具有混合群智能行为的萤火虫群优化算法研究

萤火虫群优化算法是一种新型的群智能优化算法,基本的萤火虫群优化算法存在收敛精度低等问题。为了提高算法的性能,借鉴蜂群和鸟群的群体智能行为,改进萤火虫群优化算法的移动策略。运用均匀设计调整改进算法的参数取值。若干经典测试问题的实验仿真结果表明,引入混合智能行为大幅提升了算法的优化性能。

Research on Glowworm Swarm Optimization with Hybrid Swarm Intelligence Behavior

Glowworm swarm optimization(GSO ) algorithm is the one of the newest nature inspired heuristics for optimization problems. In order to enhance accuracy and convergence rate of the GSO, two behaviors which are inspired by artificial bee colony algorithm(AI3C) and particle swarm optimization(PSO )of the movement phase of GSO were proposed. The effects of the parameters about the improvement algorithms were discussed by uniform design experiment. A number of experiments were carried out on a set of well-known benchmark global optimization problems. Numerical re- sups reveal that the proposed algorithms can find better solutions compared with classical GSO and other heuristic algorithms and are powerful search algorithms for various global optimization problems.