Abstract: A novel method for global optimization, multi-sense swarm intelligence algorithm (MSA), was presented to solve continuous function optimization problems. Inspired by the artificial fish-swarm algorithm (AFA) and the FS algorithm (free search algorithm, FSA), the search mechanism of MSA combined large scale exploration and local precise search; even more, in this algorithm, the unit employed both visual information for quick approaching to local optimization solution and pheromone information to avoid overcrowding and to guide itself to global solution. Simulation shows that MSA has strong robustness, good global convergence, quick convergence speed and high convergence accuracy. At last, MSA was applied to feed-forward neural network training. The result shows that this algorithm is fit for the application.