Calculus has been used in solving many scientific and
engineering problems. For optimization problems, however, the
differential calculus technique sometimes has a drawback when the
objective function is step-wise, discontinuous, or multi-modal, or
when decision variables are discrete rather than continuous. Thus,
researchers have recently turned their interests into metaheuristic
algorithms that have been inspired by natural phenomena such as
evolution, animal behavior, or metallic annealing.
This book especially focuses on a music-inspired metaheuristic
algorithm, harmony search. Interestingly, there exists an analogy
between music and optimization: each musical instrument corresponds
to each decision variable; musical note corresponds to variable
value; and harmony corresponds to solution vector. Just like
musicians in Jazz improvisation play notes randomly or based on
experiences in order to find fantastic harmony, variables in the
harmony search algorithm have random values or previously-memorized
good values in order to find optimal solution.
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