Authors
Duangjai Jitkongchuen*, Udomlux Ampant
College of Innovative Technology and Engineering, Dhurakij Pundit University, Thailand
*
Corresponding author. Email: [email protected]
Corresponding Author
Duangjai Jitkongchuen
Received 24 March 2018, Accepted 6 June 2018, Available Online 1 December 2018.
DOI
https://doi.org/10.2991/jrnal.2018.5.3.5How to use a DOI?
Keywords
Meta-heuristic; differential evolution algorithm; grasshopper optimization algorithm; optimization
Abstract
This paper proposes a scheme to improve the differential evolution (DE) algorithm performance with integrated the grasshopper optimization algorithm (GOA). The grasshopper optimization algorithm mimics the behavior of grasshopper. The characteristic of grasshoppers is slow movement in the larval stage but sudden movement in the adulthood which seem as exploration and exploitation. The grasshopper optimization algorithm concept is added to DE to guide the search process for potential solutions. The efficiency of the DE/GOA is validated by testing on unimodal and multimodal benchmarks optimization problems. The results prove that the DE/GOA algorithm is competitive compared to the other meta-heuristic algorithms.

Copyright
© 2018 The Authors. Published by ALife Robotics Corp. Ltd.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).