Solving Uncertain Multimodal Transport Routing Optimization Problems Based on AFO, PSO and GA Algorithms
In the field of logistics, route planning is a very important problem. Due to the uncertainty of factors such as customer demand and road conditions, the route planning problem becomes more complicated. To solve this problem, Emperor Penguin AFO, Particle Swarm PSO and Genetic GA algorithms can be used.
This article will introduce how to use these algorithms to solve the problem of uncertain multimodal transport route optimization, and provide the corresponding Matlab code.
- Problem Description
We assume that there are N cities that need to be connected, and each city needs to specify the departure time, arrival time and means of transportation. At the same time, we also need to consider issues such as the length of the journey and the cost of the goods. Therefore, we need to optimize a route that minimizes the total route cost and satisfies various constraints (such as the number of vehicles, time window, etc.).
- AFO algorithm
The AFO algorithm is an optimization algorithm inspired by the foraging behavior of giant pandas. The algorithm is able to optimize the objective function and perform local and global searches during the search. In our case, we will use the AFO algorithm to optimize the entire path.
Here is the Matlab code using the AFO algorithm:
function [best_path, min_cost] = AFO_algorithm(...