Operational Optimal Ship Routing Using a Hybrid Parallel Genetic Algorithm

TL;DR

This paper presents a hybrid parallel genetic algorithm for optimal ship routing that efficiently handles complex environments, outperforming traditional methods in speed and accuracy, especially in challenging maritime conditions.

Contribution

It introduces a novel hybrid parallel genetic algorithm within an island model framework for operational ship routing, applicable to large-scale and complex maritime environments.

Findings

Fast computation in complex environments

Effective handling of micro-climate conditions

Applicable to clusters and grids

Abstract

Optimization of ship routing depends on several parameters, like ship and cargo characteristics, environmental factors, topography, international navigation rules, crew comfort etc. The complex nature of the problem leads to oversimplifications in analytical techniques, while stochastic methods like simulated annealing can be both time consuming and sensitive to local minima. In this work, a hybrid parallel genetic algorithm - estimation of distribution algorithm is developed in the island model, to operationally calculate the optimal ship routing. The technique, which is applicable not only to clusters but to grids as well, is very fast and has been applied to very difficult environments, like the Greek seas with thousands of islands and extreme micro-climate conditions.