Personalized tourist route planning (TRP) and navigation are online or real-time applications whose mathematical modeling leads to complex optimization problems. These problems are usually formulated with mathematical programming and can be described as NP hard problems. Moreover, the state-of-the-art (SOA) path search algorithms do not perform efficiently in solving multi-objective optimization (MO) problems making them inappropriate for real-time processing. To address the above limitations and the need for online processing, a swarm intelligence graph-based pathfinding algorithm (SIGPA) for MO route planning was developed. SIGPA generates a population whose individuals move in a greedy approach based on $A^{\ast }$ algorithm to search the solution space from different directions. It can be used to find an optimal path for every graph-based problem under various objectives. To test SIGPA, a generic MOTRP formulation is proposed. A generic TRP formulation remains a challenge since it has not been studied thoroughly in the literature. To this end, a novel mixed binary quadratic programming model is proposed for generating personalized TRP based on multi-objective criteria and user preferences, supporting, also, electric vehicles or sensitive social groups in outdoor cultural environments. The model targets to optimize the route under various factors that the user can choose, such as travelled distance, smoothness of route without multiple deviations, safety and cultural interest. The proposed model was compared to five SOA models for addressing TRP problems in 120 various scenarios solved with CPLEX solver and SIGPA. SIGPA was also tested in real scenarios with A* algorithm. The results proved the effectiveness of our model in terms of optimality but also the efficiency of SIGPA in terms of computing time. The convergence and the fitness landscape analysis showed that SIGPA achieved quality solutions with stable convergence.

个性化的旅游路线规划（TRP）和导航是在线或实时应用程序，其数学建模会导致复杂的优化问题。这些问题通常用数学编程来表述，可以描述为NP难题。此外，最新的（SOA）路径搜索算法无法有效解决多目标优化（MO）问题，从而使其不适用于实时处理。为了解决上述限制和在线处理的需求，开发了用于MO路线规划的基于群体智能图的寻路算法（SIGPA）。SIGPA产生了一个种群，其个体基于${\mathrm{一种}}^{\ast }$从不同方向搜索解空间的算法。它可用于为各种目标下的每个基于图的问题找到最佳路径。为了测试SIGPA，提出了一种通用的MOTRP公式。通用的TRP配方仍然是一个挑战，因为尚未在文献中对其进行深入研究。为此，提出了一种新颖的混合二进制二次规划模型，该模型用于基于多目标标准和用户偏好来生成个性化的TRP，还支持电动汽车或户外文化环境中的敏感社会群体。该模型的目标是在用户可以选择的各种因素下优化路线，例如行驶距离，路线平整度（无多重偏差），安全性和文化趣味性。将该提议的模型与五个SOA模型进行了比较，以解决用CPLEX求解器和SIGPA解决的120种各种情况下的TRP问题。SIGPA还使用A *算法在实际场景中进行了测试。结果证明了我们的模型在最优性方面的有效性，但在计算时间方面也证明了SIGPA的有效性。收敛性和适应度景观分析表明，SIGPA获得了具有稳定收敛性的优质解决方案。