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Many real world problems are dynamic in the sense that changes occur during the optimisation process. These problems are more convincing in real world applications than the static ones. This is due to the fact that most of the real world applications are dynamic as the problems differ in the changes that occur in the optimisation environment or the size of the problem increases from time to time [1, 2]. This phenomenon can be illustrated by the following example of a delivery company having to render a service to a set of customers where usually, the number of customers to be served changes on the service schedule due to the length of the contract period. Furthermore, the service that is demanded from the customer could also vary over time. This sort of situation could be considered to be a dynamic problem because the parameters would only be revealed during the delivery process where the number of customers or the demand of the product may increase or decrease.

Much effort has been made to solve dynamic optimisation problems over the recent decade [3]. In solving this problem, a solution method that is able to keep track of the changes is much needed. In addition the solution method should be adaptable in line with the current changes. In contrast to static optimisation problems (where the aim is to find the global optima), the goal of dynamic optimisation problems is to find not only the global optima but also to keep track of changes that usually occur during the optimisation process.