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Celestial navigation fix based on particle swarm optimization

Tsou M.-C.

Polish Maritime Research

2015

nr 3

20--27

A technique for solving celestial fix problems is proposed in this study. This method is based on Particle Swarm Optimization from the field of swarm intelligence, utilizing its superior optimization and searching abilities to obtain the most probable astronomical vessel position. In addition to being applicable to two-body fix, multi-body fix, and high-altitude observation problems, it is also less reliant on the initial dead reckoning position. Moreover, by introducing spatial data processing and display functions in a Geographical Information System, calculation results and chart work used in Circle of Position graphical positioning can both be integrated. As a result, in addition to avoiding tedious and complicated computational and graphical procedures, this work has more flexibility and is more robust when compared to other analytical approaches.

An Ant Colony Algorithm for efficient ship routing

Tsou M.-C., Cheng H.-C.

Polish Maritime Research

2013

nr 3

28--38

With the substantial rising of international oil price and global warming on the rise, how to reduce operational fuel consumption and decrease air pollution has become one of the pursued goals of green ship. Ship route planning is an indispensible part of the ship navigation process, especially in transoceanic crossing ship routing. The soundness of ship routing not only affects the safety of ship navigation but also the operation economy and environmental protection. This research is based on the platform of Electronic Chart Display and Information System (ECDIS), and founded on Ant Colony Algorithm (ACA) combined with the concept of Genetic Algorithm (GA), to model living organisms optimization behaviour to perform efficient ship route planning in transoceanic crossing. Besides the realization of route planning automation, ship routing will achieve the goal of optimum carbon dioxide reduction and energy conservation, and provide reference for route planning decision.