A Hybrid Approach for Simultaneous Effective Automobile Navigation with DE and PSO

Automobile Navigation is fundamentally an optimization challenge focused on transportation logistics between a depot and various clients. In this paper, we specifically address the complex variant of Automobile Navigation that involves simultaneous pickup and delivery tasks, which must be executed concurrently at clients' locations. This dual requirement introduces significant complexity, as traditional exact approaches struggle to rapidly identify near-optimal solutions due to the problem's NP-hardness. Therefore, the objective of this research is to develop a novel hybrid algorithm that integrates Differential Evolution (DE) and Particle Swarm Optimization (PSO) to effectively solve the Automobile Navigation problem with simultaneous pickup and delivery. The proposed method uses the nearest neighbor heuristic to initially produce results. It is based on the iterated local search paradigm. Variable neighborhood descent is used to improve the search process by adding random sequences to the neighborhood structures for improved search space intensification. Furthermore, exploration across various sections of the search space is made possible by the perturbation process. This method solves the problem of different truck loads on every client visit because it does the pickup and delivery at the same time, which makes the Navigation strategy more effective.