Multi-Objective Optimization For Employee Carpooling Using The IMGAMO Algorithm And Baldwin Effect

The primary objective of the carpooling task, which involves the shared use of vehicles for transportation, is to determine the optimal configuration of passengers and drivers to maximize the number of transported people Various optimization criteria have been defined for this problem, including minimizing the travel distance or time, maximizing the number of passengers, reducing CO2 emissions and carbon footprint, enhancing user comfort and safety, and many others. This study presents a modified version of the carpooling problem, focusing on ridesharing among employees of a specific company for commuting to its headquarters. In order to solve this problem, the IMGAMO algorithm combined with local search (Baldwin effect) has been proposed. The algorithm’s effectiveness has been validated by comparing it with traditional evolutionary multi-objective optimization algorithms under different problem configurations, including variations in the number of drivers and passengers and the number of objective functions.