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Services-Oriented Computing Using the Compact Genetic Algorithm for Solving the Carpool Services Problem
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DOI：10.1109/TITS.2015.2421557.png)
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Carpooling is an effective solution to traffic congestion. It increases the usage rate of vehicles by employing empty seats as a transportation resource. In order to provide carpooling services to users, we developed an intelligent carpool system called BlueNet-Ride. After prospective carpoolers submit their requests through their smart handheld devices, this system provides appropriate matches by using the proposed Low-Complexity and Low-Memory Carpool Matching method. The compact genetic algorithm is applied to our Low-Complexity and Low-Memory Carpool Matching method, which involves three proposed modules: an Evolutionary Model Initialization module, an Evolutionary Process Operation module, and an Evolutionary Model Modification module. The Evolutionary Model Initialization module takes advantage of the manipulation of the evolving population on a probability distribution to achieve low-memory requirements during the evolution process of the carpool match solution. The Evolutionary Process Operation and Evolutionary Model Modification modules simulate genetic operations to accomplish superior matching within a short amount of time. The experimental results demonstrate that our Low-Complexity and Low-Memory Carpool Matching method achieves the highest degree of performance with regard to solution quality, processing time, and memory requirements of all evaluated methods.
Keyword:
Carpool services optimization
compact genetic algorithm (cGA)
high-load architecture
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