Concerning the low accuracy and convergence of Glowworm Swarm Optimization (GSO) algorithm when resolving high-dimensional functions, an Improved GSO (IGSO) algorithm with mutation operator and foraging behavior was proposed. Using mutation operator to guide the evolution of glow worms which cannot find their peers in the visible range, the proposed algorithm could enhance the utilization of outliers and improve the overall efficiency. The operator with foraging behavior substantially increased the accuracy and convergence speed by searching accurately in the global optimal field captured by the algorithm. In the meantime, the operator could effectively avoid local optimum and enlarge the global search range of the algorithm in the late stage. The experimental results indicate that IGSO has better ability of global optimization and higher success ratio than GSO according to the tests of eight Benchmarks.