Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
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A new neural network---based multiobjective optimization approach is presented, which performs an approximation of the direct problem by means of a neural network, and solves the inverse problem inverting the neural network itself, namely by imposing the value of the desired objective functions and by searching the corresponding value of the design parameters. The search for the Pareto front can be performed directly in the objectives space, rather than in the design parameters, allowing both to uniformly sample the Pareto front, and to limit the computational load. Inverting a neural network corresponds to find the intersection of non convex domains. The proposed inversion algorithm allows to exploit the algorithms available for linear domains, by iteratively evaluating linear approximations of non linear domains, increasing the convergence property. To demonstrate the procedure and the performance of the neural network---based approach, the problem of optimal configuration of an electromagnetic device is selected for analysis and discussion.