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Bio-inspired Computing and Smart Mobility

This PhD thesis presents a summary of the research work done with the aim of addressing and solving Smart Mobility problems in a smart city context. Several big cities are modeled to be optimized using new evolutionary techniques and the traffic simulator SUMO. Three new architectures, Red Swarm, Green Swarm and Yellow Swarm are proposed, analyzed and used to reduce travel times, greenhouse gas emissions, and fuel consumption of vehicles. A new method for calculating alternative routes for GPS navigators and the prediction of car park occupancy rates are also included in this PhD thesis. Moreover, a novel algorithm for generating realistic traffic flows is developed and tested in different scenarios: working
days, Saturdays, and Sundays. Finally, a new family of bio-inspired algorithms based on epigenesis was designed and tested on the Multidimensional Knapsack Problem and used in the Yellow Swarm architecture.

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Defence date: 01-Oct-2018

Evaluation: International, Cum Laude honors.

Keywords: bio-inspired algorithms; smart mobility; prediction; metaheuristics; artificial intelligence; evolutionary algorithms; Wi-Fi connections; travel times; greenhouse gas emissions; LED panels

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