Study on hybrid PS-ACO algorithm

Authors:
Bing Shuang;Jiapin Chen;Zhenbo Li
Affiliations:
National Key Laboratory of Nano/Micro Fabrication Technology, Key laboratory for Thin Film and Microfabrication of Ministry of Education, Research Institute of Micro/Nano Science and Technology, S ...;National Key Laboratory of Nano/Micro Fabrication Technology, Key laboratory for Thin Film and Microfabrication of Ministry of Education, Research Institute of Micro/Nano Science and Technology, S ...;National Key Laboratory of Nano/Micro Fabrication Technology, Key laboratory for Thin Film and Microfabrication of Ministry of Education, Research Institute of Micro/Nano Science and Technology, S ...
Venue:
Applied Intelligence
Year:
2011

Citing 9
Cited 4

Ant algorithms for discrete optimization

Artificial Life
MAX-MIN Ant system

Future Generation Computer Systems
Recent approaches to global optimization problems through Particle Swarm Optimization

Natural Computing: an international journal
Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators

Artificial Intelligence Review
The Ant System Applied to the Quadratic Assignment Problem

IEEE Transactions on Knowledge and Data Engineering
Comparison between Genetic Algorithms and Particle Swarm Optimization

EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
An Ant Colony System Hybridized with a New Local Search for the Sequential Ordering Problem

INFORMS Journal on Computing
Ant colony system: a cooperative learning approach to the traveling salesman problem

IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Handwritten characters recognition based on nature-inspired computing and neuro-evolution

Applied Intelligence
Control and synchronization of chaotic systems by an improved biogeography-based optimization algorithm

Applied Intelligence
Dynamic group-based differential evolution using a self-adaptive strategy for global optimization problems

Applied Intelligence
Cooperative Velocity Updating model based Particle Swarm Optimization

Applied Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Ant colony optimization (ACO) algorithm is a recent meta-heuristic method inspired by the behavior of real ant colonies. The algorithm uses parallel computation mechanism and performs strong robustness, but it faces the limitations of stagnation and premature convergence. In this paper, a hybrid PS-ACO algorithm, ACO algorithm modified by particle swarm optimization (PSO) algorithm, is presented. The pheromone updating rules of ACO are combined with the local and global search mechanisms of PSO. On one hand, the search space is expanded by the local exploration; on the other hand, the search process is directed by the global experience. The local and global search mechanisms are combined stochastically to balance the exploration and the exploitation, so that the search efficiency can be improved. The convergence analysis and parameters selection are given through simulations on traveling salesman problems (TSP). The results show that the hybrid PS-ACO algorithm has better convergence performance than genetic algorithm (GA), ACO and MMAS under the condition of limited evolution iterations.