Search Mechanisms for Discrete Structural Optimization Problems

2009 ◽  
Author(s):  
S. Shanmuganathan ◽  
S. Manoharan
Keyword(s):  
Structural Optimization ◽  
Optimization Problems

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.

A Sequential Approximation Method for Structural Optimization Using Logarithmic Barriers

1993 ◽  
Author(s):  
Ashok V. Kumar ◽  
David C. Gossard
Keyword(s):  
Structural Optimization ◽  
Objective Function ◽  
Line Search ◽  
Optimization Problems ◽  
Approximation Technique ◽  
Barrier Method ◽  
Non Linear Programming ◽  
Linearly Constrained ◽  
Sequential Approximation ◽  
And Function

Abstract A sequential approximation technique for non-linear programming is presented here that is particularly suited for problems in engineering design and structural optimization, where the number of variables are very large and function and sensitivity evaluations are computationally expensive. A sequence of sub-problems are iteratively generated using a linear approximation for the objective function and setting move limits on the variables using a barrier method. These sub-problems are strictly convex. Computation per iteration is significantly reduced by not solving the sub-problems exactly. Instead at each iteration, a few Newton-steps are taken for the sub-problem. A criteria for moving the move limit, is described that reduces or eliminates stepsize reduction during line search. The method was found to perform well for unconstrained and linearly constrained optimization problems. It requires very few function evaluations, does not require the hessian of the objective function and evaluates its gradient only once per iteration.

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