Abstract: The application of swarm robots in multi-objective search is an unexplored dynamic environment with several issues, such as collision prediction and low search efficiency. We proposes a multi-objective search strategy by combining the collision geometric cone and particle swarm optimization algorithm with improved inertia weight. First, the collision cone (CC) and the velocity obstacle method (VO) are introduced using the difference between static and dynamic threats. Furthermore, the swelling geometry method is proposed to simplify complex obstacles, and a collision geometric cone model (CGC) is applied to improve CC and VO. This model can effectively resolve the issue of predicting collision avoidance of complex irregular threats and can determine the optimal obstacle avoidance direction from the threat assessment report based on the CGC model. Second, an improved inertia weight particle swarm optimization algorithm (IWPSO) is proposed, which improves the search efficiency and effectively solves the issue that the particle swarm optimization algorithm is easy to fall into local optimum. Finally, two improved methods, CGC and IWPSO, are combined to conduct a multi-objective search of swarm robots. Compared with the simplified virtual force, adaptive robotic bat algorithm, and particle swarm optimization algorithm with kinematic constraints, the proposed method reduces the search time consumption, energy consumption, and number of obstacle avoidance times by at least 15. 59%, 10. 14%, and 14. 12%, respectively.