Harmony search (HS) is a metaheuristic algorithm (also known as soft computing algorithm or evolutionary algorithm) mimicking the improvisation process of musicians. In the process, each musician plays a note for finding a best harmony all together. Likewise, each decision variable in optimization process has a value for finding a best vector all together. Shortcut: WP:-( Vandalism is indisputable bad-faith addition, deletion, or change to content, made in a deliberate attempt to compromise the integrity of the encyclopedia. ... Shortcut: WP:-( Vandalism is indisputable bad-faith addition, deletion, or change to content, made in a deliberate attempt to compromise the integrity of the encyclopedia. ... Image File history File links Information_icon. ... A metaheuristic is a heuristic method for solving a very general class of computational problems by combining user given black-box procedures — usually heuristics themselves — in a hopefully efficient way. ... Soft Computing refers to a collection of computational techniques in computer science, artificial intelligence, machine learning and some engineering disciplines, which attempt to study, model, and analyze very complex phenomena: those for which more conventional methods have not yielded low cost, analytic, and complete solutions. ... In artificial intelligence, an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. ...

Harmony search applications

The HS algorithm had been successful in a wide variety of optimization problems in the following fields.

Bench-mark problems

• Traveling sales person problem
• Rosenbrock's banana function
• Six-hump camel back function

Real-world problems

• Water distribution network design
• Structural design
• Vehicle routing
• Satellite heat pipe design
• Hydrologic parameter calibration
• Multiple dam scheduling
• Offshore structure mooring
• Music composition
• Sudoku puzzle solving

Harmony search features

HS has several advantages when compared with traditional gradient-based mathematical optimization techniques as follows:

• HS does not require complex calculus, thus it is free from divergence.
• HS does not require initial value settings for the decision variables, thus it may escape local optima.
• HS can handle discrete variables as well as continuous variables, while gradient-based techniques handle continuous variables only.

Also, the HS algorithm could overcome the drawback of genetic algorithm's building block theory by considering the relationship among decision variables using its ensemble operation.

Other Related Algorithms

• Genetic algorithms
• Simulated annealing
• Tabu search
• Ant colony optimization

A genetic algorithm (GA) is an algorithm used to find approximate solutions to difficult-to-solve problems through application of the principles of evolutionary biology to computer science. ... Simulated annealing (SA) is a generic probabilistic meta-algorithm for the global optimization problem, namely locating a good approximation to the global optimum of a given function in a large search space. ... Tabu search is a mathematical optimization method, belonging to the class of local search techniques. ... The ant colony optimization algorithm (ACO), introduced by Marco Dorigo [Dor92,DoSt04], is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. ...

References

• Harmony Search Algorithm