A novel two-stage hybrid swarm intelligence optimization algorithm and application

This paper presents a novel two-stage hybrid swarm intelligence optimization algorithm called GA–PSO–ACO algorithm that combines the evolution ideas of the genetic algorithms, particle swarm optimization and ant colony optimization based on the compensation for solving the traveling salesman problem...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Vol. 16; no. 10; pp. 1707 - 1722
Main Authors: Deng, Wu, Chen, Rong, He, Bing, Liu, Yaqing, Yin, Lifeng, Guo, Jinghuan
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer-Verlag 01.10.2012
Springer Nature B.V
Subjects:
Ant colony optimization
Artificial Intelligence
Cities
Computational Intelligence
Control
Engineering
Genetic algorithms
Mathematical Logic and Foundations
Mechatronics
Neural networks
Optimization algorithms
Optimization techniques
Original Paper
Particle swarm optimization
Positive feedback
Robotics
Swarm intelligence
Tabu search
Traveling salesman problem
Ant colony optimization
Traveling salesman problem
Two-stage hybrid algorithm
Swarm intelligence
Particle swarm optimization
Genetic algorithms
ISSN:1432-7643, 1433-7479
Online Access:Get full text
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Summary:This paper presents a novel two-stage hybrid swarm intelligence optimization algorithm called GA–PSO–ACO algorithm that combines the evolution ideas of the genetic algorithms, particle swarm optimization and ant colony optimization based on the compensation for solving the traveling salesman problem. In the proposed hybrid algorithm, the whole process is divided into two stages. In the first stage, we make use of the randomicity, rapidity and wholeness of the genetic algorithms and particle swarm optimization to obtain a series of sub-optimal solutions (rough searching) to adjust the initial allocation of pheromone in the ACO. In the second stage, we make use of these advantages of the parallel, positive feedback and high accuracy of solution to implement solving of whole problem (detailed searching). To verify the effectiveness and efficiency of the proposed hybrid algorithm, various scale benchmark problems from TSPLIB are tested to demonstrate the potential of the proposed two-stage hybrid swarm intelligence optimization algorithm. The simulation examples demonstrate that the GA–PSO–ACO algorithm can greatly improve the computing efficiency for solving the TSP and outperforms the Tabu Search, genetic algorithms, particle swarm optimization, ant colony optimization, PS–ACO and other methods in solution quality. And the experimental results demonstrate that convergence is faster and better when the scale of TSP increases.
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-012-0855-z