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.
Other articles related to "harmony search":
... Harmony search lies in the fields of Evolutionary computing Metaheuristics Stochastic optimization Optimization Other evolutionary computing methods include Evolutionary algorithms, including Genetic ...
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