Improvement of Maneuverability Within a Multiagent Fuzzy Transportation System With the Use of Parallel Biobjective Real-Coded Genetic Algorithm

Over the past two decades, several simulation-based approaches have been developed to seek optimal solutions in complex multiagent systems (MASs). One example of these complex systems is the proposed multiagent fuzzy transportation system (FTS), in which agents, such as manned ground vehicles (MGVs) and unmanned ground vehicles (UGVs), use fuzzy logic for maneuvering while interacting with each other. The complexity of an FTS as an optimization problem is caused by two major factors, viz., inability to apply gradient-based optimization methods and uncertainty present in the FTS model. In these MASs, the objective functions are computed as an outcome of multiple agent-to-agent interactions. For the proposed FTS, two interconnected objectives are of prime importance while modeling the system: the number of potential traffic accidents, which should be minimized, and the output traffic that needs to be maximized. The resulting model is a simulation-based biobjective optimization problem. In an attempt to improve the maneuverability at various road network configurations (RNCs), in this work, a new parallel real-coded genetic algorithm based on fuzzy clustering (FCGA) is proposed that considers the specific requirements of the FTS model. Use of the FCGA aggregated with the FTS overcomes the difficulties associated with the optimization model and the limitations caused by simulation-based optimization (the large dimensionality of decision space, uncertainty present in the MAS environment, etc.). After showing superior performance in approximating the Pareto-optimal solutions for known test instances, the proposed FCGA is applied to the proposed multiagent FTS to approximate the optimal parameters. Because of the optimization of FTS parameters with the FCGA and the use of the proposed fuzzy clustering algorithm (FCA) for the traffic density estimate, the suggested approach substantially improves the maneuverability of UGVs and MGVs to change lanes while considering turning based on fuzzy rules and overtaking.