Integrated Genetic Algorithm for Optimization of Space Structures

TLDR

Gradient‑based optimization often converges to local optima, making global solutions difficult, while genetic algorithms, inspired by natural evolution, are effective for global search. The study aims to optimize space structures by integrating a genetic algorithm with a penalty‑function method. The integrated algorithm employs Darwinian selection, random crossover, and multiple crossover types to generate superior offspring, implemented in C and applied to three space truss structures. The method achieved optimal solutions for all three truss structures within a limited number of iterations.

Abstract

Gradient‐based mathematical‐optimization algorithms usually seek a solution in the neighborhood of the starting point. If more than one local optimum exists, the solution will depend on the choice of the starting point, and the global optimum cannot be found. This paper presents the optimization of space structures by integrating a genetic algorithm with the penalty‐function method. Genetic algorithms are inspired by the basic mechanism of natural evolution, and are efficient for global‐searches. The technique employs the Darwinian survival‐of‐the‐fittest theory to yield the best or better characters among the old population, and performs a random information exchange to create superior offspring. Different types of crossover operations are used in this paper, and their relative merit is investigated. The integrated genetic algorithm has been implemented in C language and is applied to optimization of three space truss structures. In each case, an optimum solution was obtained after a limited number of iterations.