A deterministic and nature-inspired algorithm for the fuzzy multi-objective path optimization problem

Abstract Increasing evaluation indexes have been involved in the network modeling, and some parameters cannot be described precisely. Fuzzy set theory becomes a promising mathematical method to characterize such uncertain parameters. This study investigates fuzzy multi-objective path optimization problem (FMOPOP), which each arc has multiple crisp weights simultaneously. are characterized by triangular numbers or trapezoidal numbers. We adopt two number ranking methods based on their graded mean values distances from minimum number. Motivated ripple spreading patterns natural water surface, we propose novel ripple-spreading algorithm (RSA) solve FMOPOP. Theoretical analyses prove that RSA can find all Pareto optimal paths source node other nodes within single run. Numerical examples comparative experiments demonstrate efficiency robustness of newly proposed RSA. Moreover, first numerical example, processes illustrated using metaphor-based language phenomena more comprehensible. To best our knowledge, is for FMOPOP various while maintaining optimality.