Metadata about: Recently, Swarm Intelligence Based (SIB) method, a nature-inspired metaheuristic optimization method, has been widely used in many problems that their solutions fall in discrete and continuous domains. SIB 1.0 is efficient to converge to optimal solution but its particle size is fixed and pre-defined, while SIB 2.0 allows particle size changes during the procedure but it takes longer time to converge. This paper introduces a two-step SIB method that combines the advantages of two SIB methods. The first step via SIB 2.0 serves as a preliminary study to determine the optimal particle size and the second step via SIB 1.0 serves as a follow-up study to obtain the optimal solution. This method is applied to the search of optimal minimum energy design and the result outperforms the results from both SIB 1.0 and SIB 2.0.

About: Recently, Swarm Intelligence Based (SIB) method, a nature-inspired metaheuristic optimization method, has been widely used in many problems that their solutions fall in discrete and continuous domains. SIB 1.0 is efficient to converge to optimal solution but its particle size is fixed and pre-defined, while SIB 2.0 allows particle size changes during the procedure but it takes longer time to converge. This paper introduces a two-step SIB method that combines the advantages of two SIB methods. The first step via SIB 2.0 serves as a preliminary study to determine the optimal particle size and the second step via SIB 1.0 serves as a follow-up study to obtain the optimal solution. This method is applied to the search of optimal minimum energy design and the result outperforms the results from both SIB 1.0 and SIB 2.0.

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