feasible region or search space. Typically, D is some subset of the Euclidean space and is a set with limited elements. The elements of D are called candidate solution or feasible solution. Such a formulation is an optimization problem and the feasible solution that minimizes or maximizes the objective function is the optimal solution. Advanced algorithms We can get the optimal solution by enumerating all possible candidate solutions and comparing the value of objective function. However, it is

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... time-consuming and the computational time is sometime unacceptable for complicated optimization problems. When the general methods are not available to find the optimal solution in an acceptable computational time, advanced algorithms are required to solve these problems. Two fundamental goals are needed to be considered to design advanced algorithms: (a) running time; (b) optimal solution quality. Heuristic algorithm refers to an algorithm that is constructed by intuition or prior experiences and the heuristic algorithm abandons one or both of these goals. For example, some heuristic algorithms give up finding the optimal solution for an improvement in run time. Advanced algorithms belong to heuristic algorithm. Recently, advanced algorithms develop very quickly. These advanced algorithms include tabu search, simulated annealing, genetic algorithms, neural networks algorithms, greedy algorithm, etc. Among these advanced algorithms, greedy algorithms are used to solve optimization problems and sometimes work very well. Greedy algorithms are very simple and straightforward. They are widely used as heuristic algorithms and sometimes are designed to embed into other heuristic algorithms to solve the optimization problem due to its rapid calculation speed and acceptable solution quality.