QuantumBehaved Particle Swarm Algorithm with Selfadapting Adjustment of Inertia Weight
HUANG Ze-Xia-1, 2 , YU You-Hong-3, HUANG De-Cai-1
(1College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023,China; 2Department of Information and Electronic, Shaoxing University YuanPei College, Shaoxing Zhejiang 312000,China; 3College of Science, Zhejiang University of Technology, Hangzhou 310023,China)
A new quantumbehaved particle swarm algorithm with selfadapting adjustment of inertia weight was presented to solve the problem that the linearly decreasing weight of the quantumbehaved particle swarm algorithm cannot adapt to the complex and nonlinear optimization process. The evolution speed factor and aggregation degree factor of the swarm are introduced in this new algorithm and the weight is formulated as a function of these two factors according to their impact on the search performance of the swarm. In each iteration process, the weight is changed dynamically based on the current evolution speed factor and aggregation degree factor, which provides the algorithm with effective dynamic adaptability. The algorithms of quantumbehaved particle swarm were tested with benchmark functions. The experiments show that the convergence speed of adaptive quantumbehaved particle swarm algorithm is significantly superior to quantumbehaved particle swarm algorithm.