A new meta-heuristic ebb-tide-fish-inspired algorithm for traffic navigation

More and more bio-inspired or meta-heuristic algorithms have been proposed to tackle the tough optimization problems. They all aim for tolerable velocity of convergence, a better precision, robustness, and performance. In this paper, we proposed a new algorithm, ebb tide fish algorithm (ETFA), which mainly focus on using simple but useful update scheme to evolve different solutions to achieve the global optima in the related tough optimization problem rather than PSO-like velocity parameter to achieve diversity at the expenses of slow convergence rate. The proposed ETFA achieves intensification and diversification in a new way. First, a flag is used to demonstrate the search status of each particle candidate. Second, the single search mode and population search mode tackle the intensification and diversification for tough optimization problem respectively. We also compare the proposed algorithm with other existing algorithms, including bat algorithm, cat swarm optimization, harmony search algorithm and particle swarm optimization. Simulation results demonstrate that the proposed ebb tide fish algorithm not only obtains a better precision but also gets a better convergence rate. Finally, the proposed algorithm is used in the application of vehicle route optimization in Intelligent Transportation Systems (ITS). Experiment results show that the proposed scheme also can be well performed for vehicle navigation with a better performance of the reduction of gasoline consumption than the shortest path algorithm (Dijkstra Algorithm) and A* algorithm.