To solve real-world problems, we usually formulate these problems as mathematical models and then develop optimization algorithms for them. In this context, optimization algorithms generally find a single best or nearly best solution. However, these mathematical models may overly simplify or even ignore involved factors emerging in real-world problems, which yields that a solution obtained from the mathematical model may not be a solution for the original real-world problem. One possible approach to this issue is to enumerate multiple solutions. This approach also has a serious drawback in that the solutions enumerated are similar to one another, which may potentially defeat the purpose of finding multiple solutions. To fill the gap between real-world problems and mathematical models, diversity of solutions is a significant factor, which has received considerable attention in many fields. In this talk, I will present recent developments for finding diverse solutions in combinatorial optimization problems.