A Hybrid Algorithm Based on Particle Swarm Optimization and Ant Colony Optimization Algorithm

Particle swarm optimization (PSO) and Ant Colony Optimization (ACO) are two important methods of stochastic global optimization. PSO has fast global search capability with fast initial speed. But when it is close to the optimal solution, its convergence speed is slow and easy to fall into the local optimal solution. ACO can converge to the optimal path through the accumulation and update of the information with the distributed parallel global search ability. But it has slow solving speed for the lack of initial pheromone at the beginning. In this paper, the hybrid algorithm is proposed in order to use the advantages of both of the two algorithm. PSO is first used to search the global solution. When it maybe fall in local one, ACO is used to complete the search for the optimal solution according to the specific conditions. The experimental results show that the hybrid algorithm has achieved the design target with fast and accurate search.

[1]  Martin W. P. Savelsbergh,et al.  Local search in routing problems with time windows , 1984 .

[2]  Mitsuo Gen,et al.  An effective genetic algorithm approach to the quadratic minimum spanning tree problem , 1998, Comput. Oper. Res..

[3]  Thomas Kiel Rasmussen,et al.  Hybrid Particle Swarm Optimiser with breeding and subpopulations , 2001 .

[4]  Arun Khosla,et al.  Particle swarm optimization for fuzzy models , 2007, GECCO '07.

[5]  Ying Qin,et al.  An ACO-Based User Community Preference Clustering System for Customized Content Service in Broadband New Media Platforms , 2008, 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.

[6]  Ronald L. Graham,et al.  On the History of the Minimum Spanning Tree Problem , 1985, Annals of the History of Computing.

[7]  Gang Wang,et al.  Instruction scheduling using MAX-MIN ant system optimization , 2005, ACM Great Lakes Symposium on VLSI.

[8]  Mohammad Majid al-Rifaie,et al.  An investigation into the merger of stochastic diffusion search and particle swarm optimisation , 2011, GECCO '11.

[9]  Hoang Thanh Lam,et al.  A heuristic particle swarm optimization , 2007, GECCO '07.

[10]  Stefano Cagnoni,et al.  Automatic Tuning of Standard PSO Versions , 2015, GECCO.

[11]  Schalk Kok,et al.  A strongly interacting dynamic particle swarm optimizational method , 2007, GECCO '07.

[12]  James Montgomery,et al.  A simple strategy to maintain diversity and reduce crowding in particle swarm optimization , 2011, Australasian Conference on Artificial Intelligence.

[13]  Jason H. Moore,et al.  Fast genome-wide epistasis analysis using ant colony optimization for multifactor dimensionality reduction analysis on graphics processing units , 2010, GECCO '10.

[14]  Man Leung Wong,et al.  Optimizing stacking ensemble by an ant colony optimization approach , 2011, GECCO '11.

[15]  J. Amudhavel,et al.  A hybrid ACO-PSO based clustering protocol in VANET , 2015, ICARCSET '15.

[16]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[17]  Camelia-Mihaela Pintea,et al.  Solving the linear ordering problem using ant models , 2009, GECCO '09.

[18]  Chun-Yin Wu,et al.  Topology optimization of structures using ant colony optimization , 2009, GEC '09.

[19]  Ashraf M. Abdelbar,et al.  Is there a computational advantage to representing evaporation rate in ant colony optimization as a gaussian random variable? , 2012, GECCO '12.

[20]  Vasantha Kalyani David,et al.  Swarm optimization and Flexible Neural Tree for microarray data classification , 2012, CCSEIT '12.

[21]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[22]  Ardeshir Bahreininejad,et al.  Optimization of laminate stacking sequence for minimizing weight and cost using elitist ant system optimization , 2013, Adv. Eng. Softw..