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Научен доклад ID 2176 : 2021/2
OPTIMIZATION OF SPEED REDUCER, PRESSURE VESSEL AND HELICAL SPRING BY USING GRASSHOPPER ALGORITHM
Branislav Milenković1, Mladen Krstić2 The optimization plays very important roles especially in mechanical engineering, civil engineering, railway engineering, traffic engineering, computer engineering, chemical engineering and electrical engineering. In the past, many researchers have considered the problem of speed reducer, pressure vessel and helical spring optimization by using Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Harmony Search Algorithm (HSA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Water Cycle Algorithm (WCA), Ant Lion Optimizer (ALO), Differential Evolution (DE), Firefly Algorithm (FA), Bat Algorithm (BA), Whale Optimization Algorithm (WOA) and other optimization algorithms. The goal of this paper is to perform speed reducer, pressure vessel and helical spring parameter optimization by using the Grasshopper Optimization Algorithm (further referred to as GOA). The pseudo code for this algorithm was written using the Matlab R2019a software suite. At the end of this paper, results and conclusions are presented.
grasshopper optimization speed reducer pressure vessel helical springgrasshopper optimization speed reducer pressure vessel helical springBranislav Milenković Mladen Krstić BIBLIOGRAPHY [1] Kirkpatrick S., Gelatt C., and Vecchi M., Optimization of simulated annealing, Science, vol 220, 4598, pp. 671-680, 1983. [2] Holland J H, Adaptation in Natural and Artificial System, University of Michigan Press, Ann Arbor, 1975. [3] Zong Woo Geem and Joong Hoon Kim, A new Heuristic Optimization Algorith: Harmony Search, Simulation 76, Vol 2. Pp.60-68, 2001. [4] Kraiss K. Advanced man-machine interaction (Fundamentals and implementation). Springer Berlin Heidelberg New York, ISBN-10 3-540-30618-8. [5] P.Zhang, H.Yao,C.Fang and Y.Lui, “Multi-objective enhanced particle swarm optimization in virtual network embedding”, EURASIP Journal on Wireless Communications and Networking (2016) 2016:167. [6] D.Manickavelu, R.Vaidyanathan, “Particle swarm optimization (PSO)-based node and link lifetime prediction algorithm for route recovery in MANET”, EURASIP Journal on Wireless Communications and Networking2014,2014:107. [7] Ali Asghar Heidari, Seyedali Mirjalili, Hossam Faris, Ibrahim Aljarah, Majdi Mafarja, Huiling Chen: Harris hawks optimization: Algorithm and applications, Future Generation Computer Systems, March 2019. [8] Carlos A. Coello Coello: Constraint-handling inge-netic algorithms through the use of dominance ba-sed tournament selection, Advanced Engineering In-formatics, 16, 193–203, 2002. [9] Sandgren E, Nonlinear integer and discrete progra-mming in mechanical design optimization, Journal of Mechanical Design,112(43):223-9, 1990. [10] Arora J. S. Introduction to optimum design. New York: McGraw-Hill, 1989. [11] Abhishek G Neve, Ganesh M Kakandikar and Omkar Kulkarni, Application of Grasshopper Optimization for Constrained and Unconstrained Test Functions, India, 2017. [12] B.Milenković,M.Krstić,Đ.Jovanović:Primena algoritma sivog vuka za rešavanje inženjerskih optimizacionih problema, Tehnika, 2021.,Vol.76, Br.1. str.50-57, ISSN 0040-2176. [13] Bulatović, R. R., Bošković, G., Savković, M. M., & Gašić, M. M. (2014). Improved Cuckoo Search (ICS) algorthm for constrained optimization problems. /Latin American Journal of Solids and Structures/, /11/(8), 1349-1362. [14] H.Eskandar, A. Sadollah, A. Bahreininejad, M.Hamdi: Water cycle algorithm – A novel metaheuristic optimization method for solving constrained engineering optimization problems, Computers and Structures, 2012. [15] https://www.mathworks.com/matlabcentral/fil... |