Abstract: 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.

Key words: Glowworm swarm optimization, Artificial bee colony algorithm, Particle swarm optimization, Global optimization