Abstract
Grasshopper optimization algorithm (GOA) is a novel meta-heuristic algorithm for solving single-objective numeric optimization problems. While it has a simple principle and it is easy to implement, grasshopper optimization algorithm performs badly in some aspects. GOA cannot make full utilization of every iteration, and it is not good at getting rid of local optima. To solve these problems and improve the performance of GOA, this paper proposed an improved grasshopper optimization algorithm based on dynamic weight mechanism and random jumping strategy (DJGOA). The dynamic weight mechanism promoted the utilization of the iterations of the algorithm. The random jumping strategy was introduced to help the algorithm jumping out of the local optima. Several experiments relating to 13 benchmark functions and 4 algorithms were conducted to demonstrate the performance of DJGOA. The results of the experiments demonstrated that DJGOA performed better than GOA and the other algorithms.
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Zhao, R., Ni, H., Feng, H., Zhu, X. (2019). A Dynamic Weight Grasshopper Optimization Algorithm with Random Jumping. In: Bhatia, S., Tiwari, S., Mishra, K., Trivedi, M. (eds) Advances in Computer Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 924. Springer, Singapore. https://doi.org/10.1007/978-981-13-6861-5_35
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DOI: https://doi.org/10.1007/978-981-13-6861-5_35
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