Hybrid evolutionary search method for complex function optimisation problems

Hybrid evolutionary search method for complex function optimisation problems

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In this Letter, harmony search (HS) technique hybridised with genetic algorithm (GA) is proposed. This technique mainly takes HS direction estimation mechanism and genetic operators in GA, which significantly increase the convergence of the HS algorithm. Specifically, the authors propose to incorporate main operators of GA into the HS algorithm to avoid some inherent drawbacks of the HS. For example, crossover is incorporated into HS to deal with low accuracy problem, while mutation is incorporated to escape from the local optimum solutions. In addition, elitism is introduced into the HS, to precipitate the performance and prevent the loss of favourable individuals found during the search process. The authors compare the performance of the GA, HS, and other popular HS variants on several benchmark functions. Numerical results show that the proposed hybridisation exhibits a superior performance in comparison to other algorithms.


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