Abstract
This paper presents the challenges and problems faced in dynamic environment optimization problems. Real world problems are mostly dynamic thus such algorithms are required which can track the moving optima. Classical Optimization algorithm searches sequentially for the solution and are based on differential equations. In order to track the moving optima algorithm will have to be restarted and there is no guarantee that algorithm will find the optimum solution. On the other, Evolutionary algorithms are more suitable for such kind of problems as they can find the multiple optima in parallel. Evolutionary algorithms can track the optima and then repeatedly balance the need to exploit known optima with the need to search for new optima. A number of techniques have been proposed to deal with the dynamic environment problems. Different challenges and problems faced in dynamic environment whether it is for a multimodal functions or multi-objective have been discussed here.
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