence tested on engineering and mathematical optimization problems

TLDR: In this article, an Interactive Fuzzy Bayesian Search Algorithm (IFBSA) is proposed to adjust the trade-off between exploration and exploitation search behaviors of the swarm-based algorithm.

Abstract: The current study deals with introducing a new probabilistic, self-adaptive, and gradient-free search algorithm. In the proposed method new Bayesian and Fuzzy auxiliary mechanisms are defined and simultaneously employed to extremely adjust the trade-off between exploration and exploitation search behaviors of the swarm-based technique so-called Interactive Search Algorithm (ISA). In this regard, a nine-rule fuzzy decision-making strategy and a hierarchical forecasting Bayesian formulation are developed. The integrated fuzzy and Bayesian mechanisms permanently monitor the search process, and try to dynamically tune the search behavior of each agent based on the governing conditions of the current problem and cause the proposed method to work as a self-adaptive search algorithm. This new search technique is named Interactive Fuzzy Bayesian Search Algorithm (IFBSA) and its performance is tested on a suit of unconstrained mathematical functions and constrained structural and mechanical optimization problems with different properties. Acquired outcomes demonstrate that the proposed IFBSA, thanks to its dual supplementary module, provides promising and superior results in the terms of accuracy, stability, and convergence rate.