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Many real-world problems involve two types of problem difficulty: 1) multiple, conflicting objectives and 2) a highly complex search space. Thus, efficient optimization strategies are required that are able to deal with both difficulties. Thus in recent years, intelligent optimization techniques have been a growing interest for solving such complex problems. They model some natural phenomena, based on the principle of evolution (survival of the fittest). They are able to handle problems, which have special features such as discontinuities, multimodality, and disjoint feasible spaces, and any other real complex problems; we focus on how intelligent optimization techniques solve multistage decision making problems.