A novel Physarum-Based ant colony system for solving the real-world traveling salesman problem

The solutions to Traveling Salesman Problem can be widely applied in many real-world problems. Ant colony optimization algorithms can provide an approximate solution to a Traveling Salesman Problem. However, most ant colony optimization algorithms suffer premature convergence and low convergence rate. With these observations in mind, a novel ant colony system is proposed, which employs the unique feature of critical tubes reserved in the Physaurm-inspired mathematical model. A series of experiments are conducted, which are consolidated by two realworld Traveling Salesman Problems. The experimental results show that the proposed new ant colony system outperforms classical ant colony system, genetic algorithm, and particle swarm optimization algorithm in efficiency and robustness.

Fabrication and visible-light photocatalytic behavior of perovskite praseodymium ferrite porous nanotubes

Perovskite praseodymium ferrite (PrFeO3) porous nanotubes are prepared by electrospinning of the precursor solution into nanofibers, subsequently by annealing the precursor fibers at a low temperature (e.g. 40 °C) and finally by calcination at a high temperature. The low temperature annealing treatment is found to play a key role in the formation of porous nanotube. The porous tubes show a perovskite-type PrFeO3 crystal characteristic with high optical absorption in the UV-visible region and an energy band gap of 1.97 eV. When compared with PrFeO3 porous nanofibers and PrFeO3 particles, the PrFeO3 porous nanotubes show better visible-light photo-catalytic ability to degrade Rhodamine B in aqueous phase because of the increased surface area and more active catalytic sites on the inner walls and outer surfaces.

A multi-objective ant colony optimization algorithm based on the physarum-inspired mathematical model

Multi-objective traveling salesman problem (MOTSP) is an important field in operations research, which has wide applications in the real world. Multi-objective ant colony optimization (MOACO) as one of the most effective algorithms has gained popularity for solving a MOTSP. However, there exists the problem of premature convergence in most of MOACO algorithms. With this observation in mind, an improved multiobjective network ant colony optimization, denoted as PMMONACO, is proposed, which employs the unique feature of critical tubes reserved in the network evolution process of the Physarum-inspired mathematical model (PMM). By considering both pheromones deposited by ants and flowing in the Physarum network, PM-MONACO uses an optimized pheromone matrix updating strategy. Experimental results in benchmark networks show that PM-MONACO can achieve a better compromise solution than the original MOACO algorithm for solving MOTSPs.