Open AccessAn enhanced adaptive global‐best harmony search algorithm for continuous optimization problems
Author(s) -
Yarmohamadi Hasan,
Zhang Qianyun,
Jiao Pengcheng,
Alavi Amir H.
Publication year - 2020
Publication title -
engineering reports
Language(s) - English
Resource type - Journals
ISSN - 2577-8196
DOI - 10.1002/eng2.12264
Subject(s) - harmony search , benchmark (surveying) , mathematical optimization , computer science , global optimization , algorithm , swarm intelligence , mathematics , particle swarm optimization , geodesy , geography
This paper presents an enhanced adaptive global‐best harmony search (EAGHS) to solve global continuous optimization problems. The global‐best HS (GHS) is one of the strongest versions of the classical HS algorithm that hybridizes the concepts of swarm intelligence and conventional HS. However, randomized selection of harmony in the permissible interval diverts the GHS algorithm from the global optimum. To address this issue, the proposed EAGHS method introduces a dynamic coefficient into the GHS algorithm to increase the search power in early iterations. Various complex and extensively‐applied benchmark functions are used to validate the developed EAGHS algorithm. The results indicate that the EAGHS algorithm offers faster convergence and better accuracy than the standard HS, GHS and other similar algorithms. Further analysis is performed to evaluate the sensitivity of the proposed method to the changes of parameters such as harmony memory consideration rate, harmony search memory, and larger dimensions.