Evolving a pareto front for an optimal bi-objective robust interception problem with imperfect information

In this paper, a multi-objective optimal interception problem with imperfect information is solved by using a Multi-Objective Evolutionary Algorithm (MOEA). The traditional setting of the interception problem is aimed either at minimizing a miss distance for a given interception duration or at minimizing an interception time for a given miss distance. In contrast with such a setting, here the problem is posed as a simultaneous search for both objectives. Moreover, it is assumed that the interceptor has imperfect information on the target. This problem can be considered as a game between the interceptor, who is aiming at a minimum final distance between himself and the target at a minimal final time, and an artificial opponent aiming at maximizing these values. The artificial opponent represents the effect of the interceptor's imperfect information (measurement inaccuracies) on the success of the interception. Both players utilize neural net controllers that evolve during the evolutionary optimization. This study is the first attempt to utilize evolutionary multi-objective optimization for solving multi-objective differential games, and as far as our review went, the first attempt to solve multi-objective differential games in general.