Picture of UK Houses of Parliament

Leading national thinking on politics, government & public policy through Open Access research

Strathprints makes available scholarly Open Access content by researchers in the School of Government & Public Policy, based within the Faculty of Humanities & Social Sciences.

Research here is 1st in Scotland for research intensity and spans a wide range of domains. The Department of Politics demonstrates expertise in understanding parties, elections and public opinion, with additional emphases on political economy, institutions and international relations. This international angle is reflected in the European Policies Research Centre (EPRC) which conducts comparative research on public policy. Meanwhile, the Centre for Energy Policy provides independent expertise on energy, working across multidisciplinary groups to shape policy for a low carbon economy.

Explore the Open Access research of the School of Government & Public Policy. Or explore all of Strathclyde's 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: 2010 Congress on evolutionary computation (CEC). IEEE Congress on Evolutionary Computation . IEEE, New York. ISBN 9781424481262

Full text not available in this repository.Request a copy from the Strathclyde author

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 solutionincrementally, according to Ant System paradigms. Unlikestandard 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.