Abstract
Harmony search (HS) was introduced in 2001 as a heuristic population-based optimisation algorithm. Since then HS has become a popular alternative to other heuristic algorithms like simulated annealing and particle swarm optimisation. However, some flaws, like the need for parameter tuning, were identified and have been a topic of study for much research over the last 10 years. Many variants of HS were developed to address some of these flaws, and most of them have made substantial improvements. In this paper we compare the performance of three recent HS variants: exploratory harmony search, self-adaptive harmony search, and dynamic local-best harmony search. We compare the accuracy of these algorithms, using a set of well-known optimisation benchmark functions that include both unimodal and multimodal problems. Observations from this comparison led us to design a novel hybrid that combines the best attributes of these modern variants into a single optimiser called generalised adaptive harmony search.
Highlights
Harmony search (HS) is a relatively new metaheuristic optimisation algorithm first introduced in 2001 [1]
HS fits into the category of population-based evolutionary algorithms together with genetic algorithms (GAs) and the particle swarm optimisation (PSO) algorithm
We compared the performance of the three HS variants together with the original HS algorithm using a series of test runs over all five benchmark functions
Summary
Harmony search (HS) is a relatively new metaheuristic optimisation algorithm first introduced in 2001 [1]. Journal of Applied Mathematics solution vectors, and they suggested a method of adapting PAR and FW to the relative progress of the optimiser instead of keeping these parameters constant through all iterations Their algorithm alleviates the problem of choosing an exact value for the PAR and FW, but it still requires that a range of values specified by a minimum and maximum value be given. Another early approach to improving HS’s performance and solving the parameter setting problem is the global-best harmony search (GHS) algorithm [10].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
