Opportunistic Self Organizing Migrating Algorithm for Real-Time Dynamic Traveling Salesman Problem

TL;DR

This paper introduces OSOMA, an enhanced meta-heuristic algorithm that effectively solves real-time dynamic TSP problems by generating better solutions through a novel perturbation strategy, outperforming existing algorithms.

Contribution

The paper presents OSOMA, a novel variant of SOMA with an innovative perturbation strategy, applied successfully to real-time dynamic TSP using real-world data.

Findings

OSOMA outperforms SOMA, Differential Evolution, and Particle Swarm Optimization.

OSOMA achieves better solutions on benchmark functions and real-time DTSP.

The perturbation strategy enhances solution diversity and quality.

Abstract

Self Organizing Migrating Algorithm (SOMA) is a meta-heuristic algorithm based on the self-organizing behavior of individuals in a simulated social environment. SOMA performs iterative computations on a population of potential solutions in the given search space to obtain an optimal solution. In this paper, an Opportunistic Self Organizing Migrating Algorithm (OSOMA) has been proposed that introduces a novel strategy to generate perturbations effectively. This strategy allows the individual to span across more possible solutions and thus, is able to produce better solutions. A comprehensive analysis of OSOMA on multi-dimensional unconstrained benchmark test functions is performed. OSOMA is then applied to solve real-time Dynamic Traveling Salesman Problem (DTSP). The problem of real-time DTSP has been stipulated and simulated using real-time data from Google Maps with a varying cost-metric between any two cities. Although DTSP is a very common and intuitive model in the real world, its presence in literature is still very limited. OSOMA performs exceptionally well on the problems mentioned above. To substantiate this claim, the performance of OSOMA is compared with SOMA, Differential Evolution and Particle Swarm Optimization.