MOGA application feasibility research on ocean petroleum exploration platform seawater environment supervision

To supervise the seawater pollution situation around the ocean petroleum exploration platform is crucial at present to avoid the serious environment event and protect the ocean biological life. Robotic fishes are suitable tool to improve the supervising ability compared with the static sensor monitoring system. The obstacle to do so is how to control the robotic fishes group to work more efficiently. Robotic fishes group control is obvious a multi-objective problem, and multi-objective genetic algorithm (MOGA) is an effective method to deal with the multi-objective problem which can avoid the drawbacks of the traditional ones such as the weighted sum method. In this paper, based the ion-exchange polymer metal composite (IPMC) robotic fish dynamic model and MOGA, a new petroleum exploration platform supervising structure is set up and the control process is simulated to test the application feasibility of MOGA on it. The simulation result shows that it is valid in certain supervising task and provide the feasibility for further research.