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.2018.5.3.5
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/).