Optimal dome design considering member-related design constraints

This study proposes to optimize the design of geometrically nonlinear dome structures. A new Multi-objective Optimization Algorithm named Pareto Archived Genetic Algorithm (PAGA), which has an ability of integrating the nonlinear structural analysis with the provisions of American Petroleum Institute specification is employed to optimize the design of ellipse and sphere-shaped dome configurations. Thus, it is possible to investigate how the qualities of optimal designations vary considering the shape, size, and topology-related design variables. Furthermore, the computing efficiency of PAGA is evaluated considering six multi-objective optimization algorithms and eight quality measuring indicators. It is shown that PAGA has a capability of both exploring an increased number of pareto solutions and predicting a pareto front with a higher convergence degree. Moreover, the inclusion of shape-related design variables leads to a decrease in both the weights of dome structures and their load-carrying capacities. However, the designer easily determines the most requested optimal design through the archiving feature of PAGA. Thus, it is also demonstrated that the proposed optimal design procedure increases the correctness degree in the evaluation of optimal dome designs through the tradeoff analysis. Consequently, PAGA is recommended as an optimization tool for the design optimization of geometrically nonlinear dome structures.

[1]  Xin-She Yang,et al.  Engineering Optimization: An Introduction with Metaheuristic Applications , 2010 .

[2]  Siamak Talatahari,et al.  Optimal design of Schwedler and ribbed domes via hybrid Big Bang–Big Crunch algorithm , 2010 .

[3]  Tugrul Talaslioglu,et al.  A new genetic algorithm methodology for design optimization of truss structures: bipopulation-based genetic algorithm with enhanced interval search , 2009 .

[4]  Singiresu S. Rao Game theory approach for multiobjective structural optimization , 1987 .

[5]  A. Kaveh,et al.  Topology and geometry optimization of single-layer domes utilizing CBO and ECBO , 2016 .

[6]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[7]  Marco Laumanns,et al.  SPEA2: Improving the Strength Pareto Evolutionary Algorithm For Multiobjective Optimization , 2002 .

[8]  Stéphane Bordas,et al.  Probabilistic multiconstraints optimization of cooling channels in ceramic matrix composites , 2015 .

[9]  Timon Rabczuk,et al.  A multi-material level set-based topology optimization of flexoelectric composites , 2018, 1901.10752.

[10]  Carlos A. Coello Coello,et al.  Multi-objective Evolutionary Algorithms in Real-World Applications: Some Recent Results and Current Challenges , 2015 .

[11]  R. S. Laundy,et al.  Multiple Criteria Optimisation: Theory, Computation and Application , 1989 .

[12]  Nicola Beume,et al.  SMS-EMOA: Multiobjective selection based on dominated hypervolume , 2007, Eur. J. Oper. Res..

[13]  Carlos A. Coello Coello,et al.  A Short Tutorial on Evolutionary Multiobjective Optimization , 2001, EMO.

[14]  Ali Kaveh,et al.  Topology and geometry optimization of different types of domes using ECBO , 2016 .

[15]  Martin J. Oates,et al.  PESA-II: region-based selection in evolutionary multiobjective optimization , 2001 .

[16]  Tugrul Talaslioglu,et al.  Global stability-based design optimization of truss structures using multiple objectives , 2013 .

[17]  Mehmet Polat Saka,et al.  Optimum Geometry Design of Geodesic Domes Using Harmony Search Algorithm , 2007 .

[18]  Antonio J. Nebro,et al.  Structural design using multi-objective metaheuristics. Comparative study and application to a real-world problem , 2016 .

[19]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[20]  C. A. Coello Coello,et al.  Multiobjective structural optimization using a microgenetic algorithm , 2005 .

[21]  M. P. Saka,et al.  Optimum topology design of various geometrically nonlinear latticed domes using improved harmony search method , 2012 .

[22]  Talaslioglu Tugrul,et al.  Multiobjective size and topolgy optimization of dome structures , 2012 .

[23]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[24]  Timon Rabczuk,et al.  A level-set based IGA formulation for topology optimization of flexoelectric materials , 2017 .

[25]  El-Ghazali Talbi,et al.  Metaheuristics - From Design to Implementation , 2009 .

[26]  Mehmet Polat Saka,et al.  Optimum design of nonlinear elastic framed domes , 1998 .

[27]  Siamak Talatahari,et al.  Geometry and topology optimization of geodesic domes using charged system search , 2011 .

[28]  Kalyanmoy Deb,et al.  Towards a Quick Computation of Well-Spread Pareto-Optimal Solutions , 2003, EMO.

[29]  Antonio J. Nebro,et al.  A survey of multi-objective metaheuristics applied to structural optimization , 2014 .

[30]  Joseph Morlier,et al.  Development of a dynamic virtual reality model of the inner ear sensory system as a learning and demonstrating tool , 2009 .

[31]  Ali Kaveh,et al.  Optimal analysis and design of large-scale domes with frequency constraints , 2016 .