An explanation of ordinal optimization: soft computing for hard problems
Information Sciences: an International Journal
A Graph-based Ant system and its convergence
Future Generation Computer Systems
Future Generation Computer Systems
Exchange strategies for multiple Ant Colony System
Information Sciences: an International Journal
First steps to the runtime complexity analysis of ant colony optimization
Computers and Operations Research
Solution bias in ant colony optimisation: Lessons for selecting pheromone models
Computers and Operations Research
Two-Stage Ant Colony Optimization for Solving the Traveling Salesman Problem
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
Population declining ant colony optimization algorithm and its applications
Expert Systems with Applications: An International Journal
Runtime analysis of an ant colony optimization algorithm for TSP instances
IEEE Transactions on Evolutionary Computation
Expert Systems with Applications: An International Journal
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
A short convergence proof for a class of ant colony optimizationalgorithms
IEEE Transactions on Evolutionary Computation
Search bias in ant colony optimization: on the role of competition-balanced systems
IEEE Transactions on Evolutionary Computation
On the Invariance of Ant Colony Optimization
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The hyper-cube framework for ant colony optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multi-satellite control resource scheduling based on ant colony optimization
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Ant colony optimization (ACO) is a metaheuristic approach for combinatorial optimization problems. With the introduction of hypercube framework, invariance property of ACO algorithms draws more attention. In this paper, we propose a novel two-stage updating pheromone for invariant ant colony optimization (TSIACO) algorithm. Compared with standard ACO algorithms, TSIACO algorithm uses solution order other than solution itself as independent variable for quality function. In addition, the pheromone trail is updated with two stages: in one stage, the first r iterative optimal solutions are employed to enhance search capability, and in another stage, only optimal solution is used to accelerate the speed of convergence. And besides, the pheromone value is limited to an interval. We prove that TSIACO not only has the property of linear transformational invariance but also has translational invariance. We also prove that the pheromone trail can limit to the interval (0,1]. Computational results on the traveling salesman problem show the effectiveness of TSIACO algorithm.