scholarly journals APPLICATION OF HYBRID RANDOM SEARCH METHOD TO OPTIMISATION OF ENGINEERING SYSTEMS’ PARAMETERS

2018 ◽  
Vol 21 (3) ◽  
pp. 139-149
Author(s):  
A. V. Panteleev ◽  
D. A. Rodionova

This paper presents a modification of the Luus-Jaakola global optimization method, which belongs to the class of metaheuristic algorithms. A hybrid method is suggested, using a combination of random search methods: Luus-Jaakola method, adaptive random search method and best trial method. The obtained method is applied to the optimization of parameters of different engineering systems. This class of problems appears during the design of aerospace and aeronautical structures; its goal is the cost or weight minimization of the construction. These problems belong to the class of constrained global optimization problems, where the level surface of the objective function has uneven relief and there is a large number of variables. This means that the classical optimization methods prove to be inefficient and these problems should be solved using metaheuristic optimization methods, which provide sufficient accuracy at reasonable operating time. In this paper, the constrained global optimization problem is solved using the penalty method. Thus, the problem of exterior penalty function optimization is considered, where the penalty coefficients are chosen in such a way as to avoid the violation of the constraints. Two applied problems are considered in the paper: the determination of the high-pressure vessel parameters and the anti rattle spring parameters determination. Using the suggested algorithm, a software complex was developed, which allows us to solve engineering optimization problems. The results obtained using the suggested methods were compared with the results obtained using the non-modified Luus-Jaakola method in order to demonstrate the efficiency of the suggested hybrid random search method.

2004 ◽  
Vol 8 (4) ◽  
pp. 351-358 ◽  
Author(s):  
Kotaro HIRASAWA ◽  
Hiroyuki MIYAZAKI ◽  
Jinglu HU ◽  
Kenichi GOTO

1996 ◽  
Vol 32 (8) ◽  
pp. 1277-1286 ◽  
Author(s):  
Masaru KOGA ◽  
Kotaro HIRASAWA ◽  
Masanao OBAYASHI ◽  
Jun-ichi MURATA

2012 ◽  
Vol 2012 ◽  
pp. 1-36 ◽  
Author(s):  
Jui-Yu Wu

This work presents a hybrid real-coded genetic algorithm with a particle swarm optimization (RGA-PSO) algorithm and a hybrid artificial immune algorithm with a PSO (AIA-PSO) algorithm for solving 13 constrained global optimization (CGO) problems, including six nonlinear programming and seven generalized polynomial programming optimization problems. External RGA and AIA approaches are used to optimize the constriction coefficient, cognitive parameter, social parameter, penalty parameter, and mutation probability of an internal PSO algorithm. CGO problems are then solved using the internal PSO algorithm. The performances of the proposed RGA-PSO and AIA-PSO algorithms are evaluated using 13 CGO problems. Moreover, numerical results obtained using the proposed RGA-PSO and AIA-PSO algorithms are compared with those obtained using published individual GA and AIA approaches. Experimental results indicate that the proposed RGA-PSO and AIA-PSO algorithms converge to a global optimum solution to a CGO problem. Furthermore, the optimum parameter settings of the internal PSO algorithm can be obtained using the external RGA and AIA approaches. Also, the proposed RGA-PSO and AIA-PSO algorithms outperform some published individual GA and AIA approaches. Therefore, the proposed RGA-PSO and AIA-PSO algorithms are highly promising stochastic global optimization methods for solving CGO problems.


2020 ◽  
Vol 25 ◽  
pp. 159-170
Author(s):  
Necati Ozbey ◽  
Celaleddin Yeroglu ◽  
Baris Baykant Alagoz ◽  
Norbert Herencsar ◽  
Aslihan Kartci ◽  
...  

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