Genetic Algorithms as an Approach to Configuration and Topology Design

Abstract

The genetic algorithm, a search and optimization technique based on the theory of natural selection, is applied to problems of structural topology design. An overview of the genetic algorithm will first describe the genetics-based representations and operators used in a typical genetic algorithm search. Then, a review of previous research in structural optimization is provided. A discretized design representation, and methods for mapping genetic algorithm “chromosomes” into this representation, is then detailed. Several examples of genetic algorithm-based structural topology optimization are provided: we address the optimization of cantilevered plate topologies, and we investigate methods for optimizing finely-discretized design domains. The genetic algorithm’s ability to find families of highly-fit designs is also examined. Finally, a description of potential future work in genetic algorithm-based structural topology optimization is offered.

An adjacency representation for structural topology optimization using genetic algorithm

“…Hence, the original '0-1' optimization problem was attacked directly by using a bit-array representation and a genetic algorithm. The work of Sandgren and his co-workers, using bit-array representation, has been extended by Jakiela and his coworkers [3][4][5], by Schoenauer and his co-workers [6,7,9], by Fanjoy and Crossley [8,10], and, more recently, by Wang and Tai [11]. Although all these extensions can well prevent checkerboard patterns by exploiting a connectiva Corresponding author: matthieu.domaszewski@utbm.fr ity restriction, the other numerical instabilities in structural topology optimization such as mesh dependency and one-node connections still exist.…”

Mesh dependency of stress-based crossover for structural topology optimization

“…However, smoothing is based on image processing, which ignores the underlying problem [9]. Moreover, experiments indicated that only higher-order finite element methods with simple GA operators can eliminate the checkerboard-like material distribution in the solution [13]. Furthermore, it is obvious that using higher-order finite element methods will substantially increase computation cost.…”

A domain-specific crossover and a helper objective for generating minimum weight compliant mechanisms

“…In the early studies, a genetic algorithm (GA) was used for structural topology optimization using single point crossover and bitwise mutation operators [4]. Extending the idea of using GA, the structural optimization problems with different kinds of objective functions and constraints were solved.…”