Metaheuristic Optimization Methods

A Metaheuristic optimization method can be considered as a more comprehensive intuitive method for the solution of optimization problems. Metaheuristic Optimization Methods developed in recent years can also be considered as metaphor-based optimization methods. All the metaheuristic methods are based on the use of random numbers (probabilistic approaches) in the various stages of the optimization process. Whereas the convergence of the methods such as genetic algorithms, simulated annealing and ant colony optimization has been established, the convergence of most of the recently developed metaheuristic optimization methods does not appear to have been established at the present time. In fact, some researchers think that although the names of the methods might be different, the fundamental ideas used are the same in most of the recently developed metaheuristic optimization methods. The chapter lists the metaphors associated with several of the recently developed metaheuristic optimization methods.