Rajani Kumari
Anand Nayyar
ABSTRACT
The grasshopper optimization algorithm (GOA) mimics the foraging behavior of grasshopper insects. It is one of the youngest and widespread algorithms for optimization. In GOA exploration and exploitation depends on coefficient c used in position update process. So as to improve balancing in exploration and exploitation this paper introduced modified coefficient c for fine tuning these to contradictory process while searching for optimum solution. The new value of c is decided adaptively and stimulated by hyperbolic function. The anticipated algorithm is named as modified GOA (mGOA) and tested over a standard set of benchmark problems. Outcomes proves that mGOA outperformed considered algorithm for more than 90% problems.
- Anand Nayyar, Dac-Nhuong Le, and Nhu Gia Nguyen. 2018. Advances in swarm intelligence for optimizing problems in computer science, Boca Raton, FL: CRC Press, Taylor & Francis Group. Google Scholar
- Kumar S, Kumari R. 2018. Artificial bee colony, firefly swarm optimization, and bat algorithms. Advances in swarm intelligence for optimizing problems in computer science. 145--82.Google Scholar
- Kennedy J, Eberhart R. 1995. Particle swarm optimization. In Proceedings of ICNN'95-International Conference on Neural Networks (Vol 4, pp. 1942--1948). IEEE.Google ScholarCross Ref
- Pawan Bhambu, Sandeep Kumar, and Kavita Sharma. 2017. Self balanced particle swarm optimization. International Journal of System Assurance Engineering and Management 9, 4 (August 2017), 774--783. Google ScholarCross Ref
- Dervis Karaboga. 2010. Artificial bee colony algorithm. Scholarpedia 5, 3 (2010), 6915. Google ScholarCross Ref
- Kumar S, Nayyar A, Kumari R. 2019. Arrhenius artificial bee colony algorithm. In International conference on innovative computing and communications (pp. 187--195). Springer, Singapore.Google ScholarCross Ref
- Dorigo M, Di Caro G. 1999. Ant colony optimization: a new meta-heuristic, In Proceedings of the 1999 congress on evolutionary compulation-CEC99 (Cat. No. 99TH8406) (Vol. 2, pp. 1470--1477).IEEE.Google ScholarCross Ref
- Kumar S, Nayyar A, Paul A, 2019. Swarm Intelligence and Evolutionary Algorithms in Healthcare and Drug Development Chapman and Hall/CRC.Google Scholar
- J.H. Holland. 1992. "Genetic algorithms," Scientific American. 267(1):66--73.Google ScholarCross Ref
- Kumar S, Jain S, Sharma H. 2018. Genetic algorithms. Advances in swarm intelligence for optimizing problems in computer science. 27--52.Google Scholar
- Price K, Storn RM, Lampinen JA 2006. Differential evolution: a practical approach to global optimization. Springer Science & Business Media; 2006 Mar 4. Google Scholar
- Jain S, Sharma VK, Kumar S. Peregrine Preying Pattern-Based Differential Evolution. In Soft Computing: Theories and Applications 2020 (pp. 375--383). Springer, Singapore.Google Scholar
- Jain S, Sharma VK, Kumar S. 2020. Robot Path Planning Using Differential Evolution. In Advances in Computing and Intelligent Systems (pp. 531--537). Springer, Singapore.Google Scholar
- Yang, X. S., & Deb, S. (2009). Cuckoo search via Levy flights. In 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC) (pp. 210--214). IEEE.Google ScholarCross Ref
- Krishnanand K. N., & Ghose, D. (2006). Glow worm swarm based optimization algorithm for multimodal functions with collective robotics applications. Multiagent and Grid Systems, 2(3), 209--222. Google ScholarDigital Library
- Li. X. L., Shao, Z. J., Qian, J. X. (2002), An Optimizing Method based on Autonomous Animate: Fish Swarm Algorithm, System Engineering Theory and Practice 22 (11), 32--38.Google Scholar
- Bansal JC, Sharma H, Jadon SS, Clerc M. 2014. Spider monkey optimization algorithm for numerical optimization. Memetic computing. 6(1):31--47.Google Scholar
- Sharma B, Sharma VK, Kumar S. 2020. Sigmoidal Spider Monkey Optimization Algorithm. In Soft Computing: Theories and Applications (pp. 109--117). Springer, Singapore.Google Scholar
- Kumar S, Kumari R, Sharma VK. 2015. Fitness Based Position Update in Spider Monkey Optimization Algorithm, Procedia Computer Science. 62:442--449.Google ScholarCross Ref
- Wolpert DH, Macready WG. No free lunch theorems for optimization. Evol Comput IEEE Trans 1997;1:67--82. Google Scholar
- Saremi S, Mirjalili S, Lewis A Grasshopper optimisation algorithm; theory and application. Advances in Engineering Software. 2017 Mar 1;105:30--47. Google Scholar
- Saxena, A (2019). A comprehensive study of chaos embedded bridging mechanisms and crossover operators for grasshopper optimisation algorithm. Expert Systems with Applications, 132, 166--188.Google ScholarDigital Library
- Saxena, A, Shekhawat, S., & Kumar, R. (2018). Application and development of enhanced chaotic grasshopper optimization algorithms. Modelling and Simulation in Engineering, 2018.Google ScholarDigital Library
- Bairathi, D., & Gopalani, D. (2018, December). An Improved Opposition Based Grasshopper Optimisation Algorithm for Numerical Optimization. In International Conference on Intelligent Systems Design and Applications (pp. 843--851). Springer, Cham.Google Scholar
- Heidari, A A, Faris, H., Aljarah, I., & Mirjalili, S. (2019). An efficient hybrid multilayer perceptron neural network with grasshopper optimization. Soft Computing, 23(17), 7941--7958. Google ScholarDigital Library
- Mafarja, M., Aljarah, L, Faris, H, Hammouri, A I, Ala'M, A Z., & Mirjalili, S. (2019). Binary grasshopper optimisation algorithm approaches for feature selection problems. Expert Systems with Applications, 117, 267--286.Google ScholarCross Ref
- Aljarah, I., Ala'M, A. Z, Faris, H., Hassonah, M. A, Mirjalili, S., & Saadeh, H. (2018). Simultaneous feature selection and support vector machine optimization using the grasshopper optimization algorithm. Cognitive Computation, 10(3), 478--495.Google ScholarCross Ref
- Barik, A K., & Das, D. C. (2018). Expeditious frequency control of solar photovoltaic/biogas/biodiesel generator based isolated renewable microgrid using grasshopper optimisation algorithm.IET Renewable Power Generation, 12(14), 1659--1667.Google Scholar
- Jamil, M., & Mittal, S. (2020). Hourly load shifting approach for demand side management in smart grid using grasshopper optimisation algorithm. IET Generation, Transmission & Distribution, 14(5), 808--815.Google ScholarCross Ref
- Elsayed, A M., Mishref, M. M., & Farrag, S. M. (2019). Optimal allocation and control of fixed and switched capacitor banks on distribution systems using grasshopper optimisation algorithm with power loss sensitivity and rough set theory. IET Generation, Transmissions Distribution, 13(17), 3863--3878.Google ScholarCross Ref
- Hekimoglu, B., & Ekinci, S. (2018, May). Grasshopper optimization algorithm for automatic voltage regulator system. In 2018 5fh International Conference on Electrical and Electronic Engineering (ICEEE) (pp. 152--156). IEEE.Google Scholar
- Luo, J., Chen, H., Xu, Y., Huang, H., & Zhao, X. (2018). An improved grasshopper optimization algorithm with application to financial stress prediction. Applied Mathematical Modelling, 64, 654--668.Google ScholarCross Ref
- Lukasik, S., Kowalski, P. A, Charytanowicz, M., & Kulczycki, P. (2017, September). Data clustering with grasshopper optimization algorithm. In 2017 Federated Conference on Computer Science and Information Systems (FedCSIS) (pp. 71--74). IEEE.Google ScholarCross Ref
- Crawford, B., Soto, R., Peña, A, & Astorga, G. (2018, April). A binary grasshopper optimisation algorithm applied to the set covering problem. In Computer Science On-line Conference (pp. 1--12). Springer, Cham.Google Scholar
- Liang, H., Jia, H., Xing, Z., Ma, J., & Peng, X. (2019). Modified grasshopper algorithm-based multilevel thresholding for color image segmentation. IEEE Access, 7, 11258--11295.Google ScholarCross Ref
- Elmi, Z., & Efe, M. 0. (2018, February). Multi-objective grasshopper optimization algorithm for robot path planning in static environments. In 2018 IEEE International Conference on Industrial Technology (ICIT) (pp. 244--249). IEEE.Google ScholarCross Ref
- Kumar S, Nayyar A, Nguyen NG, Kumari R. (2020) Hyperbolic spider monkey optimization algorithm. Recent Advances in Computer Science and Communications(Formerly: Recent Patents on Computer Science). 13(1):35--42Google Scholar
Index Terms
- Modified grasshopper optimisation algorithm
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