Volume 30 Issue 3
May  2021
Turn off MathJax
Article Contents
YAN Yan, MA Hongzhong, LI Zhendong. An Improved Grasshopper Optimization Algorithm for Global Optimization[J]. Chinese Journal of Electronics, 2021, 30(3): 451-459. doi: 10.1049/cje.2021.03.008
Citation: YAN Yan, MA Hongzhong, LI Zhendong. An Improved Grasshopper Optimization Algorithm for Global Optimization[J]. Chinese Journal of Electronics, 2021, 30(3): 451-459. doi: 10.1049/cje.2021.03.008

An Improved Grasshopper Optimization Algorithm for Global Optimization

doi: 10.1049/cje.2021.03.008
  • Received Date: 2020-09-06
  • 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.
  • loading
  • Tisa and J.T., “Improved differential evolution algorithm for nonlinear programming and engineering design problems”, Neurocomputing, Vol.148, pp.628–640, 2015.
    Nematzadeh H., Enayatifar R., Motameni H., et al., “Medical image encryption using a hybrid model of modified genetic algorithm and coupled map lattices”, Optics and Lasers in Engineering, Vol.110, pp.24–32, 2018.
    K. Chung, “Some improved algorithms to locate the optimal solutions for exponentially deteriorating items under trade credit financing in a supply chain system”, Computers & Mathematics with Applications, Vol.6, No.9, pp.2353–2361, 2011.
    Sotoudeh-Anvari A. and Hafezalkotob A., “A bibliography of metaheuristics-review from 2009 to 2015”, International Journal of Knowledge Based & Intelligent Engi–neering Systems, Vol.22, No.1, pp.83–95,2018.
    Mota M M. and Piera M A., “An improved time line search algorithm for manufacturing decision-making”, Interna-tional Journal of Food Engineering, Vol.52, No.4, pp.1116–1132,2014.
    Sarwar F., Ahmed M. and Rahman M., “Application of nature inspired algorithms for multi-objective inventory control scenarios”, International Journal of industrial Engineering Computations, Vol.427, No.1, pp.405–420, 2013.
    Nguyen TT. and Vo DN., “An efficient cuckoo bird inspired meta-heuristic algorithm for short-term combined economic emission hydrothermal scheduling”, Ain Shams Engineering Journal, Vol.9, No.4, pp.483–497,2018.
    Pourgholi R.,Dana H. and Tabasi SH., “Solving an inverse heat conduction problem using genetic algorithm: Sequential and multi-core parallelization approach”, Applied Mathmatical Modeling, Vol.38, No.7-8, pp.1948–1958, 2014.
    Mirjalili S., Mirjalili S M. and Lewis A., “Grey wolf optimizer”, Advances in Engineering Software, Vol.69, pp.46–61,2014.
    Mirjalili S and Lewis A. “The whale optimization algorithm”, Advances in Engineering Software, Vol.95, pp.51–67,2016.
    Fausto F., Cuevas E., Valdivia A, et al, “A global optimization algorithm inspired in the behavior of selfish herds”, Biosystems, Vol.160, pp.39–55, 2017.
    Cuevas E. and Cienfuegos M, “A new algorithm inspired in the behavior of the social-spider for constrained optimization”, Expert Systems with Applications, Vol.41, No.2, pp.412–425,2014.
    Tumuluru P. and Ravi B., “GOA–based DBN: Grasshopper optimization algorithm–based deep belief neural networks for cancer classification”, International Journal of Applied Engineering Research, Vol.12, No.24, pp.14218–14231, 2017.
    Mafarja M., Aljarah I., Heidari A A., et al., “Evolutionary population dynamics and grasshopper optimization approaches for feature selection problems”, Knowledge-Based Systems, Vol.145, pp.25–45,2017.
    Barman M., Choudhury N B D. and Sutradhar S., “A regional hybrid GOA-SVM model based on similar day approach for short-term load forecasting in Assam, India”, Energy, Vol.145, pp.710–720, 2018,
    Omar A I., Aleem S H E A., El-Zahab E E A., et al., “An improved approach for robust control of dynam-ic voltage restorer and power quality enhancement using grasshopper optimization algorithm”, ISA Transactions, Vol.95,pp.110–129. 2019.
    Wu J., Wang H., Li N., et al., “Distributed trajecto-ry optimization for multiple solar-powered UAVs target tracking in urban environment by adaptive grasshopper optimization algorithm”, Aerospace Science and Technology, Vol.70, pp. 497–510, 2017.
    Arora S. and Anand P., “Chaotic grasshopper optimiza–tion algorithm for global optimization”, Neural Compu-ting & Applications, Vol.31, No.8, pp.4385–4405, 2019.
    Mirjalili S, Gandomi A H., “Chaotic gravitational constants for the gravitational search algorithm”, Applied Soft Computing, Vol.53, pp.407–419, 2017.
    Saxena A., Shekhawat S. and Kumar R., “Application and development of enhanced chaotic grasshopper optimization algorithms”, Modelling & Simulation in Engineering, Vol.2018, pp.1–14, 2018.
    Ahamed A A., Elaziz M A. and Houssein E H., “Improved grasshopper optimization algorithm using opposition–based learning”, Expert Systems with Application, Vol.112, pp.156–172, 2018.
    Mahmoodabadi MJ. and Babak NR., “ Fuzzy adaptive robust proportional–integral–derivative control optimized by the multi–objective grasshopper optimization algorithm for a nonlinear quadrotor”, Journal of Vibration and Control, Vol.26, No.17–18, pp.1574–1589,2020.
    Alphonsa M M A. and Mohanasundaram N., “A re-formed grasshopper optimization with genetic principle for securing medical data”, Information Security Tech-nical Report, Vol.47, pp.410–420, 2019.
    Luo J., Chen H., Zhang Q., et al., “An improved grasshopper optimization algorithm with application to financial stress prediction”, Applied Mathematical Modelling, Vol.64, pp.654–668. 2018.
    Yue, X. and Zhang H., “Grasshopper optimization algorithm with principal component analysis for global optimization”, Journal of Supercomputing, Vol.7, No.7, pp.5609–5635, 2019.
    Algamal ZY., Qasim M.K., LEE M.H., et al., “QSAR model for predicting neuraminidase inhibitors of influenza A viruses (H1N1) based on adaptive grasshopper optimization algorithm”, Expert Systems with Applications, Vol.31, No.11, pp.803–814,2020.
    Mafarja, M., et al., “Evolutionary population dynamics and grasshopper optimization approaches for feature selection problems”, Knowledge–Based Systems, Vol.145, pp.24–45, 2018.
    Wolpert D H. and Macready W G., “No free lunch theorems for optimization”, IEEE Transactions on Evolu–tionary Computation, Vol.1, No.1, pp.67–82, 1997.
    Saxena, A., “A comprehensive study of chaos embedded bridging mechanisms and crossover operators for grasshopper optimisation algorithm”, Expert Systems with Applications, Vol.132, pp.166–188,2019.
    H. R. Tizhoosh, “Opposition-based learning: A new scheme for machine intelligence”, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Com-merce (CIMCAIAWTIC’06), Vienna, Austria, pp.695–701, 2005.
    Yan Y., Hu J H., Liu Y M., et al, “Doctor Recom-mendation Based on an Intuitionistic Normal Cloud Model Considering Patient Preferences”, Cognitive computation, Vol.12, No.2, pp.460–478, 2020.
    Yang M., Liu D., Cui Y., et al, “Research on complementary algorithm of photovoltaic power missing data based on improved cloud model”, International Transactions on Electrical Energy Systems, Vol.30, No.7, 2020.
    Liu Q., Cui J., Zhao H D., et al., “A soft fault-concept diagnosis method of analog circuits based on cloud model theory” Journal of Networks, Vol.8, No.7, pp.1497–1503, 2013.
    Li F X., Lu M. and Yi S J, “Eutrophication evaluation of lake based on cloud model”, Advanced Materials Research, Vol.955–959, pp.1653–1656, 2014,
    Li W., Du J., Zhao Z., et al., “Fusion of medical sensors using adaptive cloud model in local laplacian pyramid domain”, IEEE Transactions on Biomedical Engineering, Vol.66, No.4, pp.1172–1183, 2019.
    Zhang C., Zhang H., Ma X., et al., “Driving risk assessment in work zones using cloud model”, Mathematical Problems in Engineering, Vol.2018, pp.1–9,2018.
    Wang H., Liu J., Wen G., “An efficient evolutionary structural optimization method with smooth edges based on the game of building blocks”, Engineering Optimization, Vol.51, No.12, pp.2089–2108, 2019.
    Li Y J., Liang K., Tang X J., et al, “Cloud-based adaptive particle swarm optimization for waveband selection in big data”, Journal of signal processing systems for signal, image, and video technology, Vol.90, No.8-9, pp.1105–1113, 2018.
    Dong Y., Zhang Z. and Hong W., “A hybrid seasonal mechanism with a chaotic cuckoo search algorithm with a support vector regression model for electric load forecasting”, Energies. Vol.11, No.1009, pp.1–21,2018.
    Seyedali M., “SCA:A sine cosine algorithm for solving optimization problems”, Knowledge-Based Systems, Vol.96, pp.120–133, 2016.
    Mahadevan K. and Kannan P S., “Comprehensive learning particle swarm optimization for reactive power dispatch”, Applied Soft Computing, Vol.10, No.2, pp.641–652, 2010.
    Mafarja M., Aljarah I., Faris H., et al., “Binary grasshopper optimisation algorithm approaches for feature selection problems”, Expert Systems with Applications, Vol.117, pp.267–286, 2018.
    Chen B., Yan Y., Wang L., et al., “One dimension nlm denoising method based on hasudorff distance and its application in OTLC”, 2019 IEEE Asia Power and Energy Engineering Conference (APEEC), Chengdu, China, pp.75–79, 2019,
  • 加载中


    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (272) PDF downloads(47) Cited by()
    Proportional views


    DownLoad:  Full-Size Img  PowerPoint