Metaheuristic algorithms nowadays represent the standard approach to engineering optimization. A very challenging field is large scale structural optimization entailing hundreds of design variables and thousands of nonlinear constraints on element stresses and nodal displacements. However, a very few studies documented the use of metaheuristic algorithms in large scale structural optimization. In order to fill this gap, an enhanced hybrid harmony search (HS) algorithm for weight minimization of large scale truss structures is presented in this PhD dissertation. The new algorithm, Large Scale Structural Optimization - Hybrid Harmony Search JAYA (LSSO-HHSJA), developed here combines a well established method like HS with a very recent method like JAYA, which has however the simplest and inherently most powerful search engine amongst metaheuristic optimizers. All stages of LSSO-HHSJA are aimed at reducing the number of structural analyses required in large scale structural optimization. The basic idea is to move along descent directions to generate new trial designs: directly through the use of gradient information in the HS phase, indirectly by correcting trial designs with JA-based operators that push search towards the best design currently stored in the population or the best design included in some local neighborhood of the currently analyzed trial design. The proposed algorithm is tested in three large scale weight minimization problems of truss structures. Optimization results obtained for the three benchmark examples with up to 280 sizing variables and 37374 nonlinear constraints prove the efficiency of the proposed LSSO-HHSJA algorithm, which is very competive with other HS and JAYA variants as well as with commercial gradient-based optimizers. The possibility of using the same hybridization strategy for another metaheuristic algorithm such as Big Bang-Big Crunch is also investigated by solving two highly nonlinear design problems including up to 84 variables, (i) shape optimization of a concrete dam and (ii) discrete layout optimization of a planar steel frame, to prove the feasibility of the proposed approach.

Design optimization of large-scale structures with advanced meta-heuristic methods

Minooei Mazloom, Seyed Mohammad
2022

Abstract

Metaheuristic algorithms nowadays represent the standard approach to engineering optimization. A very challenging field is large scale structural optimization entailing hundreds of design variables and thousands of nonlinear constraints on element stresses and nodal displacements. However, a very few studies documented the use of metaheuristic algorithms in large scale structural optimization. In order to fill this gap, an enhanced hybrid harmony search (HS) algorithm for weight minimization of large scale truss structures is presented in this PhD dissertation. The new algorithm, Large Scale Structural Optimization - Hybrid Harmony Search JAYA (LSSO-HHSJA), developed here combines a well established method like HS with a very recent method like JAYA, which has however the simplest and inherently most powerful search engine amongst metaheuristic optimizers. All stages of LSSO-HHSJA are aimed at reducing the number of structural analyses required in large scale structural optimization. The basic idea is to move along descent directions to generate new trial designs: directly through the use of gradient information in the HS phase, indirectly by correcting trial designs with JA-based operators that push search towards the best design currently stored in the population or the best design included in some local neighborhood of the currently analyzed trial design. The proposed algorithm is tested in three large scale weight minimization problems of truss structures. Optimization results obtained for the three benchmark examples with up to 280 sizing variables and 37374 nonlinear constraints prove the efficiency of the proposed LSSO-HHSJA algorithm, which is very competive with other HS and JAYA variants as well as with commercial gradient-based optimizers. The possibility of using the same hybridization strategy for another metaheuristic algorithm such as Big Bang-Big Crunch is also investigated by solving two highly nonlinear design problems including up to 84 variables, (i) shape optimization of a concrete dam and (ii) discrete layout optimization of a planar steel frame, to prove the feasibility of the proposed approach.
Large scale structural optimization; Metaheuristic algorithms; Harmony search; Big Bang-Big Crunch; JAYA; Truss structures; Sizing and shape optimization
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/239680
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