Multi-objective Gannet Optimization Algorithm for Dynamic Passenger Flow Allocation in Train Operation Plan Optimization

This paper proposes a multi-objective Gannet Optimization Algorithm (MOGOA) to address the issue of unbalanced train occupancy rates in railway operation planning. MOGOA employs an adaptive multi-population co-evolutionary strategy balance exploration and exploitation, utilizing non-dominated sorting algorithm based on crowding distance select parent child samples. These samples serve as initial solutions for subsequent iterations. A novel maximin fitness function guides iterative update global optimal position. is applied planning problem with dynamic passenger flow allocation feedback. It collaboratively optimizes number operations, sections, stops reduce costs, rates, minimize travel time, enhance satisfaction. The practical applicability optimizing plans significant.