Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Ant algorithms for discrete optimization
Artificial Life
The vehicle routing problem
The Ant System Applied to the Quadratic Assignment Problem
IEEE Transactions on Knowledge and Data Engineering
An Ant Colony Optimization Approach to the Probabilistic Traveling Salesman Problem
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
HAS-SOP: Hybrid Ant System for the Sequential Ordering Problem
HAS-SOP: Hybrid Ant System for the Sequential Ordering Problem
Ant Colony Optimization
Multi-Objective Genetic Algorithms for Vehicle Routing Problem with Time Windows
Applied Intelligence
Genetic algorithm with ant colony optimization (GA-ACO) for multiple sequence alignment
Applied Soft Computing
IEEE Computational Intelligence Magazine
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Data mining with an ant colony optimization algorithm
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
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
Ant colony optimization for routing and load-balancing: survey and new directions
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A new evolutionary algorithm using shadow price guided operators
Applied Soft Computing
Ant colony algorithm for traffic signal timing optimization
Advances in Engineering Software
High dynamic range optimal fuzzy color image enhancement using Artificial Ant Colony System
Applied Soft Computing
Computers and Operations Research
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This paper presents an improved ant colony optimization algorithm (IACO) for solving mobile agent routing problem. The ants cooperate using an indirect form of communication mediated by pheromone trails of scent and find the best solution to their tasks guided by both information (exploitation) which has been acquired and search (exploration) of the new route. Therefore the premature convergence probability of the system is lower. The IACO can solve successfully the mobile agent routing problem, and this method has some excellent properties of robustness, self-adaptation, parallelism, and positive feedback process owing to introducing the genetic operator into this algorithm and modifying the global updating rules. The experimental results have demonstrated that IACO has much higher convergence speed than that of genetic algorithm (GA), simulated annealing (SA), and basic ant colony algorithm, and can jump over the region of the local minimum, and escape from the trap of a local minimum successfully and achieve the best solutions. Therefore the quality of the solution is improved, and the whole system robustness is enhanced. The algorithm has been successfully integrated into our simulated humanoid robot system which won the fourth place of RoboCup2008 World Competition. The results of the proposed algorithm are found to be satisfactory.