Gradient-based/evolutionary relay hybrid for computing Pareto front approximations maximizing the S-metric

Abstract

The computation of a good approximation set of the Pareto front of a multiobjective optimization problem can be recasted as the maximization of its S-metric value. A high-precision method for computing approximation sets of a Pareto front with maximal S-Metric is presented in this paper as a high-level relay hybrid of an evolutionary algorithm and a gradient method, both guided by the S-metric. First, an evolutionary multiobjective optimizer moves the initial population close to the Pareto front. The gradient-based method takes this population as its starting point for computing a local maximal approximation set with respect to the S-metric. As opposed to existing work on gradient-based multicriteria optimization in the new gradient approach we compute gradients based on a set of points rather than for single points. We will term this approach indicatorbased gradient method, and exemplify it for the S-metric. We derive expressions for computing the gradient of a set of points with respect to its S-metric based on gradients of the objective functions. To deal with the problem of vanishing gradient components in case of dominated points in an approximation set, a penalty approach is introduced. We present a gradient based method for the aforementioned hybridization scheme and report on first results on artificial test problems.