Optimization algorithms for multi-objective problems with fuzzy data
Abstract
This paper addresses multi-objective problems with
fuzzy data which are expressed by means of triangular fuzzy
numbers. In our previous work, we have proposed a fuzzy Pareto
approach for ranking the generated triangular-valued functions.
Then, since the classical multi-objective optimization methods can
only use crisp values, we have applied a defuzzification process.
In this paper, we propose a fuzzy extension of two well-known
multi-objective evolutionary algorithms: SPEA2 and NSGAII by
integrating the fuzzy Pareto approach and by adapting their
classical techniques of diversity preservation to the triangular
fuzzy context. An application on multi-objective Vehicle Routing
Problem (VRP) with uncertain demands is finally proposed and
evaluated using some experimental tests.