A hybrid ant strategy and genetic algorithm to tune the population size for efficient structural optimization

Abstract

Purpose

Although genetic algorithm (GA) has already been extended to various types of engineering problems, tuning its parameters is still an interesting field of interest. Some recent works have addressed attempts requiring several GA runs, while more interesting approaches aim to obtain proper estimate of a tuned parameter during any run of genetic search. This paper seeks to address this issue.

Design/methodology/approach

In this paper, a competitive frequency‐based methodology is proposed to explore the least proper population size as a major affecting control parameter of GAs. In the tuning stage, the indirect shared memory in ant strategies is borrowed in a discrete manner to generate a dynamic colony of the most successive recent solutions to be added into each new population. An adaptive variable band mutation based on direct index coding for structural problems is also employed to increase the convergence rate as well as to prevent premature convergence especially after determining a proper population size. As an important field of engineering problems, the method is then applied to a number of structural size and layout optimization examples in order to illustrate and validate its capability in capturing the problem optimum with reduced computational effort.

Findings

It was shown that improper fixed size population can lead to premature convergence. Applying the proposed method could result in a more efficient convergence to the global optimum compared with the fixed size population methods.

Originality/value

A novel combination of genetic and ant colony approaches is proposed to provide a dynamic short‐term memory of the sampled representatives which can enrich the current population, avoiding unnecessary increase in its size and the corresponding computational effort in the genetic search. In addition, a dynamic band mutation is introduced and matched with such a search, to make it more efficient for structural purposes.

Citation

Kaveh, A. and Shahrouzi, M. (2007), "A hybrid ant strategy and genetic algorithm to tune the population size for efficient structural optimization", Engineering Computations, Vol. 24 No. 3, pp. 237-254. https://doi.org/10.1108/02644400710734990