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PurposeLayout optimization of structures aims to find the optimal topology and member sizes in an integrated manner. For this purpose, the most successful attempts have addressed the outstanding features of the genetic algorithms.Design/methodology/approachThis paper utilizes a direct index coding (DIC) in a way that the optimization algorithm can simultaneously integrate topology and size in a minimal length chromosome in order to seek the true optimum in an efficient and reasonable manner. Proper genetic operators are adopted for this special kind of encoding together with some modifications in the topological mutation aiming to improve the convergence of the algorithm.FindingsThe present DIC, has the following features: enforcing one‐to‐one correspondence between discrete genotype space and the problems' phenotype space; avoiding any out‐of‐bound parameter addressing and limiting the GA search only to necessary genotypes; reduction in the size of genotype search space to increase the algorithm convergence and the possibility of leading to the global optimum; dealing with direct genetic operators so that the GA parameters can be purely controlled to tune the desired balance between convergence and escaping from local optima.Originality/valueEmploying direct index chromosome makes it possible to eliminate the additional topological bits in treated examples.

PurposeGenetic Algorithm, as a generalized constructive search method, has already been applied to various fields of optimization problems using different encoding schemes. In conventional GAs, the optimum solution is usually announced as the fittest feasible individual achieved in a limited number of generations. In this paper, such a pseudo‐optimum is extended to a neighborhood structure, known as optimal design family.Design/methodology/approachIn this paper, the constructive feature of genetic search is combined with trail update strategy of ant colony approach in a discrete manner, in order to sample more competitive individuals from various subspaces of the search space as a dynamic‐memory of updating design family.FindingsThe proposed method is applied to structural layout and size optimization utilizing an efficient integer index encoding and its appropriate genetic operators. Different applications of the proposed method are illustrated using three truss and frame examples. In the first example, topological classes are identified during layout optimization. In the second example, an objective function containing the stress response, displacement response, and the weight of the structure is considered to solve the optimal design of non‐braced frames. This approach allows the selection of less sensitive designs among the family of solutions. The third example is selected for eigenvalue maximization with minimal number of bracings and structural weight for braced frames.Originality/valueIn this paper, a pseudo‐optimum is extended to a neighborhood structure, known as optimal design family.

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.

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SUMMARYGenetic algorithms have already been applied to various fields of engineering problems as a general optimization tool in charge of expensive sampling of the coded design space. In order to reduce such a computational cost in practice, application of evolutionary strategies is growing rapidly in the adaptive use of problem-specific information. This paper proposes a hybrid strategy to utilize a cooperative dynamic memory of more competitive solutions combining indirect information share in ant systems with direct constructive genetic search. Some proper coding techniques are employed to enable testing the method with various sets of control parameters. As a challenging field of interest, its application to structural layout optimization is considered while an example of a traveling salesman problem is also treated as a combinatorial benchmark.