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A Membrane-Inspired Algorithm with Exchange-Tree Mechanism for Traffic Network Transportation Optimization Problem
Yingying Duan, Kang Zhou, Gexiang Zhang, Prithwineel Paul, Haina Rong and Juanjuan He

Traffic network transportation optimization problem (TNTOP) has important applications in logistics distribution fields. In various disciplines, methods about the solutions-termed TNTOP can have shown promising performance from different types of detection, at different conditions. Due to the limitations of the calculation speed of traditonal algorithms, it is rare that a simple unmodified method provides complete techniques of tackling large-scale TNTOP. We use the term P systems to solve the above limitations. Specifically, it is a tissue-like P system with four cells based on particle swarm algorithm, referred to as MPSO. In this system, the modified prim algorithm and the position-updated mechanism are adopted to generate and update all particle individuals, velocity-updated mechanism and an exchange-tree strategy are adopted to balance exploration and exploitation processes. Besides, some special strategies are also added to this systems. Numerous experiments are presented to verify the performance of the MPSO. The results show that it can generate the individuals of higher quality in shorter computation time when comparing to other benchmark algorithms. These empirical results validate the effectiveness and competitiveness of our proposed algorithm in solving TNTOP in terms of both quality and speed.

Keywords: Traffic network transportation optimization problem, tissue-like P system, particle swarm algorithm, exchange-tree operator.

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