Picture of classic books on shelf

Literary linguistics: Open Access research in English language

Strathprints makes available Open Access scholarly outputs by English Studies at Strathclyde. Particular research specialisms include literary linguistics, the study of literary texts using techniques drawn from linguistics and cognitive science.

The team also demonstrates research expertise in Renaissance studies, researching Renaissance literature, the history of ideas and language and cultural history. English hosts the Centre for Literature, Culture & Place which explores literature and its relationships with geography, space, landscape, travel, architecture, and the environment.

Explore all Strathclyde Open Access research...

An ant system algorithm for automated trajectory planning

Ceriotti, M. and Vasile, M. (2010) An ant system algorithm for automated trajectory planning. In: World Congress on Computational Intelligence, WCCI 2010, 2010-07-18 - 2010-07-23, Barcelona, Spain.

[img]
Preview
PDF (Ceriotti_M_&_Vasile_M_-_strathprints_-_An_ant_system_algorithm_for_automated_trajectory_planning_Jul_2010.pdf)
Ceriotti_M_&_Vasile_M_-_strathprints_-_An_ant_system_algorithm_for_automated_trajectory_planning_Jul_2010.pdf

Download (402kB) | Preview

Abstract

The paper presents an Ant System based algorithm to optimally plan multi-gravity assist trajectories. The algorithm is designed to solve planning problems in which there is a strong dependency of one decision one all the previously made decisions. In the case of multi-gravity assist trajectories planning, the number of possible paths grows exponentially with the number of planetary encounters. The proposed algorithm avoids scanning all the possible paths and provides good results at a low computational cost. The algorithm builds the solution incrementally, according to Ant System paradigms. Unlike standard ACO, at every planetary encounter, each ant makes a decision based on the information stored in a tabu and feasible list. The approach demonstrated to be competitive, on a number of instances of a real trajectory design problem, against known GA and PSO algorithms.