Knowledge‐based algorithms in fixed‐grid GA shape optimization

Abstract

SUMMARYShape optimization through a genetic algorithm (GA) using discrete boundary steps and the ÿxed-grid (FG) ÿnite-element analysis (FEA) concept was recently introduced by the authors. In this paper, algorithms based on knowledge speciÿc to the FG method with the GA-based shape optimization (FGGA) method are introduced that greatly increase its computational e ciency. These knowledge-based algorithms exploit the information inherent in the system at any given instance in the evolution such as string structure and ÿtness gradient to self-adapt the string length, population size and step magnitude. Other non-adaptive algorithms such as string grouping and deterministic local searches are also introduced to reduce the number of FEA calls. These algorithms were applied to two examples and their e ects quantiÿed. The examples show that these algorithms are highly e ective in reducing the number of FEA calls required hence signiÿcantly improving the computational e ciency of the FGGA shape optimization method.

An enhanced genetic algorithm for structural topology optimization

“…It has been reported by many researchers [6,11,25,40] that incorporating problem-specific knowledge leads to a more advantageous GA. In the present GA for structural topology optimization, the robustness and efficiency of the GA are enhanced by using an image-processing-based connectivity analysis, the well-posedness of the problem, a hierarchical constraint violation penalty method, and an efficient strategy in the FEA, as well as the appropriately chosen GA operators.…”

“…While a GA may never produce the absolutely best solution (global optimum), it is mathematically likely to get very close to it by using a fraction of the computational requirements of an exhaustive deterministic search. The advantages of GAs would include not only the global nature of the search process, but also the indifference to system specific information [40], especially the derivative information, the versatility of application [31], the ease with which heuristics can be incorporated in optimization [41], the capability of learning and adapting to changes over time, the implicitly parallel directed random exploration of the search space [42], and the ability to accommodate discrete variables in the search process [24]. However, GAs are usually computationally expensive.…”

Structural Optimization Using Simulated Annealing

“…Many researchers applied genetic algorithms to shape optimization problems (Sandgren & Jensen, 1992;Kita & Tanie, 1997;Annicchiarico & Cerrolaza, 2001;Woon et al, 2003;Garcia & Gonzales, 2004;Zhang et al, 2005). These algorithms are based on the concepts of genetics and Darwinian survival of the fittest.…”