Abstract: A new quantumbehaved particle swarm algorithm with selfadapting adjustment of inertia weight was presented to solve the problem that the linearly decreasing weight of the quantumbehaved 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 quantumbehaved particle swarm were tested with benchmark functions. The experiments show that the convergence speed of adaptive quantumbehaved particle swarm algorithm is significantly superior to quantumbehaved particle swarm algorithm.

Key words: quantumbehaved particle swarm, adaptability, inertia weight