Research Article
Machine Learning-Based Parameter Tuned Genetic Algorithm for Energy Minimizing Vehicle Routing Problem
Algorithm 1
Overview of basic genetic algorithm developed for the EMVRP.
| Input | | Read list of cities to be served with demands from CVRP Lib file | | Read vehicle capacity from text file | | Population size () | | Number of generations () | | //Initialization | | Create a tour with a random order of cities | | Do this for times to create a population | | Get the best tour from the population | | Save as the elite | | initial energy = energy consumption of the fittest tour in first population | | //Genetic algorithm | | //Run for times | | Loop1 | | //Run for times | | Loop2 | | //Tournament selection | | Select a random set of tours from the population | | Get the fittest and return | | //Crossover | | Parent1 = tournament selection () | | Parent2 = tournament selection () | | child = Crossover (Parent1, Parent2) | | //Mutation | | Swap random two cities in the child | | endLoop2 | | //New population is created | | Get the fittest | | Replace previous elite if fittest is better than elite | | endLoop1 | | Get the elite | | Final energy = energy consumption of elite | | Reduction of energy = (initial energy − final energy)/initial energy 100% | | Print “Reduction of energy” |
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