An enhanced genetic algorithm for structural topology optimization

Abstract

SUMMARYGenetic algorithms (GAs) have become a popular optimization tool for many areas of research and topology optimization an effective design tool for obtaining efficient and lighter structures. In this paper, a versatile, robust and enhanced GA is proposed for structural topology optimization by using problemspecific knowledge. The original discrete black-and-white (0 -1) problem is directly solved by using a bit-array representation method. To address the related pronounced connectivity issue effectively, the four-neighbourhood connectivity is used to suppress the occurrence of checkerboard patterns. A simpler version of the perimeter control approach is developed to obtain a well-posed problem and the total number of hinges of each individual is explicitly penalized to achieve a hinge-free design. To handle the problem of representation degeneracy effectively, a recessive gene technique is applied to viable topologies while unusable topologies are penalized in a hierarchical manner. An efficient FEM-based function evaluation method is developed to reduce the computational cost. A dynamic penalty method is presented for the GA to convert the constrained optimization problem into an unconstrained problem without the possible degeneracy. With all these enhancements and appropriate choice of the GA operators, the present GA can achieve significant improvements in evolving into near-optimum solutions and viable topologies with checkerboard free, mesh independent and hinge-free characteristics. Numerical results show that the present GA can be more efficient and robust than the conventional GAs in solving the structural topology optimization problems of minimum compliance design, minimum weight design and optimal compliant mechanisms design. It is suggested that the present enhanced GA using problem-specific knowledge can be a powerful global search tool for structural topology optimization.

Topology Optimization of Continuum Structures Using Element Exchange Method

“…It is notable that the number of function calls (FEA plus any other analysis used in that particular method) is in the order of the number of iterations multiplied by the number of populations in both GA 38 and PSO 25 as will be shown next. …”

“…As a result of mesh refinement, the optimal topology changes with minimal change in the final strain energy. 38 based on the enhanced GA approach are also shown in Table 3.5 for comparison. Although the final geometry and strain energy values are nearly the same, the EEM solution converges much faster.…”

An Intelligent Optimal Genetic Model to Investigate the User Usage Behaviour on World Wide Web

“…In the subsequent year 2006, S. Y. Wang, K. Tai, and M. Y. Wang [29] presented a versatile, robust and enhanced genetic algorithm for structural topology optimization using problem specific knowledge. In their implementation process specifically pronounced the importance of choosing appropriate representation techniques, genetic operators and evaluation methods.…”

Optimal Shape Design of Magnetic Actuators for Magnetic and Dynamic Characteristic Improvement

“…The blurring technique, which is giving the density weight to the design domain elements and large clusters are determined according to the volume fraction, is adopted to avoid randomly distributed small spots of solid [5]. Seed element concept which defines infeasible design domain is also applied to secure magnetic flux path along the structure [3,5].…”

“…However, in case of multiphysics problems in which more than two physical governing equations must be taken into account, it is hard to adopt the gradient based approach because it is difficult to apply several sensitivity values obtained from different objectives into simultaneous design variable update. The genetic algorithm (GA) based topology optimization [3] can be an alternative choice; however, it is suffered from expensive computation cost through the random biological process such as selection, crossover and mutation in spite of recent great enhancement of computation power.…”