Applying evolutionary programming to selected traveling salesman problems
Cybernetics and Systems
The ant colony optimization meta-heuristic
New ideas in optimization
A Graph-based Ant system and its convergence
Future Generation Computer Systems
Future Generation Computer Systems
A Study of Some Properties of Ant-Q
PPSN IV Proceedings of the 4th 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
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
ACO-based Projection Pursuit clustering algorithm
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 1
Hyper-heuristics with low level parameter adaptation
Evolutionary Computation
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In order to solve the premature convergence problem of the basic Ant Colony Optimization algorithm, a promising modification based on the information entropy is proposed. The main idea is to evaluate stability of the current space of represented solutions using information entropy, which is then applied to turning of the algorithm's parameters. The path selection and evolutional strategy are controlled by the information entropy self-adaptively. Simulation study and performance comparison with other Ant Colony Optimization algorithms and other meta-heuristics on Traveling Salesman Problem show that the improved algorithm, with high efficiency and robustness, appears self -adaptive and can converge at the global optimum with a high probability. The work proposes a more general approach to evolutionary-adaptive algorithms related to the population's entropy and has significance in theory and practice for solving the combinatorial optimization problems.