Co-evolutionary computation for constrained min-max problems and its applications for pursuit-evasion games

Neural network guidance based on pursuit-evasion games with enhanced performance

“…(2) u M (t) or u T (t) implies the time history of the missile's or target's normalized control input described in Eq. ( 1), and r(t) is the range between the missile and the target at time t. This kind of differential game can be solved by some numerical algorithms, such as indirect methods, the gradient-based method (Tahk et al, 1998), the bilevel programming (Ehtamo & Raivio, 2001), and co-evolutionary methods (Kim & Tahk, 2001;Choi, Ryu, Tahk, & Bang, 2004). This work employs the gradient-based method devised by Tahk et al (1998), which is a direct optimization method based on control input parameterization.…”

A co-evolutionary method for pursuit-evasion games with non-zero lethal radii

“…The authors have made efforts to implement the co-evolutionary algorithm into pursuitevasion games by modifying it [8,9]. Both of these two works dealt with the final time problem, and shared a mindset in treating the capture condition: they considered that condition as a constraint of minimax optimization.…”

“…Both of these two works dealt with the final time problem, and shared a mindset in treating the capture condition: they considered that condition as a constraint of minimax optimization. Choi and Tahk [8] introduced a penalty function method, which leads to a bi-matrix game problem, to deal with this constraint, while Kim and Tahk [9] tried to convert the constrained minimax problem into an unconstrained one by adopting an augmented Lagrangian function. Unfortunately, these approaches were not particularly successful.…”