A Family of Ant Colony P Systems

Ant colony algorithm is a kind of bionic evolutionary algorithm, which is widely used in the field of optimization. Membrane computing is a new computing model, which has the characteristics of distributed, maximal parallelism and non-deterministic. Different with the most current researches that use ant colony algorithm as the sub-algorithm in the framework of the membrane algorithm, this paper considers the realizing ant colony algorithm completely by evolution rules, and we design new ant colony P system \(\varPi _{ACS}\), which includes the membrane structure and evolutionary rules. This paper not only provides a new way to realize the ant colony algorithm, but also lays a foundation for building a general framework for solving optimization problems in membrane computing.

[1]  Shankara Narayanan Krishna Universality results for P systems based on brane calculi operations , 2007, Theor. Comput. Sci..

[2]  Gexiang Zhang,et al.  A Modified Membrane-Inspired Algorithm Based on Particle Swarm Optimization for Mobile Robot Path Planning , 2015, Int. J. Comput. Commun. Control.

[3]  Grzegorz Rozenberg,et al.  Handbook of Natural Computing , 2011, Springer Berlin Heidelberg.

[4]  Marian Gheorghe,et al.  Cell communication in tissue P systems: universality results , 2005, Soft Comput..

[5]  M Dorigo,et al.  Ant colonies for the travelling salesman problem. , 1997, Bio Systems.

[6]  Qi Meng,et al.  A hybrid approach based on differential evolution and tissue membrane systems for solving constrained manufacturing parameter optimization problems , 2013, Appl. Soft Comput..

[7]  Shengli Xie,et al.  An ant colony optimization algorithm for image edge detection , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[8]  Alex Alves Freitas,et al.  Classification-Rule Discovery with an Ant Colony Algorithm , 2005, Encyclopedia of Information Science and Technology.

[9]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[10]  Gheorghe Paun,et al.  On the power of membrane division in P systems , 2004, Theor. Comput. Sci..

[11]  Marco Dorigo,et al.  Optimization, Learning and Natural Algorithms , 1992 .

[12]  Taishin Y. Nishida Membrane Algorithm with Brownian Subalgorithm and Genetic Subalgorithm , 2007, Int. J. Found. Comput. Sci..

[13]  Roxanne Evering,et al.  An ant colony algorithm for the multi-compartment vehicle routing problem , 2014, Appl. Soft Comput..

[14]  Ning Wang,et al.  Hybrid Optimization Method Based on Membrane Computing , 2011 .

[15]  Milan Tuba,et al.  Improved ACO Algorithm with Pheromone Correction Strategy for the Traveling Salesman Problem , 2013, Int. J. Comput. Commun. Control.

[16]  Guy Pujolle,et al.  VNE-AC: Virtual Network Embedding Algorithm Based on Ant Colony Metaheuristic , 2011, 2011 IEEE International Conference on Communications (ICC).

[17]  Juanjuan He,et al.  A hybrid membrane evolutionary algorithm for solving constrained optimization problems , 2014 .

[18]  Garima Singh,et al.  Hybridization of P Systems and Particle Swarm Optimization for Function Optimization , 2013, SocProS.

[19]  Jingan Yang,et al.  An improved ant colony optimization algorithm for solving a complex combinatorial optimization problem , 2010, Appl. Soft Comput..

[20]  Ping Guo,et al.  An ant system based on moderate search for TSP , 2012, Comput. Sci. Inf. Syst..

[21]  Xiyu Liu,et al.  P System Based Particle Swarm Optimization Algorithm , 2014 .

[22]  Hartmut Schmeck,et al.  Ant colony optimization for resource-constrained project scheduling , 2000, IEEE Trans. Evol. Comput..