Abstract:

To overcome the defections of easy getting trapped in local optimum and premature convergence, the particle swarm optimization (PSO) algorithm is studied from two aspects, namely force rules and population topology. A hybrid force PSO (HFPSO) algorithm is proposed, the search process of the algorithm is divided into earlier period and later period two stages, attractive and repulsive force rule and double attractive force rule are constructed respectively, which can maintain good population diversity in earlier stage and improve the search accuracy in later stage. The unidirectional dynamic topology HFPSO algorithm is proposed, the biological behavior selection mechanism that biological individuals are willing to interact with better ones is integrated into HFPSO algorithm, a fitness-driven edge-changing unidirectional dynamic topology (FEUDT) is put forward, the FEUDT and HFPSO algorithm are combined by simultaneously evolving of both structure and algorithm, which can further improve the searching capability of the algorithm. Benchmark functions are used to compare the performance of the proposed algorithms with standard PSO and later-stage repulsion-enhanced hybrid attraction and repulsion PSO (LRPSO) algorithms, the results show that the proposed algorithms present better search capability of optimal solution and faster convergence speed. The proposed algorithms are applied in the reliability optimization of bridge network system and the automobile product subsystem in which the suppliers are involved, the effectiveness of the proposed algorithms to solve complex engineering optimization problems are further verified.

Key words: hybrid force, reliability optimization, unidirectional dynamic topology, particle swarm optimization algorithm