Computer Oriented Algorithms for Solving Structural Optimization Problems with Discrete Programming Techniques

1981 ◽  
pp. 796-815
Author(s):  
M. Rifat Saglam
Keyword(s):  
Structural Optimization ◽  
Optimization Problems ◽  
Programming Techniques ◽  
Discrete Programming

Large Scale Structural Optimization With Linear Goal Programming

1990 ◽  
Author(s):  
Mohamed E. M. El-Sayed ◽  
T. S. Jang
Keyword(s):  
Structural Optimization ◽  
Goal Programming ◽  
Large Scale ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Minimum Weight ◽  
Design Tool ◽  
Linear Goal Programming ◽  
Programming Techniques ◽  
Structural Optimization Problem

Abstract This paper presents a method for solving large scale structural optimization problems using linear goal programming techniques. The method can be used as a multicriteria optimization tool since goal programming removes the difficulty of having to define an objective function and constraints. It also has the capacity of handling rank ordered design objectives or goals. The method uses finite element analysis, linear goal programming techniques and successive linearization to obtain the solution for the nonlinear goal optimization problems. The general formulation of the structural optimization problem into a nonlinear goal programming form is presented. The successive linearization method for the nonlinear goal optimization problem is discussed. To demonstrate the validity of the method, as a design tool, the solution of the minimum weight structural optimization problem with stress constraints for 10, 25 and 200 truss problems are included.

Structural Optimization With Nonlinear Goal Programming Using Powell’s Method

1991 ◽  
Author(s):  
Mohamed E. M. El-Sayed ◽  
T. S. Jang
Keyword(s):  
Structural Optimization ◽  
Goal Programming ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Numerical Test ◽  
Design Tool ◽  
Test Cases ◽  
Programming Techniques ◽  
Ordered Design

Abstract This paper presents a method for solving structural optimization problems using nonlinear goal programming techniques. The developed method removes the difficulty of having to define an objective function and constraints. It also has the capacity of handling rank ordered design objectives or goals. The formulation of the structural optimization problem into a goal programming form is discussed. The resulting optimization problem is solved using Powell’s conjugate direction search algorithm. To demonstrate the effectiveness of the method, as a design tool, the solutions of some numerical test cases are included.

scholarly journals Performance of the Modified Dolphin Monitoring Operator for Weight Optimization of Skeletal Structures

2018 ◽  
Author(s):  
Ali Kaveh ◽  
S.R. Hoseini Vaez ◽  
Pedram Hosseini
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Structural Optimization ◽  
Differential Evolution ◽  
Optimization Problems ◽  
Harmony Search ◽  
Average Weight ◽  
Weight Optimization ◽  
Swarm Optimization ◽  
Population Dispersion

In this study, the Modified Dolphin Monitoring (MDM) operator is used to enhance the performance of some metaheuristic algorithms. The MDM is a recently presented operator that controls the population dispersion in each iteration. Algorithms are selected from some well-established algorithms. Here, this operator is applied on Differential Evolution (DE), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Vibrating Particles System (VPS), Enhanced Vibrating Particles System (EVPS), Colliding Bodied Optimization (CBO) and Harmony Search (HS) and the performance of these algorithms are evaluated with and without this operator on three well-known structural optimization problems. The results show the performance of this operator on these algorithms for the best, the worst, average and average weight of the first quarter of answers.

scholarly journals Improved Wolf Pack Algorithm for Optimum Design of Truss Structures

2020 ◽  
Vol 6 (8) ◽  
pp. 1411-1427 ◽  
Author(s):  
Yan-Cang Li ◽  
Pei-Dong Xu
Keyword(s):  
Structural Optimization ◽  
Search Strategy ◽  
Optimization Problems ◽  
Truss Structures ◽  
Local Optimum ◽  
Effective Solution ◽  
Step Size ◽  
Simulation Experiments ◽  
Adaptive Step Size ◽  
Adaptive Step

In order to find a more effective method in structural optimization, an improved wolf pack optimization algorithm was proposed. In the traditional wolf pack algorithm, the problem of falling into local optimum and low precision often occurs. Therefore, the adaptive step size search and Levy's flight strategy theory were employed to overcome the premature flaw of the basic wolf pack algorithm. Firstly, the reasonable change of the adaptive step size improved the fineness of the search and effectively accelerated the convergence speed. Secondly, the search strategy of Levy's flight was adopted to expand the search scope and improved the global search ability of the algorithm. At last, to verify the performance of improved wolf pack algorithm, it was tested through simulation experiments and actual cases, and compared with other algorithms. Experiments show that the improved wolf pack algorithm has better global optimization ability. This study provides a more effective solution to structural optimization problems.

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