Many-objective-Optimization

Many-objective optimization problems (MaOPs) usually contain more than three objectives to be optimized simultaneously, which are extended from multi-objective optimization problems (MOPs). Due to the conflicts often arising in different objectives of MOPs, there exists no single optimal solution, but a set of trade-off solutions termed Pareto-optimal set (PS), and the mapping of PS on the objective space is termed Pareto-optimal front (PF) . During the last decades, evolutionary algorithms have become the popular and effective approach for tackling MOPs and MaOPs.