A Quantum Glowworm Swarm Optimization Algorithm based on Chaotic Sequence

The standard Glowworm Swarm Optimization(GSO) has poor global search ability and easily trap into local optimum. In order to solve these problems, a Quantum Glowworm Swarm Optimization Algorithm based on Chaotic Sequence(QCSGSO) is proposed in this paper.Firstly, chaotic sequence is generated to initialize the population, which has higher probability to cover more local optimal areas, and provides a good condition for further optimization and tuning.Then, quantum behavior is applied to elite population, which makes individuals locate in any position of the solution space randomly with a certain probability, greatly enhances the algorithm’s capability of global searching and local optimum jumping. Finally, QCSGSO adopts single dimension loop swimming rather than the original fixed step movement mode, which not only improves the solution precision and convergence speed, but also solves GSO’s problem about too sensitive to the step-size, and enhances the robustness of the algorithm indirectly. The results of simulation experiments show that the proposed method is feasible and effective.