Application of Nature-Inspired Optimization Techniques in Vessel Traffic Control

This chapter aims to present the analysis and comparison of some well-known nature-inspired global optimization techniques applied to an expert system controlling a ship locking process. A ship lock zone represents a specific area on waterway, and control of the ship lockage process requires a comprehensive approach. The initially proposed Fuzzy Expert System (FES) was developed using suggestions obtained from lockmasters (ship lock operators) with extensive experience. Further optimization of the membership function parameters of the input variables was performed to achieve better results in the local distribution of vessel arrivals. The purpose of the analysis and comparison is to find the best algorithm for optimization of membership functions parameters of FES for the ship lock control. The initially proposed FES is optimized (fine-tuned) with three global optimization algorithms from the group of evolutionary and swarm intelligence algorithms, in order to achieve the best value of the economic criterion defined as a linear combination of two opposed criteria: minimal average waiting time per vessel and minimal number of empty lockages (lockages without a vessel in a chamber). Besides the well known and widely applied Genetic Algorithm (GA), two relatively new but very promising global optimization techniques were used: Particle Swarm Optimization (PSO), the technique based on behavior of animals living in swarms and Artificial Bee Colony (ABC) algorithm, inspired by social organization of honey bees. Although all these algorithms have been widely applied and showed a great potential in engineering applications in general, their application in ship lock control and similar transportation problems is not so common. However, this chapter will present that all three algorithms may obtain the significant improvement of the adopted economic criterion value and succeed to find its (possibly global) optimum. Furthermore, the performances of these algorithms in FES parameters optimization are compared and some conclusions are adopted on their applicability, efficiency, and effectiveness in similar systems. The developed fuzzy algorithm is a rare application of artificial intelligence in navigable canals and significantly improves the performance of the ship lockage process. This adaptable FES is designed to be used as a support in decision-making processes or for the direct control of ship lock operations.