An Improved Grasshopper Optimization Algorithm for Global Optimization

YAN Yan; MA Hongzhong; LI Zhendong

doi:10.1049/cje.2021.03.008

Volume 30 Issue 3

May 2021

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YAN Yan, MA Hongzhong, LI Zhendong, “An Improved Grasshopper Optimization Algorithm for Global Optimization,” Chinese Journal of Electronics, vol. 30, no. 3, pp. 451-459, 2021, doi: 10.1049/cje.2021.03.008

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YAN Yan, MA Hongzhong, LI Zhendong, “An Improved Grasshopper Optimization Algorithm for Global Optimization,” Chinese Journal of Electronics, vol. 30, no. 3, pp. 451-459, 2021, doi: 10.1049/cje.2021.03.008

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An Improved Grasshopper Optimization Algorithm for Global Optimization

doi: 10.1049/cje.2021.03.008

YAN Yan¹,
MA Hongzhong¹,
LI Zhendong^{2,3
,
,}

1. College of Energy and Electrical Engineering, Nanjing Hohai University, Nanjing 211100, China;
2. School of Information Engineering, Ningxia University, Yinchuan 750021, China;
3. Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-founded by Ningxia Municipality and Ministry of Education, Ningxia University, Yinchuan 750021, China

Received Date: 2020-09-06

Abstract

Abstract

We proposes an improved grasshopper algorithm for global optimization problems. Grasshopper optimization algorithm (GOA) is a recently proposed meta-heuristic algorithm inspired by the swarming behavior of grasshoppers. The original GOA has some drawbacks, such as slow convergence speed, easily falling into local optimum, and so on. To overcome these shortcomings, we proposes a grasshopper optimization algorithm based on a logistic Chaos maps opposition-based learning strategy and cloud model inertia weight (CCGOA). CCGOA is divided into three stages. The chaos opposition learning initialization strategy is used to initialize the population, so that the population can be evenly distributed in the feasible solution space as much as possible, so as to improve the uniformity and diversity of the initial population distribution of the grasshopper algorithm. The inertia weight cloud model is introduced into the grasshopper algorithm, and different inertia weight strategies are used to adjust the convergence speed of the algorithm. Based on the principle of chaotic logistic maps, local depth search is carried out to reduce the probability of falling into local optimum. Fourteen benchmark functions and an engineering example are used for simulation verification. Experimental results show that the proposed CCGOA algorithm has superior performance in determining the optimal solution of the test function problem.

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References(43)

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