Many problems in science and engineering can be formulated as optimization problems, subject to complex, nonlinear constraints. The solutions of optimization problems often require sophisticated optimization techniques. Traditional algorithms may struggle to deal with such highly nonlinear problems. Nature-inspired algorithms can be good alternatives, and they are flexible and efficient for solving problems in optimization, data mining and machine learning. This chapter introduces the fundamentals of algorithms, classification of optimization problems and algorithms as well as a brief history of metaheuristics.
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