Distributed localized bi-objective search
A new distributed heuristic approach is designed for approximating the Pareto set of bi-objective optimization problems (DLBS). DLBS is based on parallel cooperative computation, objective space decomposition, and adaptive search. DLBS is studied, experimented, and deployed on a cluster of hundreds of core. The DLBS efficiency is measured in terms of approximation quality, computational time and scalability.
multiple objective programming combinatorial optimization parallel and distributed computing evolutionary computation