Comparative Evaluation of Different Optimization Algorithms for Structural Design Applications

Abstract

SUMMARYNon-linear programming algorithms play an important role in structural design optimization. Fortunately, several algorithms with computer codes are available. At NASA Lewis Research Centre, a project was initiated to assess the performance of eight different optimizers through the development of a computer code CometBoards. This paper summarizes the conclusions of that research. CometBoards was employed to solve sets of small, medium and large structural problems, using the eight different optimizers on a Cray-YMP8E/8128 computer. The reliability and efficiency of the optimizers were determined from the performance of these problems. For small problems, the performance of most of the optimizers could be considered adequate. For large problems, however, three optimizers (two sequential quadratic programming routines, DNCONG of IMSL and SQP of IDESIGN, along with Sequential Unconstrained Minimizations Technique SUMT) outperformed others. At optimum, most optimizers captured an identical number of active displacement and frequency constraints but the number of active stress constraints differed among the optimizers. This discrepancy can be attributed to singularity conditions in the optimization and the alleviation of this discrepancy can improve the efficiency of optimizers.

Reliability based robust design optimization of steel structures

“…Cascade optimization has been proposed as a remedy to this observation [15,17,18]. According to this optimization strategy, a multi-stage procedure is applied, in which various optimizers are implemented successively.…”

“…Cascade optimization has been implemented using different deterministic optimizers [17], as well as using both deterministic and probabilistic optimizers [15,18]. The main advantage in combining different optimizers in a successive manner has to do with maximizing the exploitation of the advantages offered by various optimization algorithms and alleviating the influence of the corresponding disadvantages on the optimum design achieved.…”

A Subsonic Aircraft Design Optimization with Neural Network and Regression Approximators

“…There are four airframe design variables: wing aspect ratio DV 1 , wing area DV 3 , sweep angle DV 4 , and thickness to chord ratio DV 5 . The five engine design parameters are engine thrust DV 2 , the turbine inlet temperature DV 6 , the overall pressure ratio DV 7 , the bypass ratio DV 8 , and the fan pressure ratio DV 9 . Constraints are as follows: the landing velocity g 1 is not to exceed 125 knots.…”

Significance of Strain Formulation in Theory of Solid Mechanics

“…This unexpected observation is again attributed to compatibility condition. 36 Singularity is overwhelming with the closed-form sensitivity, but it is diluted when an approximation is used. In summary, the compatibility condition has considerable influence in the design of indeterminate structures.…”

“…Benefits from the use of the completed strain formulation are being shown in continuum elasticity, [1][2][3][4][5][6] in finite element analysis, [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] and in design optimization. [26][27][28][29][30][31][32][33][34][35][36] The theory of strengths of materials began with the cantilever experiment conducted by Galileo in 1632. 37 Several decades later the stress (σ) This contained two distinct sets of equations: the field equations τ ij, j + b i = 0, and boundary or traction conditions p i = τ ij n j .…”