Over the last two decades, interest on hybrid metaheuristics has risen considerably in the field of multi-objective optimization (MOP). The best results found for many real-life or academic multi-objective optimization problems are obtained by hybrid algorithms. Combinations of algorithms such as metaheuristics, mathematical programming and machine learning techniques have provided very powerful search algorithms. Three different types of combinations are considered in this paper to solve multi-objective optimization problems: Combining metaheuristics with (complementary) metaheuristics. Combining metaheuristics with exact methods from mathematical programming approaches. Combining metaheuristics with machine learning and data mining techniques.