Chapter 4: Global Optimization Using Population-Based Metaheuristics

Optimization methods outlined in Chapter 3 are local ones, i.e., they normally allow for finding an optimum that is located in the vicinity of the initial solution. Unfortunately, in many practical problems, objective functions with multiple optima have to be handled. Furthermore, the functional landscape of the problem at hand is often unknown in terms of the nonlinearity of the objective function, importance of particular variables, and also the number and the location of the optima. At the same time, estimating a reasonably good starting point is often very difficult. In all these cases, utilization of local methods usually leads to unsatisfactory results and global search may be necessary…