SERIAL INTEGRATION OF PARTICLE SWARM AND ANT COLONY ALGORITHMS FOR STRUCTURAL OPTIMIZATION

The main objective of this study is to hybridize particle swarm optimization (PSO) and ant colony optimization (ACO) algorithms to propose an efficient algorithm for optimal designing of truss structures. Two types of serial integration of the algorithms are studied. In the first one, PSO is employed to explore the design space, while ACO is utilized to achieve a local search about the best solution found by PSO. This is denoted as serial particle swarm ant colony algorithm (SPSACA). In the second one, ACO works as the global optimizer while PSO acts as the local one. This is called as serial ant colony particle swarm algorithm (SACPSA). A number of structural optimization benchmark problems are solved by the proposed algorithms. Numerical results indicate that the SPSACA possesses better computational performance compared with the SACPSA and other existing algorithms.

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