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

Abstract

PurposeAlthough 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/approachIn 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.FindingsIt 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/valueA 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.

<b>Development of an adaptive genetic algorithm for simulation optimization

“…Moreover, as aforementioned, the larger the population size, the better the response quality will be. Due to this fact and to results provided by Kaveh and Shahrouzi (2007), Ma and Zhang (2008) and Pinho et al (2012), the researchers opted to increase the initial population calculated by Reeves and Rowe (2002) by 50% in each new algorithm generation.…”

Dynamic selective pressure using hybrid evolutionary and ant system strategies for structural optimization

“…The width of such a portion is dominated by the number of sampling ants, N , while its depth is affected by the pheromone deposit packet, . In the sampling process offered by the authors [33,34], at every generation of the evolutionary search, N ants sample N individuals of the current population that are fitter than the others. These sampled solution states are then copied to the colony of more frequent successive individuals.…”

“…As new islands are added to and old ones gradually are omitted from the current population as the search window, fitter representatives of them will compete with the current elite ones alternatively. Such a process is called alternate appearance of competitive individuals [10,33]. Despite GAs, ant strategies use an indirect type of information share between individual solution trials.…”

An Ants-Search Based Method for Optimum Synthesis of Compliant Mechanisms

“…Lately, a Modified Ant Search [18] algorithm was proposed to improve the performance of the AS algorithm by using an Elitist ants approach with dynamic enter/exit strategies between the exploration and exploitation phases. Kaveh and Shahrouzi [19,20] used a hybrid genetic algorithm (GA) and Ant Colony Optimization (ACO) approach where the latter was used to fine tune the parameters of the GA and applied the approach to structural optimization problems. Instead of fixing the population size for the GA, the proposed algorithm starts with a relatively small population.…”