Chemical Safety Inspection Path Optimization Problems Using Improved Multi-Objective Discrete Growth Optimization Algorithm

Robot path planning plays a critical role in enhancing the efficiency and accuracy of inspections and ultimately contributes to the safety of chemical production. In particular, the performance of robot inspection is influenced by amount of gas leakage and path length. In this study, a chemical safety inspection path multi-objective optimization model is constructed that considers minimizing path length and maximizing gas leakage detection sensitivity. To address this model, an improved multi-objective discrete growth optimization algorithm is proposed, which adopts discrete operators to meet the solution requirements of the discrete model and an adaptive leader selection strategy to further improve the algorithm solution performance. Finally, numerical computations and simulation experiments are conducted to validate the feasibility of the proposed method for inspection path planning. The results demonstrate that the method outperforms multi-objective discrete particle swarm optimization, non-dominated sorting genetic algorithm-II, and multi-objective gray wolf optimization in terms of solution convergence and distribution uniformity. Moreover, it can provide better non-dominated solutions in simulation experiments and provide multiple references for practical applications.