A Brief Review of Solving Dynamic Optimization Problems

Dynamic environments are those which possess the potentiality of changes over time. These changes can appear on parameters and different environmental agents. In this regard, some problems, called Dynamic Optimization Problems (DOPs), are defined both theoretically and practically in the real world. The most important challenge in solving these kinds of problems is related to how to become compatible with the newly changed environment as much as possible. Therefore, it is necessary to track and follow up the new optimum (optima) of the search space in optimization process. Moreover, finding the optimum (optima) as accurately as possible is another necessity of this compatibility. In order to meet the challenges, researchers intended to benefit from applying swarm intelligence, evolutionary algorithms inspired by the principle of evolving process and a set of special mechanisms. This paper reviews the methods of solving DOPs with some of their previous works. Diversity Scheme, Memory and Diversity Scheme, MultiPopulation Scheme and Other Solutions are emphasized on these methods.