Large-Scale Truss Topology and Sizing Optimization by an Improved Genetic Algorithm with Multipoint Approximation

Abstract

Truss size and topology optimization problems have recently been solved mainly by many different metaheuristic methods, and these methods usually require a large number of structural analyses due to their mechanism of population evolution. A branched multipoint approximation technique has been introduced to decrease the number of structural analyses by establishing approximate functions instead of the structural analyses in Genetic Algorithm (GA) when GA addresses continuous size variables and discrete topology variables. For large-scale trusses with a large number of design variables, an enormous change in topology variables in the GA causes a loss of approximation accuracy and then makes optimization convergence difficult. In this paper, a technique named the label–clip–splice method is proposed to improve the above hybrid method in regard to the above problem. It reduces the current search domain of GA gradually by clipping and splicing the labeled variables from chromosomes and optimizes the mixed-variables model efficiently with an approximation technique for large-scale trusses. Structural analysis of the proposed method is extremely reduced compared with these single metaheuristic methods. Numerical examples are presented to verify the efficacy and advantages of the proposed technique.

Shape Control of a Carbon Fiber-Reinforced Polymer Reflector and Placement Optimization of the Actuators

“…U ′ e is the electric potential applied to a piezoelectric beam element. Using Equations ( 15), ( 19) and (20), the potential energy in a piezoelectric beam element can be further expressed as…”

“…The random search algorithm is another type of optimization method for shape control problems. Dong and Huang [20] optimized a truss topology based on a multipoint approximate function and a Genetic Algorithm (GA). An optimized actuator was positioned for an adaptive truss using a two-level multipoint approximation method [21].…”

Efficient Sizing and Layout Optimization of Truss Benchmark Structures Using ISRES Algorithm

“…From the moment it was introduced to the literature, the genetic algorithm (GA) has been used for all kinds of optimization problems. In this section, a GA focused on truss optimization in recent years is presented [39][40][41][42][43][44][45][46]. Assimi et al [39] used the GA to minimize the cross-section area of truss systems; Liu and Xia [40] proposed a hybrid intelligent genetic algorithm (HIGA) and reported that the new method is a convenient tool for truss optimization.…”

“…Assimi et al [39] used the GA to minimize the cross-section area of truss systems; Liu and Xia [40] proposed a hybrid intelligent genetic algorithm (HIGA) and reported that the new method is a convenient tool for truss optimization. Noii et al [41] and Dong et al [42] proposed a new approach to improve the GA, while Delyová et al [43] used the GA for size and topology optimization, and Assimi et al [46] introduced a new mutant operator for the GA.…”