Application of the Modified 2-opt and Jumping Gene Operators in Multi-Objective Genetic Algorithm to solve MOTSP

TL;DR

This paper enhances a multi-objective genetic algorithm for the MOTSP by integrating a modified 2-opt local search and a novel Jumping Gene mutation operator, improving solution quality for complex TSP instances.

Contribution

It introduces an expanded 2-opt local search and a new Jumping Gene mutation operator tailored for the MOTSP, advancing local search techniques in multi-objective optimization.

Findings

Improved solution quality on KroAB100 city instances

Enhanced convergence speed of the genetic algorithm

Effective integration of local search with genetic operators

Abstract

Evolutionary Multi-Objective Optimization is becoming a hot research area and quite a few papers regarding these algorithms have been published. However the role of local search techniques has not been expanded adequately. This paper studies the role of a local search technique called 2-opt for the Multi-Objective Travelling Salesman Problem (MOTSP). A new mutation operator called Jumping Gene (JG) is also used. Since 2-opt operator was intended for the single objective TSP, its domain has been expanded to MOTSP in this paper. This new technique is applied to the list of KroAB100 cities.