The benefits of co-evolutionary Genetic Algorithms in voyage optimisation

Reducing emissions is of increasing global importance. Within shipping, the International Maritime Organisation's regulations are putting pressure on companies to quickly reduce emissions. One solution is the optimisation of a ship's route where even comparatively small reductions, in the order of 5%, provide sizeable cost and environmental benefits. The most recent advances from the Evolutionary Computation field have not been benchmarked on this problem, especially the co-evolutionary algorithms that provide the widest diversity of search. This paper compares state-of-the-art algorithms on three case studies, to show the impact of algorithm selection on the fuel consumption and expected voyage time. Four state-of-the-art Genetic Algorithms are selected to represent the leading families of Genetic Algorithm. The co-evolutionary approaches are shown to have the top performance, with cMLSGA (co-evolutionary Multi-Level Selection Genetic Algorithm) showing top performance on all the problems with the greatest potential reductions in fuel usage, 7.6% on average over the state of the art, and voyage times, 8.4% on average over the state of the art.

Automated summary

Reducing emissions is becoming increasingly important globally. The regulations of the International Maritime Organisation are pressuring shipping companies to quickly reduce their emissions. Optimizing a ship's route is one solution, with even small reductions leading to significant cost and environmental benefits. This paper compares state-of-the-art algorithms on three case studies and demonstrates the impact of algorithm selection on fuel consumption and expected voyage time. Co-evolutionary algorithms, particularly cMLSGA, show top performance in reducing fuel usage (7.6% on average) and voyage times (8.4% on average) compared to other algorithms.