Peregrine Falcon Stoop Optimization for Solving The Multi-Objective Travelling Salesman Problem

Document Type : Original Article

Author
ALI Abbas numman
Department of Mathematics, College of Science, Baghdad University, Baghdad 10001, Iraq.
10.24271/psr.2026.572081.2547
Abstract
The Multi-Objective Traveling Salesman Problem (MOTSP) minimizes the distance and cost simultaneously. This phenomenon leads to computational expensive status of exact algorithms, making them unsuitable for large-scale problems. This study proposes a new meta heuristic algorithm, inspired by nature, to solve the MOTSP. The proposed method, named the Peregrine Falcon Swooping Optimization (PFSO), takes any solution found using approximations and improves it by simulating the hunting and swooping actions of a peregrine falcon. In contrast to many meta-heuristic existing methods, the PFSO method does not require a search history to be stored for optimizing a solution. Through a comparative study conducted by analyzing the different methods of branching and cutting algorithms as well as local search algorithms like ant colony optimization, the results indicate that the proposed method is able to provide better results in terms of distance and cost.
Keywords
Multi-objective traveling salesman problem
peregrine falcon dive optimization
inferential algorithms
branching and cutting method
local search
ant colony optimization
Subjects
Passer Journal of Basic and Applied Sciences
Volume 8, Issue 2
July 2026
Pages 1047-1056