Grasshopper inspired artificial bee colony algorithm for numerical optimisation
Nirmala Sharma, HARISH KUMAR SHARMA, Ajay Kumar Sharma, Jagdish Chand Bansal · Journal of Experimental & Theoretical Artificial Intelligence · 2018
Swarm intelligence (SI)-based algorithms are performing very well in the field of optimisation over the past few decades. A lot of new SI-based algorithms are being developed. The existing algorithms are also modified, mostly, either by hybridising them with some other algorithms or by incorporating local search techniques. This research presents a new local search strategy based on grasshopper (GH) jumping mechanism. The proposed local search strategy is termed as GH local search strategy. Further, the proposed strategy is incorporated into an efficient SI-based algorithm, artificial bee colony (ABC) algorithm. The proposed hybridised algorithm is termed as GH inspired ABC (GHABC) algorithm. The proposed GHABC is tested on 37 numerical benchmark optimisation functions. The results indicate that the proposed GHABC algorithm is a competent approach for solving numerical optimisation problems.