Harmony Search

In computer science and operations research, harmony search (HS) is a phenomenon-mimicking algorithm (also known as metaheuristic algorithm, soft computing algorithm or evolutionary algorithm) inspired by the improvisation process of musicians. In the HS algorithm, each musician (= decision variable) plays (= generates) a note (= a value) for finding a best harmony (= global optimum) all together. The Harmony Search algorithm has the following merits:

• HS does not require differential gradients, thus it can consider discontinuous functions as well as continuous functions.
• HS can handle discrete variables as well as continuous variables.
• HS does not require initial value setting for the variables.
• HS is free from divergence.
• HS may escape local optima.
• HS may overcome the drawback of GA's building block theory which works well only if the relationship among variables in a chromosome is carefully considered. If neighbor variables in a chromosome have weaker relationship than remote variables, building block theory may not work well because of crossover operation. However, HS explicitly considers the relationship using ensemble operation.
• HS has a novel stochastic derivative applied to discrete variables, which uses musician's experiences as a searching direction.
• Certain HS variants do not require algorithm parameters such as HMCR and PAR, thus novice users can easily use the algorithm.