Keywords: hybrid metaheuristic method, sizing, shaping, topology optimization.

In this paper, a hybrid metaheuristic method is presented for sizing, geometry and topology optimization of trusses with continuous design variables. The method combines an ant colony optimization, a genetic algorithm, and a local search strategy. In the algorithm presented, ant colony optimization and genetic algorithm search alternately and cooperatively in the solution space. The local search algorithm, which is based on the local linearization of the constraint set, is applied to yield a better feasible or less infeasible solution when an ant colony optimization or a genetic algorithm obtains a solution. The hybrid algorithm is based on only three operators: random selection (ant colony optimization + genetic algorithm), random perturbation (genetic algorithm), and random combination (genetic algorithm). In the algorithm the traditional mutation operator is replaced by the local search procedure. According to the progress of the searching process, the "freedom of diversification" decreases but the "freedom of intensification" is increasing step by step. In this paper, we applied the proposed hybrid metaheuristic method for sizing, geometry and topology optimization of trusses where both the positions of the joints and the cross-sectional areas are considered. Because the cross-sectional areas are also permitted to take zero values, the problem of topology optimization is inherently solved. Numerical results demonstrate that the proposed hybrid metaheuristic method can be more efficient and robust than the conventional population based heuristic methods in solving combined truss optimization problems.

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