A hybrid algorithm based on state-adaptive slime mold model and fractional-order ant system for the travelling salesman problem

Date

2022-12-15

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

Springer

Type

Article

Peer reviewed

Yes

Abstract

The ant colony optimization (ACO) is one efficient approach for solving the travelling salesman problem (TSP). Here, we propose a hybrid algorithm based on state-adaptive slime mold model and fractional-order ant system (SSMFAS) to address the TSP. The state-adaptive slime mold (SM) model with two targeted auxiliary strategies emphasizes some critical connections and balances the exploration and exploitation ability of SSMFAS. The consideration of fractional-order calculus in the ant system (AS) takes full advantage of the neighboring information. The pheromone update rule of AS is modified to dynamically integrate the flux information of SM. To understand the search behavior of the proposed algorithm, some mathematical proofs of convergence analysis are given. The experimental results validate the efficiency of the hybridization and demonstrate that the proposed algorithm has the competitive ability of finding the better solutions on TSP instances compared with some state-of-the-art algorithms.

Description

open access article

Keywords

Ant system (AS), Slime mold (SM), Fractional-order calculus, Travelling salesman problem (TSP), Convergence proof

Citation

X. Gong, Z. Rong, J. Wang, K. Zhang, and S. Yang. (2022) A hybrid algorithm based on state-adaptive slime mold model and fractional-order ant system for the travelling salesman problem. Complex & Intelligent Systems,

Rights

Research Institute

Institute of Artificial Intelligence (IAI)