| Summary: | This paper proposes a multi-objective approach for the minimum constraint removal (MCR) A problem. First, a multi-objective model for MCR path planning is constructed. This model takes into account factors such as the minimum constraint set, the route length, and the cost. A multi-objective particle swarm optimization (MOPSO) algorithm is then designed based on the fitness function of the multi-objective MCR problem, and an iteration formula based on the personal best (pbest) and global best (gbest) of the algorithm is constructed to update the particle velocity and position. Finally, compared with ant colony optimization (ACO) A and the crow search algorithm (CSA) A, the experimental results show that the MOPSO-based path planning algorithm can find a shorter path that traverses fewer obstacle areas and can thus perform MCR path planning more effectively.
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