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1 2003

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A sequential multiobjective optimization tool designed to manage on line a set of varied algorithms

Rodon A., Collette Y., Siarry P.

Foundations of Computing and Decision Sciences

2003

Vol. 28, No. 4

247-271

We present a new sequential multiobjective optimization tool, called SMO, aimed at automatically choosing the most appropriate optimization method to be applied in order to solve a given problem. To make such choices, our program studies the structure of the set of found solutions in objective function space, using a new set of metrics : in particular, we use the Pareto ratio to determine how the solutions are placed in objective function space in relation to the Pareto surface, a correlation metric to compare the shapes of the practical and theoretical Pareto surfaces, and the "segment-dominance metric" to study the convexity of those surfaces. Some tests on academic benchmark problems are presented in this paper. A work under development is the application of SMO to a particular problem, namely the working out of the nuclear fuel loading patterns in pressurized water reactors, which is a difficult combinatorial multiobjective optimization problem.