A dominance tree and its application in evolutionary multi-objective optimization

Dynamic multi-objective optimization based on membrane computing for control of time-varying unstable plants

“…The literature on SMOOPs and DSOOPs has been greatly expanding with evolutionary algorithms being one of the most popular approaches [11,23,31,39] for solving practical problems. Multi-objective evolutionary algorithms can find a set of Pareto optimal solutions (POS) instead of a single optimal solution in a single simulation run [10,30,32,35].…”

Convergence of multi-objective evolutionary algorithms to a uniformly distributed representation of the Pareto front

“…A wide variety of multi-objective optimization algorithms (MOEAs), such as SPEA [52], SPEA2 [54], PAES [30], NSGA-II [13], IBEA [55], MSOPS [25], SMS-EMOA [16,2], MOEA/D [49,50] and MDMOEA [56], have been proposed and successfully applied to a number of real-world multi-objective optimization problems (MOPs). In addition, independent strategies have also been proposed to improve the efficiencies of MOEAs [17,45,48,6,37,46]. However, the theoretical foundations of this area are still very weak.…”