43,376 research outputs found
Increasing the density of available pareto optimal solutions
The set of available multi-objective optimization
algorithms continues to grow. This fact can be partially attributed to their widespread use and applicability. However this increase also suggests several issues remain to be addressed satisfactorily. One such issue is the diversity and the number of solutions available to the decision maker (DM). Even for algorithms very well suited for a particular problem, it is difficult - mainly due
to the computational cost - to use a population large enough
to ensure the likelihood of obtaining a solution close to the DMs preferences. In this paper we present a novel methodology that produces additional Pareto optimal solutions from a Pareto optimal set obtained at the end run of any multi-objective optimization algorithm. This method, which we refer to as Pareto estimation, is tested against a set of 2 and 3-objective test problems and a 3-objective portfolio optimization problem to illustrate its’ utility for a real-world problem
Airline Crew Scheduling with Potts Neurons
A Potts feedback neural network approach for finding good solutions to
resource allocation problems with a non-fixed topology is presented. As a
target application the airline crew scheduling problem is chosen. The
topological complication is handled by means of a propagator defined in terms
of Potts neurons. The approach is tested on artificial random problems tuned to
resemble real-world conditions. Very good results are obtained for a variety of
problem sizes. The computer time demand for the approach only grows like
\mbox{(number of flights)}^3. A realistic problem typically is solved within
minutes, partly due to a prior reduction of the problem size, based on an
analysis of the local arrival/departure structure at the single airportsComment: 9 pages LaTeX, 3 postscript figures, uufiles forma
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