Stress-based Crossover Operator for Structural Topology Optimization ∗

Abstract

Inthispaper, we propose astress-basedcrossover (SX)operatorto solvethe checkerboardlike material distributation and disconnected topology that is common for simple geneticalgorithm(SGA)tostructuraltopologyoptimizationproblems(STOPs). Apenalty function is defined to evaluate the fitness of each individual. A number of constrained problems are adopted to experiment the effectiveness of SX for STOPs. Comparison of 2-point crossover (2X) with SX indicates that SX can markedly suppress the checkerboard-like material distributionphenomena. Comparison of evolutionarystructural optimization (ESO) and SX demonstrates the global search ability and flexibility of SX. Experiments of a Michell-type problem verifies the effectiveness of SX for STOPs. For a multi-loaded problem, SX searches out alternate solutions on the same parameters that shows the global search ability of GA.