Chaotic grasshopper optimization algorithm for global optimization
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Grasshopper optimization algorithm (GOA) is a new meta-heuristic algorithm inspired by the swarming behavior of grasshoppers. The present study introduces chaos theory into the optimization process of GOA so as to accelerate its global convergence speed. The chaotic maps are employed to balance the exploration and exploitation efficiently and the reduction in repulsion/attraction forces between grasshoppers in the optimization process. The proposed chaotic GOA algorithms are benchmarked on thirteen test functions. The results show that the chaotic maps (especially circle map) are able to significantly boost the performance of GOA.
KeywordsGrasshopper optimization algorithm Chaotic maps Global optimization problem Multimodal function
The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper.
Compliance with ethical standards
Conflicts of interest
The authors declare that they have no conflict of interest. This manuscript does not contain any studies with human participants or animals performed by any of the authors. Authors have carefully checked the “Instructions for Authors” and certify that the manuscript complies with the Ethical Rules applicable for this journal.
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