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Abstract

In solving engineering optimization problems; very special cases of the problems can be solved in polynomial time while, most of them are considered as hard combinatorial optimization problems "NP-hard", and most of the solution algorithms for these problems are based on numerical linear and nonlinear programming methods that require substantial gradient information and usually seek to improve the solution in the neighborhood of a starting point. Quantum-behaved particle swarm optimization (QPSO) algorithm is a global convergence guaranteed algorithms, which outperforms original PSO in search ability but has fewer parameters to control. In this paper, we propose an improved quantum-behaved particle swarm optimization with new beta value according to fitness values of the particles. It is shown that the improved QPSO has faster local convergence speed, resulting in better balance between the global and local searching of the algorithm, and thus generating good performance. The proposed improved QPSO, called beta damping algorithm (MQPSO) approach for engineering optimization problems with both continuous and discrete designed variables, is tested on several benchmark functions and compared with QPSO. The experiment results show the superiority of MQPSO.

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